system

Abstract

We provide the system. [Solution] A means of tracking the user's gaze, A means for acquiring video footage of an object that the user is focusing on, A lip motion analysis means analyzes the movement of the target's lips from video acquired by a video acquisition means and generates text data, A speech synthesis means that converts generated text data into speech, A voice output means that provides the user with voice generated by a voice synthesis means, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In communication in a noisy environment, there is a problem that it is difficult for a user to accurately hear the target voice. Conventional earplugs and earmuffs block noise but also block the necessary voices, and directional microphones and transceivers are not suitable for conversations with multiple speakers or an unspecified large number of people. In such a situation, due to a decrease in work efficiency, a decrease in customer satisfaction, and safety problems, appropriate solutions are needed.

Means for Solving the Problems

[0005] This invention provides a system that enables efficient and natural conversation even in noisy environments by comprising: eye-tracking means for tracking the user's gaze; video acquisition means for acquiring video of an object the user is fixated on; lip movement analysis means for analyzing the movement of the object's lips from the acquired video and generating text data; speech synthesis means for converting the generated text data into speech; and speech output means for providing speech to the user. As a result, smooth communication can be achieved even in noisy environments by reading the lip movements of the person the user is fixated on and playing back the content as speech.

[0006] "Eye-tracking means" are methods for detecting the direction of a user's gaze and identifying the object the user is focusing on.

[0007] "Image acquisition means" refers to a device or method for acquiring images of a specific object.

[0008] A "lip movement analysis means" is a means for analyzing the movement of a subject's lips from acquired video footage and converting what they are saying into text data.

[0009] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to analyze spoken language from lip movements and convert it into text data.

[0010] A "speech synthesis means" is a means for converting generated text data into speech data.

[0011] "Audio output means" refers to a device for providing generated audio data to the user, and includes headsets and speakers. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the 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.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0026] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] The present invention is a system for facilitating communication in high-noise environments, and is composed of a combination of eye-tracking means, video acquisition means, lip-movement analysis means, generation AI model, speech synthesis means, and speech output means. The embodiments for carrying out the invention will be described in detail below.

[0034] System Configuration

[0035] Eye-tracking methods

[0036] The device (smart glasses) is equipped with a built-in camera to capture the user's eye movements. This camera can track the user's gaze and execute an algorithm to calculate its direction.

[0037] Video acquisition method

[0038] The device identifies the object the user is looking at through eye-tracking technology and acquires video footage of that object using a high-resolution camera. This video data is transmitted to the server in real time.

[0039] Lip motion analysis means

[0040] The server receives video data transmitted from the terminal and inputs it into a generative AI model used for lip movement analysis. This generative AI model analyzes the movement of the other person's lips from the acquired video and generates text data of what is being said.

[0041] Speech synthesis means

[0042] The generated text data is input into a speech synthesis system on the server and converted into natural, fluent speech data. This speech data is then adjusted to a quality that users can listen to without any discomfort.

[0043] Audio output means

[0044] Finally, the generated audio data is sent to a device (smart glasses) and then provided to the user through a headset or speaker. This allows the user to accurately hear what the other person is saying, even in noisy environments.

[0045] Program processing

[0046] The following describes, in natural language, how the system of the present invention works.

[0047] Eye Tracking

[0048] The device continuously captures the user's gaze using its built-in camera and calculates the direction of their gaze. The calculated gaze data is sent to the server in real time.

[0049] Video acquisition

[0050] The server receives gaze data and uses it to identify the video object the user is fixated on. The identified video is then acquired for further detailed analysis.

[0051] Lip movement analysis

[0052] The acquired video is sent to a server, where a generative AI model analyzes the lip movements to determine what is being said and generates it as text data. This text data is temporarily stored in a buffer.

[0053] Speech synthesis

[0054] The text data stored in the buffer is input to the speech synthesis engine and converted into natural-sounding speech data. This speech data is then stored back into the buffer.

[0055] Audio output

[0056] Finally, the generated audio data is sent to the terminal and provided to the user through a headset or speaker, allowing the user to accurately hear what the other person is saying even in noisy environments.

[0057] Specific example

[0058] For example, suppose User A is working in a noisy factory while wearing smart glasses. In this scenario, User B is giving instructions to User A, but User A cannot hear them due to the surrounding noise.

[0059] 1. User A turns their gaze towards User B.

[0060] 2. The device's built-in camera captures user A's gaze, and the gaze data is sent to the server.

[0061] 3. The server receives the gaze data, identifies user B's video, and retrieves the video data.

[0062] 4. The server uses a generative AI model to convert what user B is saying from the movement of their lips into text data.

[0063] 5. The server inputs the text data into the speech synthesis engine and generates the speech data.

[0064] 6. The generated audio data is sent to the terminal and played back through User A's headset.

[0065] This allows User A to accurately hear User B's instructions even in a noisy environment, enabling them to proceed with the work smoothly.

[0066] Thus, the system of the present invention enables efficient communication in a variety of high-noise environments.

[0067] The following describes the processing flow.

[0068] Step 1:

[0069] The device captures the user's gaze.

[0070] The camera built into the device continuously captures the user's eye movements.

[0071] The system processes video data from the camera in real time and executes an eye-tracking algorithm to calculate the user's gaze direction.

[0072] Step 2:

[0073] The device sends eye-tracking data to the server.

[0074] The calculated line-of-sight direction data is sent to the server at regular time intervals.

[0075] Eye-tracking data includes information such as the coordinates of the user's gaze point.

[0076] Step 3:

[0077] The server receives gaze data and acquires video footage of the object the user is looking at.

[0078] The server receives the gaze data transmitted from the terminal.

[0079] Based on the received gaze data, the system identifies the video area of ​​the object the user is fixated on.

[0080] Detailed video data of the identified video area is obtained from the device.

[0081] Step 4:

[0082] The server analyzes lip movements and generates text data.

[0083] The acquired video data is input into the AI ​​model on the server.

[0084] The generative AI model analyzes lip movements and converts what is being said into text data.

[0085] The generated text data is temporarily stored in a buffer.

[0086] Step 5:

[0087] The server inputs text data into the speech synthesis engine.

[0088] The text data stored in the buffer is input to the speech synthesis engine.

[0089] A speech synthesis engine converts input text data into natural-sounding speech data.

[0090] The generated audio data is stored back into the buffer.

[0091] Step 6:

[0092] The server sends the audio data to the terminal.

[0093] The audio data stored in the buffer is sent to the terminal as soon as it is ready for playback.

[0094] Step 7:

[0095] The device plays the audio data.

[0096] The device plays the received audio data through a headset or speaker.

[0097] The user listens to the generated audio through a headset or similar device.

[0098] Specific example

[0099] For example, the following shows the processing flow when User B gives visual instructions to User A while User A is working in a noisy factory.

[0100] Step 1:

[0101] User A wears smart glasses and directs their gaze towards User B. The camera in the device captures User A's eye movements and calculates the direction of their gaze.

[0102] Step 2:

[0103] The terminal sends calculated gaze data to the server. This includes coordinate information for the direction in which user A is looking.

[0104] Step 3:

[0105] The server receives gaze data and identifies user B's video area based on it. The identified video area is then retrieved from the terminal.

[0106] Step 4:

[0107] The server inputs the acquired video data into a generating AI model, which analyzes lip movements and converts them into text data. The generated text data is then temporarily stored in a buffer.

[0108] Step 5:

[0109] The server inputs the text data stored in the buffer into the speech synthesis engine and converts it into natural-sounding speech data. The generated speech data is then stored back into the buffer.

[0110] Step 6:

[0111] The server sends the generated audio data to the terminal.

[0112] Step 7:

[0113] The terminal plays the received audio data through the headset, and user A listens to the audio.

[0114] This allows user A to accurately receive instructions from user B even in noisy environments, enabling them to carry out their work efficiently.

[0115] (Example 1)

[0116] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0117] Communication in high-noise environments presents a significant challenge due to the difficulty in hearing voices. This is particularly problematic in places requiring concentration, such as factories and construction sites, where important instructions may be difficult to convey, potentially negatively impacting work efficiency and safety. The present invention aims to provide a means for effective communication even in such high-noise environments.

[0118] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0119] In this invention, the server includes eye-tracking means for tracking the user's gaze, image acquisition means for acquiring images of an object the user is fixated on, lip-movement analysis means for analyzing the movement of the object's lips from the images acquired by the image acquisition means and generating text data, speech synthesis means for converting the generated text data into speech, and speech output means for providing the user with the speech generated by the speech synthesis means. This makes it possible for the user to accurately hear what the other person is saying, even in a high-noise environment.

[0120] "Eye-tracking means" refers to devices or algorithms that capture the movement of a user's eyes and calculate the direction of their gaze.

[0121] "Image acquisition means" refers to devices or technologies for acquiring images of objects that a user is focusing on.

[0122] A "lip movement analysis means" refers to a device or generation AI model that analyzes the movement of a subject's lips from acquired video footage and converts the spoken content into text data.

[0123] "Speech synthesis means" refers to a device or engine that converts generated text data into natural-sounding speech data.

[0124] "Audio output means" refers to a device for providing the user with audio generated by a speech synthesis means, and includes headsets and speakers.

[0125] A "wearable device" is an electronic device that a user can wear and use.

[0126] A "machine learning model" is an algorithm that learns patterns from data and uses them to make predictions and classifications.

[0127] The present invention is a system for facilitating communication in high-noise environments, and is comprised of a combination of eye-tracking means, video acquisition means, lip-movement analysis means, a generation AI model, speech synthesis means, and speech output means. The following describes in detail the embodiments for implementing this system.

[0128] System Configuration

[0129] Eye-tracking methods

[0130] The device (wearable device) is equipped with a built-in camera to capture the user's eye movements. This camera can track the user's gaze and execute an algorithm to calculate its direction. Specifically, a face detection algorithm using Haar-like features is applied.

[0131] Video acquisition method

[0132] The device identifies the object the user is looking at through eye-tracking technology and captures video of that object using a high-resolution camera. This camera has a 1080p resolution and captures video in real time. The acquired video data is compressed and transmitted to a server via Wi-Fi.

[0133] Lip motion analysis means

[0134] The server receives video data transmitted from the terminal and inputs it into a generative AI model. This generative AI model is a TENSORFLOW®-based RNN model that analyzes the lip movements of the other party from the acquired video and generates text data of what is being said. This text data is temporarily stored in a Redis buffer.

[0135] Speech synthesis means

[0136] The text data stored in the buffer is input to a speech synthesis engine on the server (e.g., Google® Text-to-Speech API). The speech synthesis engine converts the text data into natural, fluent speech data. This speech data is also stored in the buffer.

[0137] Audio output means

[0138] Finally, the generated audio data is sent to the device and delivered to the user through a headset or speaker (e.g., Bluetooth earphones). This allows the user to accurately hear what the other person is saying, even in noisy environments.

[0139] Specific example

[0140] For example, suppose User A is working in a noisy factory while wearing smart glasses. In this scenario, User B is giving instructions to User A, but User A cannot hear them due to the surrounding noise.

[0141] 1. User A turns their gaze towards User B.

[0142] 2. The device's built-in camera captures user A's gaze, and the gaze data is sent to the server.

[0143] 3. The server receives the gaze data, identifies user B's video, and retrieves the video data.

[0144] 4. The server uses a generative AI model to convert what user B is saying from the movement of their lips into text data.

[0145] 5. The server inputs the text data into the speech synthesis engine and generates the speech data.

[0146] 6. The generated audio data is sent to the terminal and played back through User A's headset.

[0147] This allows User A to accurately hear User B's instructions even in a noisy environment, enabling them to proceed with the work smoothly.

[0148] Example of a prompt

[0149] "User A wears smart glasses in the factory, identifies User B's image through their gaze, analyzes what they are saying from their lip movements, and plays it back as audio."

[0150] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0151] Step 1:

[0152] The device uses its built-in camera to capture the user's gaze. The captured gaze data is processed at a rate of 60 frames per second and input into an algorithm that calculates the direction of the gaze (for example, a face detection algorithm using Haar-like features). The calculated gaze data is sent to a server via Wi-Fi.

[0153] Input: User eye-tracking data

[0154] Data processing: Calculation of line of sight

[0155] Output: Eye-tracking data

[0156] Specific operation: When the user focuses on something, the device's built-in camera captures this action, detects the direction of their gaze in real time, and sends it to the server.

[0157] Step 2:

[0158] The server receives the gaze data and identifies the object the user is looking at. To obtain video of the identified object, the device's high-resolution camera is activated and captures video in the specified direction. The captured video data is compressed and sent to the server in real time.

[0159] Input: Eye-tracking data

[0160] Data processing: Identifying and capturing video of objects the user is focusing on.

[0161] Output: Video data

[0162] Specific operation: The server analyzes the gaze data to identify the object the user is looking at. The terminal's high-resolution camera captures a specified area and sends the video data to the server.

[0163] Step 3:

[0164] The server inputs the received video data into a generative AI model. This generative AI model analyzes the changes between video frames and recognizes lip movements. Based on the recognition results, it generates text data of what is being said. This text data is temporarily stored in a Redis buffer.

[0165] Input: Video data

[0166] Data processing: Recognition of lip movements and conversion to text.

[0167] Output: Text data

[0168] Specific operation: Video data is input into an AI model that generates text data by analyzing lip movements.

[0169] Step 4:

[0170] The server inputs the text data stored in the buffer into a speech synthesis engine. This speech synthesis engine (for example, the Google Text-to-Speech API) converts the text data into natural, fluent speech data. This converted speech data is also stored back into the buffer.

[0171] Input: Text data

[0172] Data processing: Converting text data to audio data

[0173] Output: Audio data

[0174] Specific operation: The server inputs text data into the speech synthesis engine and stores the generated audio file in a buffer.

[0175] Step 5:

[0176] The server sends the audio data from the buffer to the terminal. The terminal decodes the received audio data and plays it back to the user through a headset or speaker (e.g., Bluetooth earphones).

[0177] Input: Audio data

[0178] Data processing: Sending and playing back audio data.

[0179] Output: Audio to be played

[0180] Specific operation: The server sends audio data to the terminal, and the terminal plays it back, allowing the server to hear what the user is saying.

[0181] (Application Example 1)

[0182] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0183] Conventional systems presented challenges in human-machine communication within noisy factory environments, hindering smooth work instructions and information exchange. Furthermore, despite advancements in eye-tracking and lip-syncing technologies, a reliable method for accurately and quickly sending instructions to machines using these techniques remained unestablished. This resulted in decreased work efficiency and an increased risk of operational errors.

[0184] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0185] In this invention, the server is

[0186] A means of tracking the user's gaze,

[0187] A means for acquiring video footage of an object that the user is focusing on,

[0188] A lip motion analysis means analyzes the movement of the target's lips from video acquired by a video acquisition means and generates text data,

[0189] A speech synthesis means that converts generated text data into speech,

[0190] A voice output means that provides the user with voice generated by a voice synthesis means,

[0191] An automated means that transmits generated voice data to a machine and executes instructions,

[0192] This includes enabling accurate and efficient instruction transmission from the user to the machine, even in high-noise environments.

[0193] "Eye-tracking means" refers to a device or system that captures the movement of a user's eyes and calculates its direction.

[0194] "Image acquisition means" refers to a device or system for acquiring images of an object that the user is focusing on.

[0195] A "lip movement analysis means" is a device or system that analyzes the movement of a subject's lips from acquired video footage and generates text data.

[0196] "Speech synthesis means" refers to a device or system that converts generated text data into speech.

[0197] "Speech output means" refers to a device or system that provides the user with speech generated by speech synthesis means.

[0198] "Automation means" refers to a device or system for transmitting generated voice data to a machine and executing instructions.

[0199] A "wearable display" is a general term for a device that a user can wear.

[0200] A "generative AI model" is an artificial intelligence model used to generate specific information (in this case, the content of speech based on lip movements) from video data.

[0201] Modes for carrying out the invention

[0202] The present invention is a system for facilitating communication between a user and a machine in a high-noise environment, and is composed of a combination of eye-tracking means, video acquisition means, lip-movement analysis means, a generation AI model, speech synthesis means, speech output means, and automation means. The embodiments for carrying out the present invention will be described in detail below.

[0203] System Configuration

[0204] Eye-tracking methods

[0205] The device (wearable display) is equipped with a built-in camera to capture the user's eye movements. This camera can track the user's gaze and execute algorithms to calculate its direction. For example, Tobii Eye Tracker is used.

[0206] Video acquisition method

[0207] The device identifies the object the user is looking at through eye-tracking technology and acquires video footage of that object using a high-resolution camera. This video data is transmitted to the server in real time.

[0208] Lip motion analysis means

[0209] The server receives video data transmitted from the terminal and inputs it into a generative AI model used for lip movement analysis. This generative AI model, such as one from OpenAI (registered trademark), analyzes the movement of the other person's lips from the acquired video and generates text data of what is being said.

[0210] Speech synthesis means

[0211] The generated text data is input into a speech synthesis system on the server and converted into natural, fluent speech data. For example, the Python speech synthesis library pyttsx3 is used. This speech data is then adjusted to a quality that can be heard without discomfort by a user or machine.

[0212] Audio output means

[0213] Finally, the generated audio data is transmitted to a device (wearable display) and then provided to the user through a headset or speaker. This allows the user to accurately hear what the other person is saying, even in noisy environments.

[0214] automated means

[0215] Furthermore, the generated voice data is transmitted to a machine by automated means. The machine then performs predetermined actions based on the voice instructions.

[0216] Program processing

[0217] Each of the above-described mechanisms of the system is implemented using the following hardware and software.

[0218] Eye tracking method: Wearable display with built-in Tobii Eye Tracker

[0219] Video acquisition method: High-resolution camera, OpenCV

[0220] Lip movement analysis method: Generative AI model (OpenAI)

[0221] Speech synthesis method: pyttsx3

[0222] Audio output method: Headset or speaker

[0223] Automation methods: Machines (e.g., Boston Dynamics robots)

[0224] Specific example

[0225] For example, suppose a robot is used to transport parts within a factory. In a high-noise environment, communication using normal voice commands is difficult. In such a situation, a worker uses a wearable display to instruct the robot to move parts to a specific location.

[0226] 1. The worker turns their gaze towards the robot.

[0227] 2. The built-in camera captures the user's gaze and sends the gaze data to the server.

[0228] 3. The server analyzes the gaze data, identifies the robot's image, and acquires the image data.

[0229] 4. The generation AI model analyzes the video and generates text data that says, "Transport part A to line B."

[0230] 5. The speech synthesis engine converts this text data into speech data.

[0231] 6. The generated voice data is sent to the robot, and the robot acts according to the instructions.

[0232] Examples of prompts to input into a generative AI model

[0233] Video data:<video_stream>

[0234] Prompt: Analyze the lip movements in the video and output the spoken content as text data. For example, convert it to "Take part A to line B."

[0235] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0236] Step 1:

[0237] The user uses a wearable display and directs their gaze towards the robot. The device's built-in camera captures the user's gaze and transmits the data to a server in real time. Specifically, the Tobii Eye Tracker detects the user's eye movements and outputs gaze data. This gaze data indicates the direction and object the user is focusing on.

[0238] Step 2:

[0239] The server receives gaze data and performs data calculations based on that data to identify the image of the object the user is fixated on. The server acquires a video stream from a high-resolution camera and identifies the image of the object the user is fixated on. This video data includes the robot or work environment that the user is looking at. The identified video data is then stored on the server.

[0240] Step 3:

[0241] The server inputs the received video data into a generative AI model, which then performs lip-movement analysis. This generative AI model uses OpenAI technology to analyze lip movements in the video and convert the spoken content into text data. Specifically, it analyzes lip movements in the video data frame by frame and outputs the spoken content in text format in real time.

[0242] Step 4:

[0243] The generated text data is input into a speech synthesis system and converted into natural, fluent speech data on the server. Using the Python speech synthesis library pyttsx3, the generated text data is converted into an audio file. This audio data is in a format that can be understood by both users and machines.

[0244] Step 5:

[0245] The generated audio data is transmitted from the server to the terminal and provided to the user through the terminal's headset or speakers. By listening to this audio data, the user can accurately understand what the other person is saying, even in noisy environments. Specifically, the generated audio data is output from the headset and reaches the user's ears.

[0246] Step 6:

[0247] Furthermore, the generated voice data is transmitted from the server to the machine, and instructions are executed by automated means. The machine (for example, a Boston Dynamics robot) performs predetermined actions based on these voice instructions. Specifically, a robot that receives the voice instruction "Move part A to line B" will move the part to the designated location accordingly.

[0248] This allows users to give precise instructions to robots even in noisy environments, enabling efficient work.

[0249] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0250] The present invention is a system for facilitating communication in high-noise environments, and is comprised of a combination of eye-tracking means, video acquisition means, lip-movement analysis means, a generative AI model, speech synthesis means, speech output means, and an emotion engine. The embodiments for carrying out the invention will be described in detail below.

[0251] System Configuration

[0252] Eye-tracking methods

[0253] The device (smart glasses) uses a built-in camera to capture the user's eye movements and calculate their gaze direction. This data is transmitted to a server in real time.

[0254] Video acquisition method

[0255] The system uses eye-tracking technology to capture video of the object the user is focusing on using a high-resolution camera. The video data is transmitted to a server in real time and used for analysis.

[0256] Lip motion analysis means

[0257] The server receives video data transmitted from the terminal and analyzes the lip movements using a generation AI model. This generates text data of what the subject is saying.

[0258] Speech synthesis means

[0259] The generated text data is input into a speech synthesis engine and converted into natural, fluent speech data. This speech data is then adjusted for listening.

[0260] Audio output means

[0261] The generated audio data is sent to the terminal and provided to the user through a headset or speaker. This allows the user to accurately hear what the other person is saying, even in noisy environments.

[0262] Emotional Engine

[0263] This system also incorporates an emotion engine that analyzes the user's facial expressions and voice tone to recognize their emotions. This enables the system to provide feedback and responses tailored to the user's emotional state.

[0264] Program processing

[0265] The following describes, in natural language, how the system of the present invention works.

[0266] Eye Tracking

[0267] The device continuously captures the user's gaze using its built-in camera and calculates its direction. This gaze data is transmitted to the server in real time.

[0268] Video acquisition

[0269] The server receives gaze data and identifies the video of the object the user is fixated on based on that data. The identified video is acquired in high resolution and further analyzed.

[0270] Lip movement analysis

[0271] The acquired video data is input into an AI model, which generates text data from the lip movements to understand what is being said. This text data is temporarily stored in a buffer.

[0272] Speech synthesis

[0273] Text data is input into a speech synthesis engine and converted into natural-sounding speech data. The generated speech data is then stored back in a buffer.

[0274] Audio output

[0275] The audio data stored in the buffer is transmitted to the terminal and provided to the user through a headset or speaker. By listening to this, the user can understand what the other person is saying, even in noisy environments.

[0276] emotion recognition

[0277] The emotion engine analyzes the user's facial expressions and voice tone to recognize their emotional state. This information is sent to the server in real time, and appropriate feedback is provided as needed.

[0278] Specific example

[0279] For example, suppose User A is working in a noisy factory. In this situation, User B is giving User A visual instructions, but User A cannot hear the instructions due to the surrounding noise. The following is the processing flow for this situation.

[0280] 1. User A wears smart glasses and directs their gaze towards User B. The device's camera captures User A's gaze and sends the data to the server.

[0281] 2. The server receives the gaze data, identifies user B's video, and acquires the video in high resolution.

[0282] 3. The server uses the generated AI model to generate the content being spoken from the lip movements of User B as text data.

[0283] 4. The server inputs the text data into a speech synthesis engine and converts it into natural speech data.

[0284] 5. The generated speech data is sent to the terminal and played back through User A's headset. User A can thus accurately listen to the instructions.

[0285] 6. At the same time, the emotion engine analyzes User A's facial expressions and voice tones to recognize the emotional state. If necessary, the server provides appropriate feedback.

[0286] In this way, the system of the present invention enables efficient communication even in a noisy environment and also enables corresponding actions according to the emotional state of the user.

[0287] The processing flow will be described below.

[0288] Processing Flow

[0289] Step 1:

[0290] The terminal captures the user's line of sight.

[0291] The camera built into the terminal (smart glasses) continuously captures the movement of the user's pupils.

[0292] The captured video data is processed in real time to calculate the user's line of sight direction.

[0293] Step 2:

[0294] The terminal sends the line of sight data to the server.

[0295] The data of the calculated line-of-sight direction is transmitted to the server at regular time intervals.

[0296] The line-of-sight data includes information such as the coordinates of the user's fixation point.

[0297] Step 3:

[0298] The server receives the line-of-sight data and acquires the video of the object being fixated on.

[0299] The server receives the line-of-sight data transmitted from the terminal.

[0300] Based on the received line-of-sight data, the video area of the object that the user is fixating on is specified.

[0301] Detailed video data of the specified video area is acquired from the terminal.

[0302] Step 4:

[0303] The server analyzes the lip movement and generates text data.

[0304] The acquired video data is input into the generation AI model in the server.

[0305] The generation AI model analyzes the lip movement and converts the spoken content into text data.

[0306] The generated text data is temporarily stored in the buffer.

[0307] Step 5:

[0308] The server inputs the text data into the speech synthesis engine.

[0309] The text data stored in the buffer is input into the speech synthesis engine.

[0310] The speech synthesis engine converts the input text data into natural speech data.

[0311] The generated audio data is stored back into the buffer.

[0312] Step 6:

[0313] The server sends the audio data to the terminal.

[0314] The audio data stored in the buffer is sent to the terminal as soon as it is ready for playback.

[0315] Step 7:

[0316] The device plays the audio data.

[0317] The device plays the received audio data through a headset or speaker.

[0318] The user listens to the generated audio through a headset or similar device.

[0319] Step 8:

[0320] The device acquires the user's facial expression data.

[0321] A camera built into the device (smart glasses) captures the user's facial expressions.

[0322] The captured facial expression data is sent to the server in real time.

[0323] Step 9:

[0324] The server analyzes the user's emotions.

[0325] The server receives facial expression data from the terminal and inputs it into the emotion engine.

[0326] The emotion engine analyzes facial expression data to identify the user's emotional state.

[0327] The analysis results are processed in real time, and feedback is provided as needed.

[0328] Specific example

[0329] For example, the following shows the processing flow when User A is working in a noisy factory and User B is giving instructions to User A, but User B cannot hear User A due to the surrounding noise.

[0330] Step 1:

[0331] User A wears smart glasses and directs their gaze towards User B. The device's built-in camera captures User A's eye movements and sends the data to the server.

[0332] Step 2:

[0333] The terminal sends calculated gaze data to the server. This includes coordinate information for the direction in which user A is looking.

[0334] Step 3:

[0335] The server receives gaze data and identifies user B's video area based on it. The identified video area is then retrieved from the terminal.

[0336] Step 4:

[0337] The server inputs the acquired video data into a generating AI model, which analyzes lip movements and converts them into text data. The generated text data is then temporarily stored in a buffer.

[0338] Step 5:

[0339] The server inputs the text data stored in the buffer into the speech synthesis engine and converts it into natural-sounding speech data. The generated speech data is then stored back into the buffer.

[0340] Step 6:

[0341] The server sends the generated audio data to the terminal.

[0342] Step 7:

[0343] The terminal plays the received audio data through the headset, and user A listens to the audio.

[0344] Step 8:

[0345] The device's built-in camera captures user A's facial expressions. The captured facial expression data is sent to the server in real time.

[0346] Step 9:

[0347] The server receives facial expression data and inputs it into the emotion engine. The emotion engine analyzes user A's facial expressions and recognizes their emotional state. The analysis results are processed in real time, and appropriate feedback is provided as needed.

[0348] This allows User A to accurately hear User B's instructions even in a noisy environment, and also enables responses tailored to User A's emotional state.

[0349] (Example 2)

[0350] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0351] In noisy environments, communication between users becomes difficult, hindering the accurate transmission of information. Furthermore, the inability to understand users' emotional states can lead to delays in responding appropriately to stressful or difficult situations. Solving these problems is essential.

[0352] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0353] In this invention, the server includes eye-tracking means, video acquisition means, lip-movement analysis means, speech synthesis means, speech output means, and emotion recognition means. This enables accurate information transmission between users even in high-noise environments, and further enables responses that correspond to the user's emotional state.

[0354] "Eye-tracking means" refers to a device or technology for detecting a user's gaze and determining its direction.

[0355] "Image acquisition means" refers to a device or technology for acquiring images of an object that a user is focusing on.

[0356] "Lip movement analysis means" refers to a device or technology for analyzing the movement of a subject's lips from video data and generating text data based on that movement.

[0357] "Speech synthesis means" refers to a device or technology for converting generated text data into speech data.

[0358] "Audio output means" refers to a device or technology for providing generated audio data to a user.

[0359] "Emotion recognition means" refers to a device or technology that analyzes a user's facial expressions and voice tone to recognize the user's emotional state.

[0360] A "head-mounted display device" is a device worn on the user's head that provides functions such as eye tracking and image acquisition.

[0361] An "image sensor" is an electronic component or technology used to capture images or videos.

[0362] A "generative model" is an algorithm or technique that generates new data from specific data based on AI technology.

[0363] The present invention is a system for facilitating communication in high-noise environments, and is comprised of a combination of eye-tracking means, video acquisition means, lip-movement analysis means, a generation AI model, speech synthesis means, speech output means, and emotion recognition means. The embodiments for carrying out the invention will be described in detail below.

[0364] System Configuration

[0365] Eye-tracking methods

[0366] The device (head-mounted display device) uses an image sensor to capture the user's eye movements and calculate their gaze direction. This data is transmitted to the server in real time.

[0367] Video acquisition method

[0368] The system uses eye-tracking technology to capture video of the object the user is focusing on using a high-resolution camera. The video data is transmitted to a server in real time and used for analysis.

[0369] Lip motion analysis means

[0370] The server receives video data transmitted from the terminal and analyzes the lip movements using a generation AI model. This generates text data of what the subject is saying.

[0371] Speech synthesis means

[0372] The generated text data is input into a speech synthesis engine and converted into natural, fluent speech data. This speech data is then adjusted for listening.

[0373] Audio output means

[0374] The generated audio data is sent to the terminal and provided to the user through a headset or speaker. This allows the user to accurately hear what the other person is saying, even in noisy environments.

[0375] emotion recognition means

[0376] This system also incorporates emotion recognition capabilities, analyzing the user's facial expressions and voice tone to recognize their emotions. This enables the system to provide feedback and responses tailored to the user's emotional state.

[0377] Specific example

[0378] For example, suppose User A is working in a noisy factory. In this situation, User B is giving User A visual instructions, but User A cannot hear the instructions due to the surrounding noise. The following is the processing flow for this situation.

[0379] 1. User A wears a head-mounted display device and directs their gaze towards User B. The terminal's image sensor captures User A's gaze and transmits the data to the server.

[0380] 2. The server receives the gaze data, identifies user B's video, and acquires the video in high resolution.

[0381] 3. The server uses a generated AI model to generate text data from the lip movements of user B, indicating what is being said.

[0382] 4. The server inputs the text data into the speech synthesis engine and converts it into natural-sounding speech data.

[0383] 5. The generated audio data is sent to the terminal and played back through User A's headset. This allows User A to accurately hear the instructions.

[0384] 6. Simultaneously, the emotion recognition system analyzes user A's facial expressions and voice tone to recognize their emotional state. If necessary, the server provides appropriate feedback.

[0385] Example of a prompt

[0386] The following are specific examples of prompt statements to be input to the generating AI model.

[0387] "Please explain the procedure for performing real-time lip-sync analysis on text content specified by User B and converting it into speech data."

[0388] "Please provide an example of how a system can support smooth communication between user A and user B in a high-noise environment."

[0389] "Please provide examples of feedback tailored to the user's emotional state."

[0390] Thus, the system of the present invention enables efficient communication even in high-noise environments and can also respond according to the user's emotional state.

[0391] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0392] Step 1:

[0393] Eye Tracking

[0394] Specific actions:

[0395] User: Wear the head-mounted display device and direct your gaze towards the object you wish to observe.

[0396] Terminal: An image sensor built into a head-mounted display device captures the user's eye movements.

[0397] Input: Eye movement data captured by the image sensor.

[0398] Data processing / calculation: Calculates the direction of gaze from eye movement data.

[0399] Output: Calculated line-of-sight direction data.

[0400] Terminal: Sends captured eye-tracking data to the server in real time.

[0401] Step 2:

[0402] Video acquisition

[0403] Specific actions:

[0404] Server: Receives gaze data and identifies the object in the direction the user is looking.

[0405] Device: Acquires video footage of the identified target using a high-resolution camera.

[0406] Input: Actual viewing direction data.

[0407] Data processing / calculation: Adjust the camera's field of view based on the direction of gaze and capture the video.

[0408] Output: Acquired video data.

[0409] Terminal: Transmits acquired video data to the server in real time.

[0410] Step 3:

[0411] Lip movement analysis

[0412] Specific actions:

[0413] Server: Receives video data sent from the terminal.

[0414] Input: Video data.

[0415] Data processing / calculation: Input video data into an AI model to analyze lip movements.

[0416] Output: Text data of words generated based on lip movements.

[0417] Server: Based on lip-movement analysis, it identifies fragments of spoken language and generates them as text data.

[0418] Step 4:

[0419] Text generation

[0420] Specific actions:

[0421] Server: Converts words identified from lip movements into text data.

[0422] Input: Lip movement analysis data.

[0423] Data processing / calculation: Based on the analyzed lip-sync data, text data is generated through natural language processing.

[0424] Output: Text data.

[0425] Server: Temporarily stores text data in a buffer.

[0426] Step 5:

[0427] Speech synthesis

[0428] Specific actions:

[0429] Server: Inputs text data into the speech synthesis engine.

[0430] Input: Text data.

[0431] Data processing / calculation: Perform a speech synthesis process to convert text data into natural-sounding speech data.

[0432] Output: Audio data.

[0433] Server: The speech synthesis engine converts text data into natural-sounding speech data.

[0434] Server: Stores the generated audio data in a buffer.

[0435] Step 6:

[0436] Audio output

[0437] Specific actions:

[0438] Server: Sends the audio data in the buffer to the terminal.

[0439] Input: Audio data in the buffer.

[0440] Data processing / calculation: Adjust data into a format that can be provided to users.

[0441] Output: Adjusted audio data.

[0442] Device: Plays audio data through a headset or speaker.

[0443] User: Listen to the audio coming from the headset and understand the instructions.

[0444] Step 7:

[0445] emotion recognition

[0446] Specific actions:

[0447] Device: The camera captures the user's facial expressions, and the microphone records their voice tone.

[0448] Input: Facial expression data and voice tone data.

[0449] Data processing / calculation: Facial expressions and voice tone are analyzed using an emotion recognition algorithm.

[0450] Output: Emotional state data.

[0451] Terminal: Sends data on facial expressions and voice tone to the server.

[0452] Server: The emotion engine analyzes facial expressions and voice tone to recognize the emotional state.

[0453] Server: Based on the recognized emotional state, it sends appropriate feedback and notifications to the device.

[0454] User: Receive and respond to the feedback provided.

[0455] (Application Example 2)

[0456] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0457] A major challenge is the difficulty of communication in high-noise environments. In such environments, verbal communication is hindered, leading to increased errors in instructions and guidance. Furthermore, accurately understanding and appropriately responding to users' emotional states becomes difficult. Therefore, a system is needed that enables effective communication and recognition of user emotions even in noisy environments.

[0458] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes eye-tracking means for tracking the user's gaze, image acquisition means for acquiring an image of an object the user is fixated on, lip movement analysis means for analyzing the movement of the object's lips from the image acquired by the image acquisition means and generating text data, speech synthesis means for converting the generated text data into speech, speech output means for providing the speech generated by the speech synthesis means to the user, and emotion recognition means for analyzing the user's facial expressions and voice tone to recognize their emotional state. This makes it possible to accurately transmit information visually and audibly even in high-noise environments, and furthermore, to provide appropriate feedback according to the user's emotional state.

[0459] "Eye-tracking means" refers to a method of tracking a user's gaze using a camera built into a wearable device and calculating its direction.

[0460] "Video acquisition means" refers to means of acquiring video footage of an object that a user is focusing on, as identified by eye-tracking means.

[0461] A "lip movement analysis means" is a means of analyzing the movement of a subject's lips from acquired video footage and converting the spoken content into text data.

[0462] A "speech synthesis method" is a means of converting generated text data into natural-sounding speech data.

[0463] "Audio output means" refers to means such as headsets or speakers for providing the generated audio data to the user.

[0464] An "emotion recognition method" is a means of recognizing a user's emotional state by analyzing their facial expressions and voice tone.

[0465] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to analyze data such as lip movements and generate text data and other output data.

[0466] The present invention is a system that enables smooth communication between users in high-noise environments. The main components are eye-tracking means, video acquisition means, lip-movement analysis means, generation AI model, speech synthesis means, speech output means, and emotion recognition means.

[0467] Eye-tracking methods

[0468] The device uses its built-in camera to track the user's gaze. This calculates the user's gaze direction, and this data is sent to the server in real time. To achieve this functionality, an eye-tracking algorithm (e.g., Tobii Pro SDK) is used.

[0469] Video acquisition method

[0470] The server receives data transmitted from the eye-tracking device and identifies and acquires the video of the object the user is fixated on. The identified video is captured by a high-resolution camera (e.g., OpenCV) and transmitted as data for analysis.

[0471] Lip motion analysis means

[0472] The server uses a generative AI model to analyze the video acquired by the video acquisition device. This generative AI model has the ability to analyze the movement of the subject's lips and convert it into text data (e.g., OpenAI GPT, DALL-E). An example of a prompt message is: "Analyze lip movements from video taken in a high-noise environment and convert what is being said into text data. Video data: {video data} Response format: text format."

[0473] Speech synthesis means

[0474] To convert the generated text data into speech, the server uses a speech synthesis engine (e.g., Google Text-to-Speech API, IBM Watson® Text to Speech). This produces natural-sounding speech data.

[0475] Audio output means

[0476] The generated audio data is transmitted to the terminal in real time and output through the user's headset or speakers. This allows the user to accurately hear what the other person is saying, even in noisy environments.

[0477] emotion recognition means

[0478] Furthermore, the emotion recognition system analyzes the user's facial expressions and voice tone. An emotion analysis engine (e.g., Microsoft® Azure® Emotion API) is used to recognize the user's emotional state. This information is transmitted to the server in real time, providing appropriate feedback based on the user's emotional state.

[0479] Specific example

[0480] For example, this system is useful when a user operating heavy machinery in a factory needs to exchange visual and auditory information with a colleague at a distance. The user identifies the colleague by their gaze and analyzes their lip movements, converting them into text data. This text data is then converted into speech and output through a headset, ensuring clear communication of instructions even in noisy environments. Furthermore, the system recognizes the user's emotional state in real time, providing appropriate feedback.

[0481] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0482] Step 1:

[0483] The device uses its built-in camera to continuously capture the user's eye movements and calculate their gaze direction. The input is video data of the user's eyes, and the output is gaze direction data. This gaze direction data is sent to the server in real time. Specifically, the gaze direction is determined using an eye-tracking algorithm (e.g., Tobii Pro SDK).

[0484] Step 2:

[0485] The server receives the transmitted gaze direction data and identifies the object the user is looking at based on that data. The input is gaze direction data, and the output is the coordinate information of the object being gazed at. Based on this information, a high-resolution camera is used to acquire video of the object being gazed at. Specifically, a video processing library (e.g., OpenCV) is used to capture the video data.

[0486] Step 3:

[0487] The server inputs video data acquired by a high-resolution camera into a generative AI model to analyze the movement of the subject's lips. The input is video data, and the output is the analyzed text data. As a specific example, a prompt message using a generative AI model (e.g., OpenAI GPT, DALL-E) would be: "Analyze lip movements from video footage taken in a high-noise environment and convert what is being said into text data. Video data: {video data} Response format: text format."

[0488] Step 4:

[0489] The server inputs text data generated by lip-sync analysis into a speech synthesis engine and converts it into natural-sounding speech data. The input is text data, and the output is speech data. Specifically, it utilizes a speech synthesis engine (e.g., Google Text-to-Speech API, IBM Watson Text to Speech).

[0490] Step 5:

[0491] The server sends the generated audio data to the terminal, which then provides it to the user through a headset or speaker. The input is the audio data, and the output is the audio provided to the user. Specifically, the process involves outputting the audio data through the terminal's headset or speaker.

[0492] Step 6:

[0493] The server uses an emotion analysis engine to analyze the user's facial expressions and voice tone to recognize their emotional state. The input is the user's facial expression data and voice tone data, and the output is the recognized emotional state data. Specifically, it performs real-time analysis using an emotion analysis engine (e.g., Microsoft Azure Emotion API). This emotional state data is sent to the server, and appropriate feedback is provided as needed.

[0494] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0495] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0496] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0497] [Second Embodiment]

[0498] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0499] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0500] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0501] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0502] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0503] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0504] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0505] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0506] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0507] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0508] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0509] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0510] The present invention is a system for facilitating communication in high-noise environments, and is composed of a combination of eye-tracking means, video acquisition means, lip-movement analysis means, generation AI model, speech synthesis means, and speech output means. The embodiments for carrying out the invention will be described in detail below.

[0511] System Configuration

[0512] Eye-tracking methods

[0513] The device (smart glasses) is equipped with a built-in camera to capture the user's eye movements. This camera can track the user's gaze and execute an algorithm to calculate its direction.

[0514] Video acquisition method

[0515] The device identifies the object the user is looking at through eye-tracking technology and acquires video footage of that object using a high-resolution camera. This video data is transmitted to the server in real time.

[0516] Lip motion analysis means

[0517] The server receives video data transmitted from the terminal and inputs it into a generative AI model used for lip movement analysis. This generative AI model analyzes the movement of the other person's lips from the acquired video and generates text data of what is being said.

[0518] Speech synthesis means

[0519] The generated text data is input into a speech synthesis system on the server and converted into natural, fluent speech data. This speech data is then adjusted to a quality that users can listen to without any discomfort.

[0520] Audio output means

[0521] Finally, the generated audio data is sent to a device (smart glasses) and then provided to the user through a headset or speaker. This allows the user to accurately hear what the other person is saying, even in noisy environments.

[0522] Program processing

[0523] The following describes, in natural language, how the system of the present invention works.

[0524] Eye Tracking

[0525] The device continuously captures the user's gaze using its built-in camera and calculates the direction of their gaze. The calculated gaze data is sent to the server in real time.

[0526] Video acquisition

[0527] The server receives gaze data and uses it to identify the video object the user is fixated on. The identified video is then acquired for further detailed analysis.

[0528] Lip movement analysis

[0529] The acquired video is sent to a server, where a generative AI model analyzes the lip movements to determine what is being said and generates it as text data. This text data is temporarily stored in a buffer.

[0530] Speech synthesis

[0531] The text data stored in the buffer is input to the speech synthesis engine and converted into natural-sounding speech data. This speech data is then stored back into the buffer.

[0532] Audio output

[0533] Finally, the generated audio data is sent to the terminal and provided to the user through a headset or speaker, allowing the user to accurately hear what the other person is saying even in noisy environments.

[0534] Specific example

[0535] For example, suppose User A is working in a noisy factory while wearing smart glasses. In this scenario, User B is giving instructions to User A, but User A cannot hear them due to the surrounding noise.

[0536] 1. User A turns their gaze towards User B.

[0537] 2. The device's built-in camera captures user A's gaze, and the gaze data is sent to the server.

[0538] 3. The server receives the gaze data, identifies user B's video, and retrieves the video data.

[0539] 4. The server uses a generative AI model to convert what user B is saying from the movement of their lips into text data.

[0540] 5. The server inputs the text data into the speech synthesis engine and generates the speech data.

[0541] 6. The generated audio data is sent to the terminal and played back through User A's headset.

[0542] This allows User A to accurately hear User B's instructions even in a noisy environment, enabling them to proceed with the work smoothly.

[0543] Thus, the system of the present invention enables efficient communication in a variety of high-noise environments.

[0544] The following describes the processing flow.

[0545] Step 1:

[0546] The device captures the user's gaze.

[0547] The camera built into the device continuously captures the user's eye movements.

[0548] The system processes video data from the camera in real time and executes an eye-tracking algorithm to calculate the user's gaze direction.

[0549] Step 2:

[0550] The device sends eye-tracking data to the server.

[0551] The calculated line-of-sight direction data is sent to the server at regular time intervals.

[0552] Eye-tracking data includes information such as the coordinates of the user's gaze point.

[0553] Step 3:

[0554] The server receives gaze data and acquires video footage of the object the user is looking at.

[0555] The server receives the gaze data transmitted from the terminal.

[0556] Based on the received gaze data, the system identifies the video area of ​​the object the user is fixated on.

[0557] Detailed video data of the identified video area is obtained from the device.

[0558] Step 4:

[0559] The server analyzes lip movements and generates text data.

[0560] The acquired video data is input into the AI ​​model on the server.

[0561] The generative AI model analyzes lip movements and converts what is being said into text data.

[0562] The generated text data is temporarily stored in a buffer.

[0563] Step 5:

[0564] The server inputs text data into the speech synthesis engine.

[0565] The text data stored in the buffer is input to the speech synthesis engine.

[0566] A speech synthesis engine converts input text data into natural-sounding speech data.

[0567] The generated audio data is stored back into the buffer.

[0568] Step 6:

[0569] The server sends the audio data to the terminal.

[0570] The audio data stored in the buffer is sent to the terminal as soon as it is ready for playback.

[0571] Step 7:

[0572] The device plays the audio data.

[0573] The device plays the received audio data through a headset or speaker.

[0574] The user listens to the generated audio through a headset or similar device.

[0575] Specific example

[0576] For example, the following shows the processing flow when User B gives visual instructions to User A while User A is working in a noisy factory.

[0577] Step 1:

[0578] User A wears smart glasses and directs their gaze towards User B. The camera in the device captures User A's eye movements and calculates the direction of their gaze.

[0579] Step 2:

[0580] The terminal sends calculated gaze data to the server. This includes coordinate information for the direction in which user A is looking.

[0581] Step 3:

[0582] The server receives gaze data and identifies user B's video area based on it. The identified video area is then retrieved from the terminal.

[0583] Step 4:

[0584] The server inputs the acquired video data into a generating AI model, which analyzes lip movements and converts them into text data. The generated text data is then temporarily stored in a buffer.

[0585] Step 5:

[0586] The server inputs the text data stored in the buffer into the speech synthesis engine and converts it into natural-sounding speech data. The generated speech data is then stored back into the buffer.

[0587] Step 6:

[0588] The server sends the generated audio data to the terminal.

[0589] Step 7:

[0590] The terminal plays the received audio data through the headset, and user A listens to the audio.

[0591] This allows user A to accurately receive instructions from user B even in noisy environments, enabling them to carry out their work efficiently.

[0592] (Example 1)

[0593] Next, we will describe Example 1. 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."

[0594] Communication in high-noise environments presents a significant challenge due to the difficulty in hearing voices. This is particularly problematic in places requiring concentration, such as factories and construction sites, where important instructions may be difficult to convey, potentially negatively impacting work efficiency and safety. The present invention aims to provide a means for effective communication even in such high-noise environments.

[0595] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0596] In this invention, the server includes eye-tracking means for tracking the user's gaze, image acquisition means for acquiring images of an object the user is fixated on, lip-movement analysis means for analyzing the movement of the object's lips from the images acquired by the image acquisition means and generating text data, speech synthesis means for converting the generated text data into speech, and speech output means for providing the user with the speech generated by the speech synthesis means. This makes it possible for the user to accurately hear what the other person is saying, even in a high-noise environment.

[0597] "Eye-tracking means" refers to devices or algorithms that capture the movement of a user's eyes and calculate the direction of their gaze.

[0598] "Image acquisition means" refers to devices or technologies for acquiring images of objects that a user is focusing on.

[0599] A "lip movement analysis means" refers to a device or generation AI model that analyzes the movement of a subject's lips from acquired video footage and converts the spoken content into text data.

[0600] "Speech synthesis means" refers to a device or engine that converts generated text data into natural-sounding speech data.

[0601] "Audio output means" refers to a device for providing the user with audio generated by a speech synthesis means, and includes headsets and speakers.

[0602] A "wearable device" is an electronic device that a user can wear and use.

[0603] A "machine learning model" is an algorithm that learns patterns from data and uses them to make predictions and classifications.

[0604] The present invention is a system for facilitating communication in high-noise environments, and is comprised of a combination of eye-tracking means, video acquisition means, lip-movement analysis means, a generation AI model, speech synthesis means, and speech output means. The following describes in detail the embodiments for implementing this system.

[0605] System Configuration

[0606] Eye-tracking methods

[0607] The device (wearable device) is equipped with a built-in camera to capture the user's eye movements. This camera can track the user's gaze and execute an algorithm to calculate its direction. Specifically, a face detection algorithm using Haar-like features is applied.

[0608] Video acquisition method

[0609] The device identifies the object the user is looking at through eye-tracking technology and captures video of that object using a high-resolution camera. This camera has a 1080p resolution and captures video in real time. The acquired video data is compressed and transmitted to a server via Wi-Fi.

[0610] Lip motion analysis means

[0611] The server receives video data sent from the terminal and inputs it into a generative AI model. This generative AI model is a TensorFlow-based RNN model that analyzes the lip movements of the other party from the acquired video and generates text data of what is being said. This text data is temporarily stored in a Redis buffer.

[0612] Speech synthesis means

[0613] The text data stored in the buffer is input to a speech synthesis engine on the server (e.g., Google Text-to-Speech API). The speech synthesis engine converts the text data into natural, fluent speech data. This speech data is also stored in the buffer.

[0614] Audio output means

[0615] Finally, the generated audio data is sent to the device and delivered to the user through a headset or speaker (e.g., Bluetooth earphones). This allows the user to accurately hear what the other person is saying, even in noisy environments.

[0616] Specific example

[0617] For example, suppose User A is working in a noisy factory while wearing smart glasses. In this scenario, User B is giving instructions to User A, but User A cannot hear them due to the surrounding noise.

[0618] 1. User A turns their gaze towards User B.

[0619] 2. The device's built-in camera captures user A's gaze, and the gaze data is sent to the server.

[0620] 3. The server receives the gaze data, identifies user B's video, and retrieves the video data.

[0621] 4. The server uses a generative AI model to convert what user B is saying from the movement of their lips into text data.

[0622] 5. The server inputs the text data into the speech synthesis engine and generates the speech data.

[0623] 6. The generated audio data is sent to the terminal and played back through User A's headset.

[0624] This allows User A to accurately hear User B's instructions even in a noisy environment, enabling them to proceed with the work smoothly.

[0625] Example of a prompt

[0626] "User A wears smart glasses in the factory, identifies User B's image through their gaze, analyzes what they are saying from their lip movements, and plays it back as audio."

[0627] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0628] Step 1:

[0629] The device uses its built-in camera to capture the user's gaze. The captured gaze data is processed at a rate of 60 frames per second and input into an algorithm that calculates the direction of the gaze (for example, a face detection algorithm using Haar-like features). The calculated gaze data is sent to a server via Wi-Fi.

[0630] Input: User eye-tracking data

[0631] Data processing: Calculation of line of sight

[0632] Output: Eye-tracking data

[0633] Specific operation: When the user focuses on something, the device's built-in camera captures this action, detects the direction of their gaze in real time, and sends it to the server.

[0634] Step 2:

[0635] The server receives the gaze data and identifies the object the user is looking at. To obtain video of the identified object, the device's high-resolution camera is activated and captures video in the specified direction. The captured video data is compressed and sent to the server in real time.

[0636] Input: Eye-tracking data

[0637] Data processing: Identifying and capturing video of objects the user is focusing on.

[0638] Output: Video data

[0639] Specific operation: The server analyzes the gaze data to identify the object the user is looking at. The terminal's high-resolution camera captures a specified area and sends the video data to the server.

[0640] Step 3:

[0641] The server inputs the received video data into a generative AI model. This generative AI model analyzes the changes between video frames and recognizes lip movements. Based on the recognition results, it generates text data of what is being said. This text data is temporarily stored in a Redis buffer.

[0642] Input: Video data

[0643] Data processing: Recognition of lip movements and conversion to text.

[0644] Output: Text data

[0645] Specific operation: Video data is input into an AI model that generates text data by analyzing lip movements.

[0646] Step 4:

[0647] The server inputs the text data stored in the buffer into a speech synthesis engine. This speech synthesis engine (for example, the Google Text-to-Speech API) converts the text data into natural, fluent speech data. This converted speech data is also stored back into the buffer.

[0648] Input: Text data

[0649] Data processing: Converting text data to audio data

[0650] Output: Audio data

[0651] Specific operation: The server inputs text data into the speech synthesis engine and stores the generated audio file in a buffer.

[0652] Step 5:

[0653] The server sends the audio data from the buffer to the terminal. The terminal decodes the received audio data and plays it back to the user through a headset or speaker (e.g., Bluetooth earphones).

[0654] Input: Audio data

[0655] Data processing: Sending and playing back audio data.

[0656] Output: Audio to be played

[0657] Specific operation: The server sends audio data to the terminal, and the terminal plays it back, allowing the server to hear what the user is saying.

[0658] (Application Example 1)

[0659] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0660] Conventional systems presented challenges in human-machine communication within noisy factory environments, hindering smooth work instructions and information exchange. Furthermore, despite advancements in eye-tracking and lip-syncing technologies, a reliable method for accurately and quickly sending instructions to machines using these techniques remained unestablished. This resulted in decreased work efficiency and an increased risk of operational errors.

[0661] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0662] In this invention, the server is

[0663] A means of tracking the user's gaze,

[0664] A means for acquiring video footage of an object that the user is focusing on,

[0665] A lip motion analysis means analyzes the movement of the target's lips from video acquired by a video acquisition means and generates text data,

[0666] A speech synthesis means that converts generated text data into speech,

[0667] A voice output means that provides the user with voice generated by a voice synthesis means,

[0668] An automated means that transmits generated voice data to a machine and executes instructions,

[0669] This includes enabling accurate and efficient instruction transmission from the user to the machine, even in high-noise environments.

[0670] "Eye-tracking means" refers to a device or system that captures the movement of a user's eyes and calculates its direction.

[0671] "Image acquisition means" refers to a device or system for acquiring images of an object that the user is focusing on.

[0672] A "lip movement analysis means" is a device or system that analyzes the movement of a subject's lips from acquired video footage and generates text data.

[0673] "Speech synthesis means" refers to a device or system that converts generated text data into speech.

[0674] "Speech output means" refers to a device or system that provides the user with speech generated by speech synthesis means.

[0675] "Automation means" refers to a device or system for transmitting generated voice data to a machine and executing instructions.

[0676] A "wearable display" is a general term for a device that a user can wear.

[0677] A "generative AI model" is an artificial intelligence model used to generate specific information (in this case, the content of speech based on lip movements) from video data.

[0678] Modes for carrying out the invention

[0679] The present invention is a system for facilitating communication between a user and a machine in a high-noise environment, and is composed of a combination of eye-tracking means, video acquisition means, lip-movement analysis means, a generation AI model, speech synthesis means, speech output means, and automation means. The embodiments for carrying out the present invention will be described in detail below.

[0680] System Configuration

[0681] Eye-tracking methods

[0682] The device (wearable display) is equipped with a built-in camera to capture the user's eye movements. This camera can track the user's gaze and execute algorithms to calculate its direction. For example, Tobii Eye Tracker is used.

[0683] Video acquisition method

[0684] The device identifies the object the user is looking at through eye-tracking technology and acquires video footage of that object using a high-resolution camera. This video data is transmitted to the server in real time.

[0685] Lip motion analysis means

[0686] The server receives video data transmitted from the terminal and inputs it into a generative AI model used for lip movement analysis. This generative AI model, such as one from OpenAI, analyzes the movement of the other person's lips from the acquired video and generates text data of what is being said.

[0687] Speech synthesis means

[0688] The generated text data is input into a speech synthesis system on the server and converted into natural, fluent speech data. For example, the Python speech synthesis library pyttsx3 is used. This speech data is then adjusted to a quality that can be heard without discomfort by a user or machine.

[0689] Audio output means

[0690] Finally, the generated audio data is transmitted to a device (wearable display) and then provided to the user through a headset or speaker. This allows the user to accurately hear what the other person is saying, even in noisy environments.

[0691] automated means

[0692] Furthermore, the generated voice data is transmitted to a machine by automated means. The machine then performs predetermined actions based on the voice instructions.

[0693] Program processing

[0694] Each of the above-described mechanisms of the system is implemented using the following hardware and software.

[0695] Eye tracking method: Wearable display with built-in Tobii Eye Tracker

[0696] Video acquisition method: High-resolution camera, OpenCV

[0697] Lip movement analysis method: Generative AI model (OpenAI)

[0698] Speech synthesis method: pyttsx3

[0699] Audio output method: Headset or speaker

[0700] Automation methods: Machines (e.g., Boston Dynamics robots)

[0701] Specific example

[0702] For example, suppose a robot is used to transport parts within a factory. In a high-noise environment, communication using normal voice commands is difficult. In such a situation, a worker uses a wearable display to instruct the robot to move parts to a specific location.

[0703] 1. The worker turns their gaze towards the robot.

[0704] 2. The built-in camera captures the user's gaze and sends the gaze data to the server.

[0705] 3. The server analyzes the gaze data, identifies the robot's image, and acquires the image data.

[0706] 4. The generation AI model analyzes the video and generates text data that says, "Transport part A to line B."

[0707] 5. The speech synthesis engine converts this text data into speech data.

[0708] 6. The generated voice data is sent to the robot, and the robot acts according to the instructions.

[0709] Examples of prompts to input into a generative AI model

[0710] Video data:<video_stream>

[0711] Prompt: Analyze the lip movements in the video and output the spoken content as text data. For example, convert it to "Take part A to line B."

[0712] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0713] Step 1:

[0714] The user uses a wearable display and directs their gaze towards the robot. The device's built-in camera captures the user's gaze and transmits the data to a server in real time. Specifically, the Tobii Eye Tracker detects the user's eye movements and outputs gaze data. This gaze data indicates the direction and object the user is focusing on.

[0715] Step 2:

[0716] The server receives gaze data and performs data calculations based on that data to identify the image of the object the user is fixated on. The server acquires a video stream from a high-resolution camera and identifies the image of the object the user is fixated on. This video data includes the robot or work environment that the user is looking at. The identified video data is then stored on the server.

[0717] Step 3:

[0718] The server inputs the received video data into a generative AI model, which then performs lip-movement analysis. This generative AI model uses OpenAI technology to analyze lip movements in the video and convert the spoken content into text data. Specifically, it analyzes lip movements in the video data frame by frame and outputs the spoken content in text format in real time.

[0719] Step 4:

[0720] The generated text data is input into a speech synthesis system and converted into natural, fluent speech data on the server. Using the Python speech synthesis library pyttsx3, the generated text data is converted into an audio file. This audio data is in a format that can be understood by both users and machines.

[0721] Step 5:

[0722] The generated audio data is transmitted from the server to the terminal and provided to the user through the terminal's headset or speakers. By listening to this audio data, the user can accurately understand what the other person is saying, even in noisy environments. Specifically, the generated audio data is output from the headset and reaches the user's ears.

[0723] Step 6:

[0724] Furthermore, the generated voice data is transmitted from the server to the machine, and instructions are executed by automated means. The machine (for example, a Boston Dynamics robot) performs predetermined actions based on these voice instructions. Specifically, a robot that receives the voice instruction "Move part A to line B" will move the part to the designated location accordingly.

[0725] This allows users to give precise instructions to robots even in noisy environments, enabling efficient work.

[0726] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0727] The present invention is a system for facilitating communication in high-noise environments, and is comprised of a combination of eye-tracking means, video acquisition means, lip-movement analysis means, a generative AI model, speech synthesis means, speech output means, and an emotion engine. The embodiments for carrying out the invention will be described in detail below.

[0728] System Configuration

[0729] Eye-tracking methods

[0730] The device (smart glasses) uses a built-in camera to capture the user's eye movements and calculate their gaze direction. This data is transmitted to a server in real time.

[0731] Video acquisition method

[0732] The system uses eye-tracking technology to capture video of the object the user is focusing on using a high-resolution camera. The video data is transmitted to a server in real time and used for analysis.

[0733] Lip motion analysis means

[0734] The server receives video data transmitted from the terminal and analyzes the lip movements using a generation AI model. This generates text data of what the subject is saying.

[0735] Speech synthesis means

[0736] The generated text data is input into a speech synthesis engine and converted into natural, fluent speech data. This speech data is then adjusted for listening.

[0737] Audio output means

[0738] The generated audio data is sent to the terminal and provided to the user through a headset or speaker. This allows the user to accurately hear what the other person is saying, even in noisy environments.

[0739] Emotional Engine

[0740] This system also incorporates an emotion engine that analyzes the user's facial expressions and voice tone to recognize their emotions. This enables the system to provide feedback and responses tailored to the user's emotional state.

[0741] Program processing

[0742] The following describes, in natural language, how the system of the present invention works.

[0743] Eye Tracking

[0744] The device continuously captures the user's gaze using its built-in camera and calculates its direction. This gaze data is transmitted to the server in real time.

[0745] Video acquisition

[0746] The server receives gaze data and identifies the video of the object the user is fixated on based on that data. The identified video is acquired in high resolution and further analyzed.

[0747] Lip movement analysis

[0748] The acquired video data is input into an AI model, which generates text data from the lip movements to understand what is being said. This text data is temporarily stored in a buffer.

[0749] Speech synthesis

[0750] Text data is input into a speech synthesis engine and converted into natural-sounding speech data. The generated speech data is then stored back in a buffer.

[0751] Audio output

[0752] The audio data stored in the buffer is transmitted to the terminal and provided to the user through a headset or speaker. By listening to this, the user can understand what the other person is saying, even in noisy environments.

[0753] emotion recognition

[0754] The emotion engine analyzes the user's facial expressions and voice tone to recognize their emotional state. This information is sent to the server in real time, and appropriate feedback is provided as needed.

[0755] Specific example

[0756] For example, suppose User A is working in a noisy factory. In this situation, User B is giving User A visual instructions, but User A cannot hear the instructions due to the surrounding noise. The following is the processing flow for this situation.

[0757] 1. User A wears smart glasses and directs their gaze towards User B. The device's camera captures User A's gaze and sends the data to the server.

[0758] 2. The server receives the gaze data, identifies user B's video, and acquires the video in high resolution.

[0759] 3. The server uses a generated AI model to generate text data from the lip movements of user B, indicating what is being said.

[0760] 4. The server inputs the text data into the speech synthesis engine and converts it into natural-sounding speech data.

[0761] 5. The generated audio data is sent to the terminal and played back through User A's headset. This allows User A to accurately hear the instructions.

[0762] 6. Simultaneously, the emotion engine analyzes user A's facial expressions and voice tone to recognize their emotional state. If necessary, the server provides appropriate feedback.

[0763] Thus, the system of the present invention enables efficient communication even in noisy environments and can also respond according to the user's emotional state.

[0764] The following describes the processing flow.

[0765] Processing flow

[0766] Step 1:

[0767] The device captures the user's gaze.

[0768] A camera built into the device (smart glasses) continuously captures the user's eye movements.

[0769] The captured video data is processed in real time to calculate the user's gaze direction.

[0770] Step 2:

[0771] The device sends eye-tracking data to the server.

[0772] The calculated line-of-sight direction data is sent to the server at regular time intervals.

[0773] Eye-tracking data includes information such as the coordinates of the user's gaze point.

[0774] Step 3:

[0775] The server receives gaze data and acquires video footage of the object the user is looking at.

[0776] The server receives the gaze data transmitted from the terminal.

[0777] Based on the received gaze data, the system identifies the video area of ​​the object the user is fixated on.

[0778] Detailed video data of the identified video area is obtained from the device.

[0779] Step 4:

[0780] The server analyzes lip movements and generates text data.

[0781] The acquired video data is input into the AI ​​model on the server.

[0782] The generative AI model analyzes lip movements and converts what is being said into text data.

[0783] The generated text data is temporarily stored in a buffer.

[0784] Step 5:

[0785] The server inputs text data into the speech synthesis engine.

[0786] The text data stored in the buffer is input to the speech synthesis engine.

[0787] A speech synthesis engine converts input text data into natural-sounding speech data.

[0788] The generated audio data is stored back into the buffer.

[0789] Step 6:

[0790] The server sends the audio data to the terminal.

[0791] The audio data stored in the buffer is sent to the terminal as soon as it is ready for playback.

[0792] Step 7:

[0793] The device plays the audio data.

[0794] The device plays the received audio data through a headset or speaker.

[0795] The user listens to the generated audio through a headset or similar device.

[0796] Step 8:

[0797] The device acquires the user's facial expression data.

[0798] A camera built into the device (smart glasses) captures the user's facial expressions.

[0799] The captured facial expression data is sent to the server in real time.

[0800] Step 9:

[0801] The server analyzes the user's emotions.

[0802] The server receives facial expression data from the terminal and inputs it into the emotion engine.

[0803] The emotion engine analyzes facial expression data to identify the user's emotional state.

[0804] The analysis results are processed in real time, and feedback is provided as needed.

[0805] Specific example

[0806] For example, the following shows the processing flow when User A is working in a noisy factory and User B is giving instructions to User A, but User B cannot hear User A due to the surrounding noise.

[0807] Step 1:

[0808] User A wears smart glasses and directs their gaze towards User B. The device's built-in camera captures User A's eye movements and sends the data to the server.

[0809] Step 2:

[0810] The terminal sends calculated gaze data to the server. This includes coordinate information for the direction in which user A is looking.

[0811] Step 3:

[0812] The server receives gaze data and identifies user B's video area based on it. The identified video area is then retrieved from the terminal.

[0813] Step 4:

[0814] The server inputs the acquired video data into a generating AI model, which analyzes lip movements and converts them into text data. The generated text data is then temporarily stored in a buffer.

[0815] Step 5:

[0816] The server inputs the text data stored in the buffer into the speech synthesis engine and converts it into natural-sounding speech data. The generated speech data is then stored back into the buffer.

[0817] Step 6:

[0818] The server sends the generated audio data to the terminal.

[0819] Step 7:

[0820] The terminal plays the received audio data through the headset, and user A listens to the audio.

[0821] Step 8:

[0822] The device's built-in camera captures user A's facial expressions. The captured facial expression data is sent to the server in real time.

[0823] Step 9:

[0824] The server receives facial expression data and inputs it into the emotion engine. The emotion engine analyzes user A's facial expressions and recognizes their emotional state. The analysis results are processed in real time, and appropriate feedback is provided as needed.

[0825] This allows User A to accurately hear User B's instructions even in a noisy environment, and also enables responses tailored to User A's emotional state.

[0826] (Example 2)

[0827] Next, we will describe Example 2. 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".

[0828] In noisy environments, communication between users becomes difficult, hindering the accurate transmission of information. Furthermore, the inability to understand users' emotional states can lead to delays in responding appropriately to stressful or difficult situations. Solving these problems is essential.

[0829] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0830] In this invention, the server includes eye-tracking means, video acquisition means, lip-movement analysis means, speech synthesis means, speech output means, and emotion recognition means. This enables accurate information transmission between users even in high-noise environments, and further enables responses that correspond to the user's emotional state.

[0831] "Eye-tracking means" refers to a device or technology for detecting a user's gaze and determining its direction.

[0832] "Image acquisition means" refers to a device or technology for acquiring images of an object that a user is focusing on.

[0833] "Lip movement analysis means" refers to a device or technology for analyzing the movement of a subject's lips from video data and generating text data based on that movement.

[0834] "Speech synthesis means" refers to a device or technology for converting generated text data into speech data.

[0835] "Audio output means" refers to a device or technology for providing generated audio data to a user.

[0836] "Emotion recognition means" refers to a device or technology that analyzes a user's facial expressions and voice tone to recognize the user's emotional state.

[0837] A "head-mounted display device" is a device worn on the user's head that provides functions such as eye tracking and image acquisition.

[0838] An "image sensor" is an electronic component or technology used to capture images or videos.

[0839] A "generative model" is an algorithm or technique that generates new data from specific data based on AI technology.

[0840] The present invention is a system for facilitating communication in high-noise environments, and is comprised of a combination of eye-tracking means, video acquisition means, lip-movement analysis means, a generation AI model, speech synthesis means, speech output means, and emotion recognition means. The embodiments for carrying out the invention will be described in detail below.

[0841] System Configuration

[0842] Eye-tracking methods

[0843] The device (head-mounted display device) uses an image sensor to capture the user's eye movements and calculate their gaze direction. This data is transmitted to the server in real time.

[0844] Video acquisition method

[0845] The system uses eye-tracking technology to capture video of the object the user is focusing on using a high-resolution camera. The video data is transmitted to a server in real time and used for analysis.

[0846] Lip motion analysis means

[0847] The server receives video data transmitted from the terminal and analyzes the lip movements using a generation AI model. This generates text data of what the subject is saying.

[0848] Speech synthesis means

[0849] The generated text data is input into a speech synthesis engine and converted into natural, fluent speech data. This speech data is then adjusted for listening.

[0850] Audio output means

[0851] The generated audio data is sent to the terminal and provided to the user through a headset or speaker. This allows the user to accurately hear what the other person is saying, even in noisy environments.

[0852] emotion recognition means

[0853] This system also incorporates emotion recognition capabilities, analyzing the user's facial expressions and voice tone to recognize their emotions. This enables the system to provide feedback and responses tailored to the user's emotional state.

[0854] Specific example

[0855] For example, suppose User A is working in a noisy factory. In this situation, User B is giving User A visual instructions, but User A cannot hear the instructions due to the surrounding noise. The following is the processing flow for this situation.

[0856] 1. User A wears a head-mounted display device and directs their gaze towards User B. The terminal's image sensor captures User A's gaze and transmits the data to the server.

[0857] 2. The server receives the gaze data, identifies user B's video, and acquires the video in high resolution.

[0858] 3. The server uses a generated AI model to generate text data from the lip movements of user B, indicating what is being said.

[0859] 4. The server inputs the text data into the speech synthesis engine and converts it into natural-sounding speech data.

[0860] 5. The generated audio data is sent to the terminal and played back through User A's headset. This allows User A to accurately hear the instructions.

[0861] 6. Simultaneously, the emotion recognition system analyzes user A's facial expressions and voice tone to recognize their emotional state. If necessary, the server provides appropriate feedback.

[0862] Example of a prompt

[0863] The following are specific examples of prompt statements to be input to the generating AI model.

[0864] "Please explain the procedure for performing real-time lip-sync analysis on text content specified by User B and converting it into speech data."

[0865] "Please provide an example of how a system can support smooth communication between user A and user B in a high-noise environment."

[0866] "Please provide examples of feedback tailored to the user's emotional state."

[0867] Thus, the system of the present invention enables efficient communication even in high-noise environments and can also respond according to the user's emotional state.

[0868] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0869] Step 1:

[0870] Eye Tracking

[0871] Specific actions:

[0872] User: Wear the head-mounted display device and direct your gaze towards the object you wish to observe.

[0873] Terminal: An image sensor built into a head-mounted display device captures the user's eye movements.

[0874] Input: Eye movement data captured by the image sensor.

[0875] Data processing / calculation: Calculates the direction of gaze from eye movement data.

[0876] Output: Calculated line-of-sight direction data.

[0877] Terminal: Sends captured eye-tracking data to the server in real time.

[0878] Step 2:

[0879] Video acquisition

[0880] Specific actions:

[0881] Server: Receives gaze data and identifies the object in the direction the user is looking.

[0882] Device: Acquires video footage of the identified target using a high-resolution camera.

[0883] Input: Actual viewing direction data.

[0884] Data processing / calculation: Adjust the camera's field of view based on the direction of gaze and capture the video.

[0885] Output: Acquired video data.

[0886] Terminal: Transmits acquired video data to the server in real time.

[0887] Step 3:

[0888] Lip movement analysis

[0889] Specific actions:

[0890] Server: Receives video data sent from the terminal.

[0891] Input: Video data.

[0892] Data processing / calculation: Input video data into an AI model to analyze lip movements.

[0893] Output: Text data of words generated based on lip movements.

[0894] Server: Based on lip-movement analysis, it identifies fragments of spoken language and generates them as text data.

[0895] Step 4:

[0896] Text generation

[0897] Specific actions:

[0898] Server: Converts words identified from lip movements into text data.

[0899] Input: Lip movement analysis data.

[0900] Data processing / calculation: Based on the analyzed lip-sync data, text data is generated through natural language processing.

[0901] Output: Text data.

[0902] Server: Temporarily stores text data in a buffer.

[0903] Step 5:

[0904] Speech synthesis

[0905] Specific actions:

[0906] Server: Inputs text data into the speech synthesis engine.

[0907] Input: Text data.

[0908] Data processing / calculation: Perform a speech synthesis process to convert text data into natural-sounding speech data.

[0909] Output: Audio data.

[0910] Server: The speech synthesis engine converts text data into natural-sounding speech data.

[0911] Server: Stores the generated audio data in a buffer.

[0912] Step 6:

[0913] Audio output

[0914] Specific actions:

[0915] Server: Sends the audio data in the buffer to the terminal.

[0916] Input: Audio data in the buffer.

[0917] Data processing / calculation: Adjust data into a format that can be provided to users.

[0918] Output: Adjusted audio data.

[0919] Device: Plays audio data through a headset or speaker.

[0920] User: Listen to the audio coming from the headset and understand the instructions.

[0921] Step 7:

[0922] emotion recognition

[0923] Specific actions:

[0924] Device: The camera captures the user's facial expressions, and the microphone records their voice tone.

[0925] Input: Facial expression data and voice tone data.

[0926] Data processing / calculation: Facial expressions and voice tone are analyzed using an emotion recognition algorithm.

[0927] Output: Emotional state data.

[0928] Terminal: Sends data on facial expressions and voice tone to the server.

[0929] Server: The emotion engine analyzes facial expressions and voice tone to recognize the emotional state.

[0930] Server: Based on the recognized emotional state, it sends appropriate feedback and notifications to the device.

[0931] User: Receive and respond to the feedback provided.

[0932] (Application Example 2)

[0933] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0934] A major challenge is the difficulty of communication in high-noise environments. In such environments, verbal communication is hindered, leading to increased errors in instructions and guidance. Furthermore, accurately understanding and appropriately responding to users' emotional states becomes difficult. Therefore, a system is needed that enables effective communication and recognition of user emotions even in noisy environments.

[0935] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes eye-tracking means for tracking the user's gaze, image acquisition means for acquiring an image of an object the user is fixated on, lip movement analysis means for analyzing the movement of the object's lips from the image acquired by the image acquisition means and generating text data, speech synthesis means for converting the generated text data into speech, speech output means for providing the speech generated by the speech synthesis means to the user, and emotion recognition means for analyzing the user's facial expressions and voice tone to recognize their emotional state. This makes it possible to accurately transmit information visually and audibly even in high-noise environments, and furthermore, to provide appropriate feedback according to the user's emotional state.

[0936] "Eye-tracking means" refers to a method of tracking a user's gaze using a camera built into a wearable device and calculating its direction.

[0937] "Video acquisition means" refers to means of acquiring video footage of an object that a user is focusing on, as identified by eye-tracking means.

[0938] A "lip movement analysis means" is a means of analyzing the movement of a subject's lips from acquired video footage and converting the spoken content into text data.

[0939] A "speech synthesis method" is a means of converting generated text data into natural-sounding speech data.

[0940] "Audio output means" refers to means such as headsets or speakers for providing the generated audio data to the user.

[0941] An "emotion recognition method" is a means of recognizing a user's emotional state by analyzing their facial expressions and voice tone.

[0942] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to analyze data such as lip movements and generate text data and other output data.

[0943] The present invention is a system that enables smooth communication between users in high-noise environments. The main components are eye-tracking means, video acquisition means, lip-movement analysis means, generation AI model, speech synthesis means, speech output means, and emotion recognition means.

[0944] Eye-tracking methods

[0945] The device uses its built-in camera to track the user's gaze. This calculates the user's gaze direction, and this data is sent to the server in real time. To achieve this functionality, an eye-tracking algorithm (e.g., Tobii Pro SDK) is used.

[0946] Video acquisition method

[0947] The server receives data transmitted from the eye-tracking device and identifies and acquires the video of the object the user is fixated on. The identified video is captured by a high-resolution camera (e.g., OpenCV) and transmitted as data for analysis.

[0948] Lip motion analysis means

[0949] The server uses a generative AI model to analyze the video acquired by the video acquisition device. This generative AI model has the ability to analyze the movement of the subject's lips and convert it into text data (e.g., OpenAI GPT, DALL-E). An example of a prompt message is: "Analyze lip movements from video taken in a high-noise environment and convert what is being said into text data. Video data: {video data} Response format: text format."

[0950] Speech synthesis means

[0951] To convert the generated text data into speech, the server uses a speech synthesis engine (e.g., Google Text-to-Speech API, IBM Watson Text to Speech). This produces natural-sounding speech data.

[0952] Audio output means

[0953] The generated audio data is transmitted to the terminal in real time and output through the user's headset or speakers. This allows the user to accurately hear what the other person is saying, even in noisy environments.

[0954] emotion recognition means

[0955] Furthermore, the emotion recognition system analyzes the user's facial expressions and voice tone. An emotion analysis engine (e.g., Microsoft Azure Emotion API) is used to recognize the user's emotional state. This information is transmitted to the server in real time, providing appropriate feedback based on the user's emotional state.

[0956] Specific example

[0957] For example, this system is useful when a user operating heavy machinery in a factory needs to exchange visual and auditory information with a colleague at a distance. The user identifies the colleague by their gaze and analyzes their lip movements, converting them into text data. This text data is then converted into speech and output through a headset, ensuring clear communication of instructions even in noisy environments. Furthermore, the system recognizes the user's emotional state in real time, providing appropriate feedback.

[0958] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0959] Step 1:

[0960] The device uses its built-in camera to continuously capture the user's eye movements and calculate their gaze direction. The input is video data of the user's eyes, and the output is gaze direction data. This gaze direction data is sent to the server in real time. Specifically, the gaze direction is determined using an eye-tracking algorithm (e.g., Tobii Pro SDK).

[0961] Step 2:

[0962] The server receives the transmitted gaze direction data and identifies the object the user is looking at based on that data. The input is gaze direction data, and the output is the coordinate information of the object being gazed at. Based on this information, a high-resolution camera is used to acquire video of the object being gazed at. Specifically, a video processing library (e.g., OpenCV) is used to capture the video data.

[0963] Step 3:

[0964] The server inputs video data acquired by a high-resolution camera into a generative AI model to analyze the movement of the subject's lips. The input is video data, and the output is the analyzed text data. As a specific example, a prompt message using a generative AI model (e.g., OpenAI GPT, DALL-E) would be: "Analyze lip movements from video footage taken in a high-noise environment and convert what is being said into text data. Video data: {video data} Response format: text format."

[0965] Step 4:

[0966] The server inputs text data generated by lip-sync analysis into a speech synthesis engine and converts it into natural-sounding speech data. The input is text data, and the output is speech data. Specifically, it utilizes a speech synthesis engine (e.g., Google Text-to-Speech API, IBM Watson Text to Speech).

[0967] Step 5:

[0968] The server sends the generated audio data to the terminal, which then provides it to the user through a headset or speaker. The input is the audio data, and the output is the audio provided to the user. Specifically, the process involves outputting the audio data through the terminal's headset or speaker.

[0969] Step 6:

[0970] The server uses an emotion analysis engine to analyze the user's facial expressions and voice tone to recognize their emotional state. The input is the user's facial expression data and voice tone data, and the output is the recognized emotional state data. Specifically, it performs real-time analysis using an emotion analysis engine (e.g., Microsoft Azure Emotion API). This emotional state data is sent to the server, and appropriate feedback is provided as needed.

[0971] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0972] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0973] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0974] [Third Embodiment]

[0975] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0976] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0977] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0978] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0979] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0980] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0981] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0982] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0983] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0984] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0985] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0986] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0987] The present invention is a system for facilitating communication in high-noise environments, and is composed of a combination of eye-tracking means, video acquisition means, lip-movement analysis means, generation AI model, speech synthesis means, and speech output means. The embodiments for carrying out the invention will be described in detail below.

[0988] System Configuration

[0989] Eye-tracking methods

[0990] The device (smart glasses) is equipped with a built-in camera to capture the user's eye movements. This camera can track the user's gaze and execute an algorithm to calculate its direction.

[0991] Video acquisition method

[0992] The device identifies the object the user is looking at through eye-tracking technology and acquires video footage of that object using a high-resolution camera. This video data is transmitted to the server in real time.

[0993] Lip motion analysis means

[0994] The server receives video data transmitted from the terminal and inputs it into a generative AI model used for lip movement analysis. This generative AI model analyzes the movement of the other person's lips from the acquired video and generates text data of what is being said.

[0995] Speech synthesis means

[0996] The generated text data is input into a speech synthesis system on the server and converted into natural, fluent speech data. This speech data is then adjusted to a quality that users can listen to without any discomfort.

[0997] Audio output means

[0998] Finally, the generated audio data is sent to a device (smart glasses) and then provided to the user through a headset or speaker. This allows the user to accurately hear what the other person is saying, even in noisy environments.

[0999] Program processing

[1000] The following describes, in natural language, how the system of the present invention works.

[1001] Eye Tracking

[1002] The device continuously captures the user's gaze using its built-in camera and calculates the direction of their gaze. The calculated gaze data is sent to the server in real time.

[1003] Video acquisition

[1004] The server receives gaze data and uses it to identify the video object the user is fixated on. The identified video is then acquired for further detailed analysis.

[1005] Lip movement analysis

[1006] The acquired video is sent to a server, where a generative AI model analyzes the lip movements to determine what is being said and generates it as text data. This text data is temporarily stored in a buffer.

[1007] Speech synthesis

[1008] The text data stored in the buffer is input to the speech synthesis engine and converted into natural-sounding speech data. This speech data is then stored back into the buffer.

[1009] Audio output

[1010] Finally, the generated audio data is sent to the terminal and provided to the user through a headset or speaker, allowing the user to accurately hear what the other person is saying even in noisy environments.

[1011] Specific example

[1012] For example, suppose User A is working in a noisy factory while wearing smart glasses. In this scenario, User B is giving instructions to User A, but User A cannot hear them due to the surrounding noise.

[1013] 1. User A turns their gaze towards User B.

[1014] 2. The device's built-in camera captures user A's gaze, and the gaze data is sent to the server.

[1015] 3. The server receives the gaze data, identifies user B's video, and retrieves the video data.

[1016] 4. The server uses a generative AI model to convert what user B is saying from the movement of their lips into text data.

[1017] 5. The server inputs the text data into the speech synthesis engine and generates the speech data.

[1018] 6. The generated audio data is sent to the terminal and played back through User A's headset.

[1019] This allows User A to accurately hear User B's instructions even in a noisy environment, enabling them to proceed with the work smoothly.

[1020] Thus, the system of the present invention enables efficient communication in a variety of high-noise environments.

[1021] The following describes the processing flow.

[1022] Step 1:

[1023] The device captures the user's gaze.

[1024] The camera built into the device continuously captures the user's eye movements.

[1025] The system processes video data from the camera in real time and executes an eye-tracking algorithm to calculate the user's gaze direction.

[1026] Step 2:

[1027] The device sends eye-tracking data to the server.

[1028] The calculated line-of-sight direction data is sent to the server at regular time intervals.

[1029] Eye-tracking data includes information such as the coordinates of the user's gaze point.

[1030] Step 3:

[1031] The server receives gaze data and acquires video footage of the object the user is looking at.

[1032] The server receives the gaze data transmitted from the terminal.

[1033] Based on the received gaze data, the system identifies the video area of ​​the object the user is fixated on.

[1034] Detailed video data of the identified video area is obtained from the device.

[1035] Step 4:

[1036] The server analyzes lip movements and generates text data.

[1037] The acquired video data is input into the AI ​​model on the server.

[1038] The generative AI model analyzes lip movements and converts what is being said into text data.

[1039] The generated text data is temporarily stored in a buffer.

[1040] Step 5:

[1041] The server inputs text data into the speech synthesis engine.

[1042] The text data stored in the buffer is input to the speech synthesis engine.

[1043] A speech synthesis engine converts input text data into natural-sounding speech data.

[1044] The generated audio data is stored back into the buffer.

[1045] Step 6:

[1046] The server sends the audio data to the terminal.

[1047] The audio data stored in the buffer is sent to the terminal as soon as it is ready for playback.

[1048] Step 7:

[1049] The device plays the audio data.

[1050] The device plays the received audio data through a headset or speaker.

[1051] The user listens to the generated audio through a headset or similar device.

[1052] Specific example

[1053] For example, the following shows the processing flow when User B gives visual instructions to User A while User A is working in a noisy factory.

[1054] Step 1:

[1055] User A wears smart glasses and directs their gaze towards User B. The camera in the device captures User A's eye movements and calculates the direction of their gaze.

[1056] Step 2:

[1057] The terminal sends calculated gaze data to the server. This includes coordinate information for the direction in which user A is looking.

[1058] Step 3:

[1059] The server receives gaze data and identifies user B's video area based on it. The identified video area is then retrieved from the terminal.

[1060] Step 4:

[1061] The server inputs the acquired video data into a generating AI model, which analyzes lip movements and converts them into text data. The generated text data is then temporarily stored in a buffer.

[1062] Step 5:

[1063] The server inputs the text data stored in the buffer into the speech synthesis engine and converts it into natural-sounding speech data. The generated speech data is then stored back into the buffer.

[1064] Step 6:

[1065] The server sends the generated audio data to the terminal.

[1066] Step 7:

[1067] The terminal plays the received audio data through the headset, and user A listens to the audio.

[1068] This allows user A to accurately receive instructions from user B even in a noisy environment, enabling them to carry out their work efficiently.

[1069] (Example 1)

[1070] Next, we will describe Example 1. 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."

[1071] Communication in high-noise environments presents a significant challenge due to the difficulty in hearing voices. This is particularly problematic in places requiring concentration, such as factories and construction sites, where important instructions may be difficult to convey, potentially negatively impacting work efficiency and safety. The present invention aims to provide a means for effective communication even in such high-noise environments.

[1072] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1073] In this invention, the server includes eye-tracking means for tracking the user's gaze, image acquisition means for acquiring images of an object the user is fixated on, lip-movement analysis means for analyzing the movement of the object's lips from the images acquired by the image acquisition means and generating text data, speech synthesis means for converting the generated text data into speech, and speech output means for providing the user with the speech generated by the speech synthesis means. This makes it possible for the user to accurately hear what the other person is saying, even in a high-noise environment.

[1074] "Eye-tracking means" refers to devices or algorithms that capture the movement of a user's eyes and calculate the direction of their gaze.

[1075] "Image acquisition means" refers to devices or technologies for acquiring images of objects that a user is focusing on.

[1076] A "lip movement analysis means" refers to a device or generation AI model that analyzes the movement of a subject's lips from acquired video footage and converts the spoken content into text data.

[1077] "Speech synthesis means" refers to a device or engine that converts generated text data into natural-sounding speech data.

[1078] "Audio output means" refers to a device for providing the user with audio generated by a speech synthesis means, and includes headsets and speakers.

[1079] A "wearable device" is an electronic device that a user can wear and use.

[1080] A "machine learning model" is an algorithm that learns patterns from data and uses them to make predictions and classifications.

[1081] The present invention is a system for facilitating communication in high-noise environments, and is comprised of a combination of eye-tracking means, video acquisition means, lip-movement analysis means, a generation AI model, speech synthesis means, and speech output means. The following describes in detail the embodiments for implementing this system.

[1082] System Configuration

[1083] Eye-tracking methods

[1084] The device (wearable device) is equipped with a built-in camera to capture the user's eye movements. This camera can track the user's gaze and execute an algorithm to calculate its direction. Specifically, a face detection algorithm using Haar-like features is applied.

[1085] Video acquisition method

[1086] The device identifies the object the user is looking at through eye-tracking technology and captures video of that object using a high-resolution camera. This camera has a 1080p resolution and captures video in real time. The acquired video data is compressed and transmitted to a server via Wi-Fi.

[1087] Lip motion analysis means

[1088] The server receives video data sent from the terminal and inputs it into a generative AI model. This generative AI model is a TensorFlow-based RNN model that analyzes the lip movements of the other party from the acquired video and generates text data of what is being said. This text data is temporarily stored in a Redis buffer.

[1089] Speech synthesis means

[1090] The text data stored in the buffer is input to a speech synthesis engine on the server (e.g., Google Text-to-Speech API). The speech synthesis engine converts the text data into natural, fluent speech data. This speech data is also stored in the buffer.

[1091] Audio output means

[1092] Finally, the generated audio data is sent to the device and delivered to the user through a headset or speaker (e.g., Bluetooth earphones). This allows the user to accurately hear what the other person is saying, even in noisy environments.

[1093] Specific example

[1094] For example, suppose User A is working in a noisy factory while wearing smart glasses. In this scenario, User B is giving instructions to User A, but User A cannot hear them due to the surrounding noise.

[1095] 1. User A turns their gaze towards User B.

[1096] 2. The device's built-in camera captures user A's gaze, and the gaze data is sent to the server.

[1097] 3. The server receives the gaze data, identifies user B's video, and retrieves the video data.

[1098] 4. The server uses a generative AI model to convert what user B is saying from the movement of their lips into text data.

[1099] 5. The server inputs the text data into the speech synthesis engine and generates the speech data.

[1100] 6. The generated audio data is sent to the terminal and played back through User A's headset.

[1101] This allows User A to accurately hear User B's instructions even in a noisy environment, enabling them to proceed with the work smoothly.

[1102] Example of a prompt

[1103] "User A wears smart glasses in the factory, identifies User B's image through their gaze, analyzes what they are saying from their lip movements, and plays it back as audio."

[1104] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1105] Step 1:

[1106] The device uses its built-in camera to capture the user's gaze. The captured gaze data is processed at a rate of 60 frames per second and input into an algorithm that calculates the direction of the gaze (for example, a face detection algorithm using Haar-like features). The calculated gaze data is sent to a server via Wi-Fi.

[1107] Input: User eye-tracking data

[1108] Data processing: Calculation of line of sight

[1109] Output: Eye-tracking data

[1110] Specific operation: When the user focuses on something, the device's built-in camera captures this action, detects the direction of their gaze in real time, and sends it to the server.

[1111] Step 2:

[1112] The server receives the gaze data and identifies the object the user is looking at. To obtain video of the identified object, the device's high-resolution camera is activated and captures video in the specified direction. The captured video data is compressed and sent to the server in real time.

[1113] Input: Eye-tracking data

[1114] Data processing: Identifying and capturing video of objects the user is focusing on.

[1115] Output: Video data

[1116] Specific operation: The server analyzes the gaze data to identify the object the user is looking at. The terminal's high-resolution camera captures a specified area and sends the video data to the server.

[1117] Step 3:

[1118] The server inputs the received video data into a generative AI model. This generative AI model analyzes the changes between video frames and recognizes lip movements. Based on the recognition results, it generates text data of what is being said. This text data is temporarily stored in a Redis buffer.

[1119] Input: Video data

[1120] Data processing: Recognition of lip movements and conversion to text.

[1121] Output: Text data

[1122] Specific operation: Video data is input into an AI model that generates text data by analyzing lip movements.

[1123] Step 4:

[1124] The server inputs the text data stored in the buffer into a speech synthesis engine. This speech synthesis engine (for example, the Google Text-to-Speech API) converts the text data into natural, fluent speech data. This converted speech data is also stored back into the buffer.

[1125] Input: Text data

[1126] Data processing: Converting text data to audio data

[1127] Output: Audio data

[1128] Specific operation: The server inputs text data into the speech synthesis engine and stores the generated audio file in a buffer.

[1129] Step 5:

[1130] The server sends the audio data from the buffer to the terminal. The terminal decodes the received audio data and plays it back to the user through a headset or speaker (e.g., Bluetooth earphones).

[1131] Input: Audio data

[1132] Data processing: Sending and playing back audio data.

[1133] Output: Audio to be played

[1134] Specific operation: The server sends audio data to the terminal, and the terminal plays it back, allowing the server to hear what the user is saying.

[1135] (Application Example 1)

[1136] Next, we will explain Application Example 1. In the following explanation, 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."

[1137] Conventional systems presented challenges in human-machine communication within noisy factory environments, hindering smooth work instructions and information exchange. Furthermore, despite advancements in eye-tracking and lip-syncing technologies, a reliable method for accurately and quickly sending instructions to machines using these techniques remained unestablished. This resulted in decreased work efficiency and an increased risk of operational errors.

[1138] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1139] In this invention, the server is

[1140] A means of tracking the user's gaze,

[1141] A means for acquiring video footage of an object that the user is focusing on,

[1142] A lip motion analysis means analyzes the movement of the target's lips from video acquired by a video acquisition means and generates text data,

[1143] A speech synthesis means that converts generated text data into speech,

[1144] A voice output means that provides the user with voice generated by a voice synthesis means,

[1145] An automated means that transmits generated voice data to a machine and executes instructions,

[1146] This includes enabling accurate and efficient instruction transmission from the user to the machine, even in high-noise environments.

[1147] "Eye-tracking means" refers to a device or system that captures the movement of a user's eyes and calculates its direction.

[1148] "Image acquisition means" refers to a device or system for acquiring images of an object that the user is focusing on.

[1149] A "lip movement analysis means" is a device or system that analyzes the movement of a subject's lips from acquired video footage and generates text data.

[1150] "Speech synthesis means" refers to a device or system that converts generated text data into speech.

[1151] "Speech output means" refers to a device or system that provides the user with speech generated by speech synthesis means.

[1152] "Automation means" refers to a device or system for transmitting generated voice data to a machine and executing instructions.

[1153] A "wearable display" is a general term for a device that a user can wear.

[1154] A "generative AI model" is an artificial intelligence model used to generate specific information (in this case, the content of speech based on lip movements) from video data.

[1155] Modes for carrying out the invention

[1156] The present invention is a system for facilitating communication between a user and a machine in a high-noise environment, and is composed of a combination of eye-tracking means, video acquisition means, lip-movement analysis means, a generation AI model, speech synthesis means, speech output means, and automation means. The embodiments for carrying out the present invention will be described in detail below.

[1157] System Configuration

[1158] Eye-tracking methods

[1159] The device (wearable display) is equipped with a built-in camera to capture the user's eye movements. This camera can track the user's gaze and execute algorithms to calculate its direction. For example, Tobii Eye Tracker is used.

[1160] Video acquisition method

[1161] The device identifies the object the user is looking at through eye-tracking technology and acquires video footage of that object using a high-resolution camera. This video data is transmitted to the server in real time.

[1162] Lip motion analysis means

[1163] The server receives video data transmitted from the terminal and inputs it into a generative AI model used for lip movement analysis. This generative AI model, such as one from OpenAI, analyzes the movement of the other person's lips from the acquired video and generates text data of what is being said.

[1164] Speech synthesis means

[1165] The generated text data is input into a speech synthesis system on the server and converted into natural, fluent speech data. For example, the Python speech synthesis library pyttsx3 is used. This speech data is then adjusted to a quality that can be heard without discomfort by a user or machine.

[1166] Audio output means

[1167] Finally, the generated audio data is transmitted to a device (wearable display) and then provided to the user through a headset or speaker. This allows the user to accurately hear what the other person is saying, even in noisy environments.

[1168] automated means

[1169] Furthermore, the generated voice data is transmitted to a machine by automated means. The machine then performs predetermined actions based on the voice instructions.

[1170] Program processing

[1171] Each of the above-described mechanisms of the system is implemented using the following hardware and software.

[1172] Eye tracking method: Wearable display with built-in Tobii Eye Tracker

[1173] Video acquisition method: High-resolution camera, OpenCV

[1174] Lip movement analysis method: Generative AI model (OpenAI)

[1175] Speech synthesis method: pyttsx3

[1176] Audio output method: Headset or speaker

[1177] Automation methods: Machines (e.g., Boston Dynamics robots)

[1178] Specific example

[1179] For example, suppose a robot is used to transport parts within a factory. In a high-noise environment, communication using normal voice commands is difficult. In such a situation, a worker uses a wearable display to instruct the robot to move parts to a specific location.

[1180] 1. The worker turns their gaze towards the robot.

[1181] 2. The built-in camera captures the user's gaze and sends the gaze data to the server.

[1182] 3. The server analyzes the gaze data, identifies the robot's image, and acquires the image data.

[1183] 4. The generation AI model analyzes the video and generates text data that says, "Transport part A to line B."

[1184] 5. The speech synthesis engine converts this text data into speech data.

[1185] 6. The generated voice data is sent to the robot, and the robot acts according to the instructions.

[1186] Examples of prompts to input into a generative AI model

[1187] Video data:<video_stream>

[1188] Prompt: Analyze the lip movements in the video and output the spoken content as text data. For example, convert it to "Take part A to line B."

[1189] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1190] Step 1:

[1191] The user uses a wearable display and directs their gaze towards the robot. The device's built-in camera captures the user's gaze and transmits the data to a server in real time. Specifically, the Tobii Eye Tracker detects the user's eye movements and outputs gaze data. This gaze data indicates the direction and object the user is focusing on.

[1192] Step 2:

[1193] The server receives gaze data and performs data calculations based on that data to identify the image of the object the user is fixated on. The server acquires a video stream from a high-resolution camera and identifies the image of the object the user is fixated on. This video data includes the robot or work environment that the user is looking at. The identified video data is then stored on the server.

[1194] Step 3:

[1195] The server inputs the received video data into a generative AI model, which then performs lip-movement analysis. This generative AI model uses OpenAI technology to analyze lip movements in the video and convert the spoken content into text data. Specifically, it analyzes lip movements in the video data frame by frame and outputs the spoken content in text format in real time.

[1196] Step 4:

[1197] The generated text data is input into a speech synthesis system and converted into natural, fluent speech data on the server. Using the Python speech synthesis library pyttsx3, the generated text data is converted into an audio file. This audio data is in a format that can be understood by both users and machines.

[1198] Step 5:

[1199] The generated audio data is transmitted from the server to the terminal and provided to the user through the terminal's headset or speakers. By listening to this audio data, the user can accurately understand what the other person is saying, even in noisy environments. Specifically, the generated audio data is output from the headset and reaches the user's ears.

[1200] Step 6:

[1201] Furthermore, the generated voice data is transmitted from the server to the machine, and instructions are executed by automated means. The machine (for example, a Boston Dynamics robot) performs predetermined actions based on these voice instructions. Specifically, a robot that receives the voice instruction "Move part A to line B" will move the part to the designated location accordingly.

[1202] This allows users to give precise instructions to robots even in noisy environments, enabling efficient work.

[1203] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1204] The present invention is a system for facilitating communication in high-noise environments, and is comprised of a combination of eye-tracking means, video acquisition means, lip-movement analysis means, a generative AI model, speech synthesis means, speech output means, and an emotion engine. The embodiments for carrying out the invention will be described in detail below.

[1205] System Configuration

[1206] Eye-tracking methods

[1207] The device (smart glasses) uses a built-in camera to capture the user's eye movements and calculate their gaze direction. This data is transmitted to a server in real time.

[1208] Video acquisition method

[1209] The system uses eye-tracking technology to capture video of the object the user is focusing on using a high-resolution camera. The video data is transmitted to a server in real time and used for analysis.

[1210] Lip motion analysis means

[1211] The server receives video data transmitted from the terminal and analyzes the lip movements using a generation AI model. This generates text data of what the subject is saying.

[1212] Speech synthesis means

[1213] The generated text data is input into a speech synthesis engine and converted into natural, fluent speech data. This speech data is then adjusted for listening.

[1214] Audio output means

[1215] The generated audio data is sent to the terminal and provided to the user through a headset or speaker. This allows the user to accurately hear what the other person is saying, even in noisy environments.

[1216] Emotional Engine

[1217] This system also incorporates an emotion engine that analyzes the user's facial expressions and voice tone to recognize their emotions. This enables the system to provide feedback and responses tailored to the user's emotional state.

[1218] Program processing

[1219] The following describes, in natural language, how the system of the present invention works.

[1220] Eye Tracking

[1221] The device continuously captures the user's gaze using its built-in camera and calculates its direction. This gaze data is transmitted to the server in real time.

[1222] Video acquisition

[1223] The server receives gaze data and identifies the video of the object the user is fixated on based on that data. The identified video is acquired in high resolution and further analyzed.

[1224] Lip movement analysis

[1225] The acquired video data is input into an AI model, which generates text data from the lip movements to understand what is being said. This text data is temporarily stored in a buffer.

[1226] Speech synthesis

[1227] Text data is input into a speech synthesis engine and converted into natural-sounding speech data. The generated speech data is then stored back in a buffer.

[1228] Audio output

[1229] The audio data stored in the buffer is transmitted to the terminal and provided to the user through a headset or speaker. By listening to this, the user can understand what the other person is saying, even in noisy environments.

[1230] emotion recognition

[1231] The emotion engine analyzes the user's facial expressions and voice tone to recognize their emotional state. This information is sent to the server in real time, and appropriate feedback is provided as needed.

[1232] Specific example

[1233] For example, suppose User A is working in a noisy factory. In this situation, User B is giving User A visual instructions, but User A cannot hear the instructions due to the surrounding noise. The following is the processing flow for this situation.

[1234] 1. User A wears smart glasses and directs their gaze towards User B. The device's camera captures User A's gaze and sends the data to the server.

[1235] 2. The server receives the gaze data, identifies user B's video, and acquires the video in high resolution.

[1236] 3. The server uses a generated AI model to generate text data from the lip movements of user B, indicating what is being said.

[1237] 4. The server inputs the text data into the speech synthesis engine and converts it into natural-sounding speech data.

[1238] 5. The generated audio data is sent to the terminal and played back through User A's headset. This allows User A to accurately hear the instructions.

[1239] 6. Simultaneously, the emotion engine analyzes user A's facial expressions and voice tone to recognize their emotional state. If necessary, the server provides appropriate feedback.

[1240] Thus, the system of the present invention enables efficient communication even in noisy environments and can also respond according to the user's emotional state.

[1241] The following describes the processing flow.

[1242] Processing flow

[1243] Step 1:

[1244] The device captures the user's gaze.

[1245] A camera built into the device (smart glasses) continuously captures the user's eye movements.

[1246] The captured video data is processed in real time to calculate the user's gaze direction.

[1247] Step 2:

[1248] The device sends eye-tracking data to the server.

[1249] The calculated line-of-sight direction data is sent to the server at regular time intervals.

[1250] Eye-tracking data includes information such as the coordinates of the user's gaze point.

[1251] Step 3:

[1252] The server receives gaze data and acquires video footage of the object the user is looking at.

[1253] The server receives the gaze data transmitted from the terminal.

[1254] Based on the received gaze data, the system identifies the video area of ​​the object the user is fixated on.

[1255] Detailed video data of the identified video area is obtained from the device.

[1256] Step 4:

[1257] The server analyzes lip movements and generates text data.

[1258] The acquired video data is input into the AI ​​model on the server.

[1259] The generative AI model analyzes lip movements and converts what is being said into text data.

[1260] The generated text data is temporarily stored in a buffer.

[1261] Step 5:

[1262] The server inputs text data into the speech synthesis engine.

[1263] The text data stored in the buffer is input to the speech synthesis engine.

[1264] A speech synthesis engine converts input text data into natural-sounding speech data.

[1265] The generated audio data is stored back into the buffer.

[1266] Step 6:

[1267] The server sends the audio data to the terminal.

[1268] The audio data stored in the buffer is sent to the terminal as soon as it is ready for playback.

[1269] Step 7:

[1270] The device plays the audio data.

[1271] The device plays the received audio data through a headset or speaker.

[1272] The user listens to the generated audio through a headset or similar device.

[1273] Step 8:

[1274] The device acquires the user's facial expression data.

[1275] The camera built into the device (smart glasses) captures the user's facial expressions.

[1276] The captured facial expression data is sent to the server in real time.

[1277] Step 9:

[1278] The server analyzes the user's emotions.

[1279] The server receives facial expression data from the terminal and inputs it into the emotion engine.

[1280] The emotion engine analyzes facial expression data to identify the user's emotional state.

[1281] The analysis results are processed in real time, and feedback is provided as needed.

[1282] Specific example

[1283] For example, the following shows the processing flow when User A is working in a noisy factory and User B is giving instructions to User A, but User B cannot hear User A due to the surrounding noise.

[1284] Step 1:

[1285] User A wears smart glasses and directs their gaze towards User B. The device's built-in camera captures User A's eye movements and sends the data to the server.

[1286] Step 2:

[1287] The terminal sends calculated gaze data to the server. This includes coordinate information for the direction in which user A is looking.

[1288] Step 3:

[1289] The server receives gaze data and identifies user B's video area based on it. The identified video area is then retrieved from the terminal.

[1290] Step 4:

[1291] The server inputs the acquired video data into a generating AI model, which analyzes lip movements and converts them into text data. The generated text data is then temporarily stored in a buffer.

[1292] Step 5:

[1293] The server inputs the text data stored in the buffer into the speech synthesis engine and converts it into natural-sounding speech data. The generated speech data is then stored back into the buffer.

[1294] Step 6:

[1295] The server sends the generated audio data to the terminal.

[1296] Step 7:

[1297] The terminal plays the received audio data through the headset, and user A listens to the audio.

[1298] Step 8:

[1299] The device's built-in camera captures user A's facial expressions. The captured facial expression data is sent to the server in real time.

[1300] Step 9:

[1301] The server receives facial expression data and inputs it into the emotion engine. The emotion engine analyzes user A's facial expressions and recognizes their emotional state. The analysis results are processed in real time, and appropriate feedback is provided as needed.

[1302] This allows User A to accurately hear User B's instructions even in a noisy environment, and also enables responses tailored to User A's emotional state.

[1303] (Example 2)

[1304] Next, we will describe Example 2. 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."

[1305] In noisy environments, communication between users becomes difficult, hindering the accurate transmission of information. Furthermore, the inability to understand users' emotional states can lead to delays in responding appropriately to stressful or difficult situations. Solving these problems is essential.

[1306] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1307] In this invention, the server includes eye-tracking means, video acquisition means, lip-movement analysis means, speech synthesis means, speech output means, and emotion recognition means. This enables accurate information transmission between users even in high-noise environments, and further enables responses that correspond to the user's emotional state.

[1308] "Eye-tracking means" refers to a device or technology for detecting a user's gaze and determining its direction.

[1309] "Image acquisition means" refers to a device or technology for acquiring images of an object that a user is focusing on.

[1310] "Lip movement analysis means" refers to a device or technology for analyzing the movement of a subject's lips from video data and generating text data based on that movement.

[1311] "Speech synthesis means" refers to a device or technology for converting generated text data into speech data.

[1312] "Audio output means" refers to a device or technology for providing generated audio data to a user.

[1313] "Emotion recognition means" refers to a device or technology that analyzes a user's facial expressions and voice tone to recognize the user's emotional state.

[1314] A "head-mounted display device" is a device worn on the user's head that provides functions such as eye tracking and image acquisition.

[1315] An "image sensor" is an electronic component or technology used to capture images or videos.

[1316] A "generative model" is an algorithm or technique that generates new data from specific data based on AI technology.

[1317] The present invention is a system for facilitating communication in high-noise environments, and is comprised of a combination of eye-tracking means, video acquisition means, lip-movement analysis means, a generation AI model, speech synthesis means, speech output means, and emotion recognition means. The embodiments for carrying out the invention will be described in detail below.

[1318] System Configuration

[1319] Eye-tracking methods

[1320] The device (head-mounted display device) uses an image sensor to capture the user's eye movements and calculate their gaze direction. This data is transmitted to the server in real time.

[1321] Video acquisition method

[1322] The system uses eye-tracking technology to capture video of the object the user is focusing on using a high-resolution camera. The video data is transmitted to a server in real time and used for analysis.

[1323] Lip motion analysis means

[1324] The server receives video data transmitted from the terminal and analyzes the lip movements using a generation AI model. This generates text data of what the subject is saying.

[1325] Speech synthesis means

[1326] The generated text data is input into a speech synthesis engine and converted into natural, fluent speech data. This speech data is then adjusted for listening.

[1327] Audio output means

[1328] The generated audio data is sent to the terminal and provided to the user through a headset or speaker. This allows the user to accurately hear what the other person is saying, even in noisy environments.

[1329] emotion recognition means

[1330] This system also incorporates emotion recognition capabilities, analyzing the user's facial expressions and voice tone to recognize their emotions. This enables the system to provide feedback and responses tailored to the user's emotional state.

[1331] Specific example

[1332] For example, suppose User A is working in a noisy factory. In this situation, User B is giving User A visual instructions, but User A cannot hear the instructions due to the surrounding noise. The following is the processing flow for this situation.

[1333] 1. User A wears a head-mounted display device and directs their gaze towards User B. The terminal's image sensor captures User A's gaze and transmits the data to the server.

[1334] 2. The server receives the gaze data, identifies user B's video, and acquires the video in high resolution.

[1335] 3. The server uses a generated AI model to generate text data from the lip movements of user B, indicating what is being said.

[1336] 4. The server inputs the text data into the speech synthesis engine and converts it into natural-sounding speech data.

[1337] 5. The generated audio data is sent to the terminal and played back through User A's headset. This allows User A to accurately hear the instructions.

[1338] 6. Simultaneously, the emotion recognition system analyzes user A's facial expressions and voice tone to recognize their emotional state. If necessary, the server provides appropriate feedback.

[1339] Example of a prompt

[1340] The following are specific examples of prompt statements to be input to the generating AI model.

[1341] "Please explain the procedure for performing real-time lip-sync analysis on text content specified by User B and converting it into speech data."

[1342] "Please provide an example of how a system can support smooth communication between user A and user B in a high-noise environment."

[1343] "Please provide examples of feedback tailored to the user's emotional state."

[1344] Thus, the system of the present invention enables efficient communication even in high-noise environments and can also respond according to the user's emotional state.

[1345] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1346] Step 1:

[1347] Eye Tracking

[1348] Specific actions:

[1349] User: Wear the head-mounted display device and direct your gaze towards the object you wish to observe.

[1350] Terminal: An image sensor built into a head-mounted display device captures the user's eye movements.

[1351] Input: Eye movement data captured by the image sensor.

[1352] Data processing / calculation: Calculates the direction of gaze from eye movement data.

[1353] Output: Calculated line-of-sight direction data.

[1354] Terminal: Sends captured eye-tracking data to the server in real time.

[1355] Step 2:

[1356] Video acquisition

[1357] Specific actions:

[1358] Server: Receives gaze data and identifies the object in the direction the user is looking.

[1359] Device: Acquires video footage of the identified target using a high-resolution camera.

[1360] Input: Actual viewing direction data.

[1361] Data processing / calculation: Adjust the camera's field of view based on the direction of gaze and capture the video.

[1362] Output: Acquired video data.

[1363] Terminal: Transmits acquired video data to the server in real time.

[1364] Step 3:

[1365] Lip movement analysis

[1366] Specific actions:

[1367] Server: Receives video data sent from the terminal.

[1368] Input: Video data.

[1369] Data processing / calculation: Video data is input into an AI model to analyze lip movements.

[1370] Output: Text data of words generated based on lip movements.

[1371] Server: Based on lip-movement analysis, it identifies fragments of spoken language and generates them as text data.

[1372] Step 4:

[1373] Text generation

[1374] Specific actions:

[1375] Server: Converts words identified from lip movements into text data.

[1376] Input: Lip movement analysis data.

[1377] Data processing / calculation: Based on the analyzed lip-sync data, text data is generated through natural language processing.

[1378] Output: Text data.

[1379] Server: Temporarily stores text data in a buffer.

[1380] Step 5:

[1381] Speech synthesis

[1382] Specific actions:

[1383] Server: Inputs text data into the speech synthesis engine.

[1384] Input: Text data.

[1385] Data processing / calculation: Perform a speech synthesis process to convert text data into natural-sounding speech data.

[1386] Output: Audio data.

[1387] Server: The speech synthesis engine converts text data into natural-sounding speech data.

[1388] Server: Stores the generated audio data in a buffer.

[1389] Step 6:

[1390] Audio output

[1391] Specific actions:

[1392] Server: Sends the audio data in the buffer to the terminal.

[1393] Input: Audio data in the buffer.

[1394] Data processing / calculation: Adjust data into a format that can be provided to users.

[1395] Output: Adjusted audio data.

[1396] Device: Plays audio data through a headset or speaker.

[1397] User: Listen to the audio coming from the headset and understand the instructions.

[1398] Step 7:

[1399] emotion recognition

[1400] Specific actions:

[1401] Device: The camera captures the user's facial expressions, and the microphone records their voice tone.

[1402] Input: Facial expression data and voice tone data.

[1403] Data processing / calculation: Facial expressions and voice tone are analyzed using an emotion recognition algorithm.

[1404] Output: Emotional state data.

[1405] Terminal: Sends data on facial expressions and voice tone to the server.

[1406] Server: The emotion engine analyzes facial expressions and voice tone to recognize the emotional state.

[1407] Server: Based on the recognized emotional state, it sends appropriate feedback and notifications to the device.

[1408] User: Receive and respond to the feedback provided.

[1409] (Application Example 2)

[1410] Next, we will explain application example 2. In the following explanation, 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."

[1411] A major challenge is the difficulty of communication in high-noise environments. In such environments, verbal communication is hindered, leading to increased errors in instructions and guidance. Furthermore, accurately understanding and appropriately responding to users' emotional states becomes difficult. Therefore, a system is needed that enables effective communication and recognition of user emotions even in noisy environments.

[1412] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes eye-tracking means for tracking the user's gaze, image acquisition means for acquiring an image of an object the user is fixated on, lip movement analysis means for analyzing the movement of the object's lips from the image acquired by the image acquisition means and generating text data, speech synthesis means for converting the generated text data into speech, speech output means for providing the speech generated by the speech synthesis means to the user, and emotion recognition means for analyzing the user's facial expressions and voice tone to recognize their emotional state. This makes it possible to accurately transmit information visually and audibly even in high-noise environments, and furthermore, to provide appropriate feedback according to the user's emotional state.

[1413] "Eye-tracking means" refers to a method of tracking a user's gaze using a camera built into a wearable device and calculating its direction.

[1414] "Video acquisition means" refers to means of acquiring video footage of an object that a user is focusing on, as identified by eye-tracking means.

[1415] A "lip movement analysis means" is a means of analyzing the movement of a subject's lips from acquired video footage and converting the spoken content into text data.

[1416] A "speech synthesis method" is a means of converting generated text data into natural-sounding speech data.

[1417] "Audio output means" refers to means such as headsets or speakers for providing the generated audio data to the user.

[1418] An "emotion recognition method" is a means of recognizing a user's emotional state by analyzing their facial expressions and voice tone.

[1419] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to analyze data such as lip movements and generate text data and other output data.

[1420] The present invention is a system that enables smooth communication between users in high-noise environments. The main components are eye-tracking means, video acquisition means, lip-movement analysis means, generation AI model, speech synthesis means, speech output means, and emotion recognition means.

[1421] Eye-tracking methods

[1422] The device uses its built-in camera to track the user's gaze. This calculates the user's gaze direction, and this data is sent to the server in real time. To achieve this functionality, an eye-tracking algorithm (e.g., Tobii Pro SDK) is used.

[1423] Video acquisition method

[1424] The server receives data transmitted from the eye-tracking device and identifies and acquires the video of the object the user is fixated on. The identified video is captured by a high-resolution camera (e.g., OpenCV) and transmitted as data for analysis.

[1425] Lip motion analysis means

[1426] The server uses a generative AI model to analyze the video acquired by the video acquisition device. This generative AI model has the ability to analyze the movement of the subject's lips and convert it into text data (e.g., OpenAI GPT, DALL-E). An example of a prompt message is: "Analyze lip movements from video taken in a high-noise environment and convert what is being said into text data. Video data: {video data} Response format: text format."

[1427] Speech synthesis means

[1428] To convert the generated text data into speech, the server uses a speech synthesis engine (e.g., Google Text-to-Speech API, IBM Watson Text to Speech). This produces natural-sounding speech data.

[1429] Audio output means

[1430] The generated audio data is transmitted to the terminal in real time and output through the user's headset or speakers. This allows the user to accurately hear what the other person is saying, even in noisy environments.

[1431] emotion recognition means

[1432] Furthermore, the emotion recognition system analyzes the user's facial expressions and voice tone. An emotion analysis engine (e.g., Microsoft Azure Emotion API) is used to recognize the user's emotional state. This information is transmitted to the server in real time, providing appropriate feedback based on the user's emotional state.

[1433] Specific example

[1434] For example, this system is useful when a user operating heavy machinery in a factory needs to exchange visual and auditory information with a colleague at a distance. The user identifies the colleague by their gaze and analyzes their lip movements, converting them into text data. This text data is then converted into speech and output through a headset, ensuring clear communication of instructions even in noisy environments. Furthermore, the system recognizes the user's emotional state in real time, providing appropriate feedback.

[1435] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1436] Step 1:

[1437] The device uses its built-in camera to continuously capture the user's eye movements and calculate their gaze direction. The input is video data of the user's eyes, and the output is gaze direction data. This gaze direction data is sent to the server in real time. Specifically, the gaze direction is determined using an eye-tracking algorithm (e.g., Tobii Pro SDK).

[1438] Step 2:

[1439] The server receives the transmitted gaze direction data and identifies the object the user is looking at based on that data. The input is gaze direction data, and the output is the coordinate information of the object being gazed at. Based on this information, a high-resolution camera is used to acquire video of the object being gazed at. Specifically, a video processing library (e.g., OpenCV) is used to capture the video data.

[1440] Step 3:

[1441] The server inputs video data acquired by a high-resolution camera into a generative AI model to analyze the movement of the subject's lips. The input is video data, and the output is the analyzed text data. As a specific example, a prompt message using a generative AI model (e.g., OpenAI GPT, DALL-E) would be: "Analyze lip movements from video footage taken in a high-noise environment and convert what is being said into text data. Video data: {video data} Response format: text format."

[1442] Step 4:

[1443] The server inputs text data generated by lip-sync analysis into a speech synthesis engine and converts it into natural-sounding speech data. The input is text data, and the output is speech data. Specifically, it utilizes a speech synthesis engine (e.g., Google Text-to-Speech API, IBM Watson Text to Speech).

[1444] Step 5:

[1445] The server sends the generated audio data to the terminal, which then provides it to the user through a headset or speaker. The input is the audio data, and the output is the audio provided to the user. Specifically, the process involves outputting the audio data through the terminal's headset or speaker.

[1446] Step 6:

[1447] The server uses an emotion analysis engine to analyze the user's facial expressions and voice tone to recognize their emotional state. The input is the user's facial expression data and voice tone data, and the output is the recognized emotional state data. Specifically, it performs real-time analysis using an emotion analysis engine (e.g., Microsoft Azure Emotion API). This emotional state data is sent to the server, and appropriate feedback is provided as needed.

[1448] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1449] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1450] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1451] [Fourth Embodiment]

[1452] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1453] As shown in Figure 7, the 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.

[1454] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1455] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1456] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1457] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1458] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1459] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1460] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1461] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[1462] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1463] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1464] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1465] The present invention is a system for facilitating communication in high-noise environments, and is composed of a combination of eye-tracking means, video acquisition means, lip-movement analysis means, generation AI model, speech synthesis means, and speech output means. The embodiments for carrying out the invention will be described in detail below.

[1466] System Configuration

[1467] Eye-tracking methods

[1468] The device (smart glasses) is equipped with a built-in camera to capture the user's eye movements. This camera can track the user's gaze and execute an algorithm to calculate its direction.

[1469] Video acquisition method

[1470] The device identifies the object the user is looking at through eye-tracking technology and acquires video footage of that object using a high-resolution camera. This video data is transmitted to the server in real time.

[1471] Lip motion analysis means

[1472] The server receives video data transmitted from the terminal and inputs it into a generative AI model used for lip movement analysis. This generative AI model analyzes the movement of the other person's lips from the acquired video and generates text data of what is being said.

[1473] Speech synthesis means

[1474] The generated text data is input into a speech synthesis system on the server and converted into natural, fluent speech data. This speech data is then adjusted to a quality that users can listen to without any discomfort.

[1475] Audio output means

[1476] Finally, the generated audio data is sent to a device (smart glasses) and then provided to the user through a headset or speaker. This allows the user to accurately hear what the other person is saying, even in noisy environments.

[1477] Program processing

[1478] The following describes, in natural language, how the system of the present invention works.

[1479] Eye Tracking

[1480] The device continuously captures the user's gaze using its built-in camera and calculates the direction of their gaze. The calculated gaze data is sent to the server in real time.

[1481] Video acquisition

[1482] The server receives gaze data and uses it to identify the video object the user is fixated on. The identified video is then acquired for further detailed analysis.

[1483] Lip movement analysis

[1484] The acquired video is sent to a server, where a generative AI model analyzes the lip movements to determine what is being said and generates it as text data. This text data is temporarily stored in a buffer.

[1485] Speech synthesis

[1486] The text data stored in the buffer is input to the speech synthesis engine and converted into natural-sounding speech data. This speech data is then stored back into the buffer.

[1487] Audio output

[1488] Finally, the generated audio data is sent to the terminal and provided to the user through a headset or speaker, allowing the user to accurately hear what the other person is saying even in noisy environments.

[1489] Specific example

[1490] For example, suppose User A is working in a noisy factory while wearing smart glasses. In this scenario, User B is giving instructions to User A, but User A cannot hear them due to the surrounding noise.

[1491] 1. User A turns their gaze towards User B.

[1492] 2. The device's built-in camera captures user A's gaze, and the gaze data is sent to the server.

[1493] 3. The server receives the gaze data, identifies user B's video, and retrieves the video data.

[1494] 4. The server uses a generative AI model to convert what user B is saying from the movement of their lips into text data.

[1495] 5. The server inputs the text data into the speech synthesis engine and generates the speech data.

[1496] 6. The generated audio data is sent to the terminal and played back through User A's headset.

[1497] This allows User A to accurately hear User B's instructions even in a noisy environment, enabling them to proceed with the work smoothly.

[1498] Thus, the system of the present invention enables efficient communication in a variety of high-noise environments.

[1499] The following describes the processing flow.

[1500] Step 1:

[1501] The device captures the user's gaze.

[1502] The camera built into the device continuously captures the user's eye movements.

[1503] The system processes video data from the camera in real time and executes an eye-tracking algorithm to calculate the user's gaze direction.

[1504] Step 2:

[1505] The device sends eye-tracking data to the server.

[1506] The calculated line-of-sight direction data is sent to the server at regular time intervals.

[1507] Eye-tracking data includes information such as the coordinates of the user's gaze point.

[1508] Step 3:

[1509] The server receives gaze data and acquires video footage of the object the user is looking at.

[1510] The server receives the gaze data transmitted from the terminal.

[1511] Based on the received gaze data, the system identifies the video area of ​​the object the user is fixated on.

[1512] Detailed video data of the identified video area is obtained from the device.

[1513] Step 4:

[1514] The server analyzes lip movements and generates text data.

[1515] The acquired video data is input into the AI ​​model on the server.

[1516] The generative AI model analyzes lip movements and converts what is being said into text data.

[1517] The generated text data is temporarily stored in a buffer.

[1518] Step 5:

[1519] The server inputs text data into the speech synthesis engine.

[1520] The text data stored in the buffer is input to the speech synthesis engine.

[1521] A speech synthesis engine converts input text data into natural-sounding speech data.

[1522] The generated audio data is stored back into the buffer.

[1523] Step 6:

[1524] The server sends the audio data to the terminal.

[1525] The audio data stored in the buffer is sent to the terminal as soon as it is ready for playback.

[1526] Step 7:

[1527] The device plays the audio data.

[1528] The device plays the received audio data through a headset or speaker.

[1529] The user listens to the generated audio through a headset or similar device.

[1530] Specific example

[1531] For example, the following shows the processing flow when User B gives visual instructions to User A while User A is working in a noisy factory.

[1532] Step 1:

[1533] User A wears smart glasses and directs their gaze towards User B. The camera in the device captures User A's eye movements and calculates the direction of their gaze.

[1534] Step 2:

[1535] The terminal sends calculated gaze data to the server. This includes coordinate information for the direction in which user A is looking.

[1536] Step 3:

[1537] The server receives gaze data and identifies user B's video area based on it. The identified video area is then retrieved from the terminal.

[1538] Step 4:

[1539] The server inputs the acquired video data into a generating AI model, which analyzes lip movements and converts them into text data. The generated text data is then temporarily stored in a buffer.

[1540] Step 5:

[1541] The server inputs the text data stored in the buffer into the speech synthesis engine and converts it into natural-sounding speech data. The generated speech data is then stored back into the buffer.

[1542] Step 6:

[1543] The server sends the generated audio data to the terminal.

[1544] Step 7:

[1545] The terminal plays the received audio data through the headset, and user A listens to the audio.

[1546] This allows user A to accurately receive instructions from user B even in noisy environments, enabling them to carry out their work efficiently.

[1547] (Example 1)

[1548] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1549] Communication in high-noise environments presents a significant challenge due to the difficulty in hearing voices. This is particularly problematic in places requiring concentration, such as factories and construction sites, where important instructions may be difficult to convey, potentially negatively impacting work efficiency and safety. The present invention aims to provide a means for effective communication even in such high-noise environments.

[1550] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1551] In this invention, the server includes eye-tracking means for tracking the user's gaze, image acquisition means for acquiring images of an object the user is fixated on, lip-movement analysis means for analyzing the movement of the object's lips from the images acquired by the image acquisition means and generating text data, speech synthesis means for converting the generated text data into speech, and speech output means for providing the user with the speech generated by the speech synthesis means. This makes it possible for the user to accurately hear what the other person is saying, even in a high-noise environment.

[1552] "Eye-tracking means" refers to devices or algorithms that capture the movement of a user's eyes and calculate the direction of their gaze.

[1553] "Image acquisition means" refers to devices or technologies for acquiring images of objects that a user is focusing on.

[1554] A "lip movement analysis means" refers to a device or generation AI model that analyzes the movement of a subject's lips from acquired video footage and converts the spoken content into text data.

[1555] "Speech synthesis means" refers to a device or engine that converts generated text data into natural-sounding speech data.

[1556] "Audio output means" refers to a device for providing the user with audio generated by a speech synthesis means, and includes headsets and speakers.

[1557] A "wearable device" is an electronic device that a user can wear and use.

[1558] A "machine learning model" is an algorithm that learns patterns from data and uses them to make predictions and classifications.

[1559] The present invention is a system for facilitating communication in high-noise environments, and is comprised of a combination of eye-tracking means, video acquisition means, lip-movement analysis means, a generation AI model, speech synthesis means, and speech output means. The following describes in detail the embodiments for implementing this system.

[1560] System Configuration

[1561] Eye-tracking methods

[1562] The device (wearable device) is equipped with a built-in camera to capture the user's eye movements. This camera can track the user's gaze and execute an algorithm to calculate its direction. Specifically, a face detection algorithm using Haar-like features is applied.

[1563] Video acquisition method

[1564] The device identifies the object the user is looking at through eye-tracking technology and captures video of that object using a high-resolution camera. This camera has a 1080p resolution and captures video in real time. The acquired video data is compressed and transmitted to a server via Wi-Fi.

[1565] Lip motion analysis means

[1566] The server receives video data sent from the terminal and inputs it into a generative AI model. This generative AI model is a TensorFlow-based RNN model that analyzes the lip movements of the other party from the acquired video and generates text data of what is being said. This text data is temporarily stored in a Redis buffer.

[1567] Speech synthesis means

[1568] The text data stored in the buffer is input to a speech synthesis engine on the server (e.g., Google Text-to-Speech API). The speech synthesis engine converts the text data into natural, fluent speech data. This speech data is also stored in the buffer.

[1569] Audio output means

[1570] Finally, the generated audio data is sent to the device and delivered to the user through a headset or speaker (e.g., Bluetooth earphones). This allows the user to accurately hear what the other person is saying, even in noisy environments.

[1571] Specific example

[1572] For example, suppose User A is working in a noisy factory while wearing smart glasses. In this scenario, User B is giving instructions to User A, but User A cannot hear them due to the surrounding noise.

[1573] 1. User A turns their gaze towards User B.

[1574] 2. The device's built-in camera captures user A's gaze, and the gaze data is sent to the server.

[1575] 3. The server receives the gaze data, identifies user B's video, and retrieves the video data.

[1576] 4. The server uses a generative AI model to convert what user B is saying from the movement of their lips into text data.

[1577] 5. The server inputs the text data into the speech synthesis engine and generates the speech data.

[1578] 6. The generated audio data is sent to the terminal and played back through User A's headset.

[1579] This allows User A to accurately hear User B's instructions even in a noisy environment, enabling them to proceed with the work smoothly.

[1580] Example of a prompt

[1581] "User A wears smart glasses in the factory, identifies User B's image through their gaze, analyzes what they are saying from their lip movements, and plays it back as audio."

[1582] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1583] Step 1:

[1584] The device uses its built-in camera to capture the user's gaze. The captured gaze data is processed at a rate of 60 frames per second and input into an algorithm that calculates the direction of the gaze (for example, a face detection algorithm using Haar-like features). The calculated gaze data is sent to a server via Wi-Fi.

[1585] Input: User eye-tracking data

[1586] Data processing: Calculation of line of sight

[1587] Output: Eye-tracking data

[1588] Specific operation: When the user focuses on something, the device's built-in camera captures this action, detects the direction of their gaze in real time, and sends it to the server.

[1589] Step 2:

[1590] The server receives the gaze data and identifies the object the user is looking at. To obtain video of the identified object, the device's high-resolution camera is activated and captures video in the specified direction. The captured video data is compressed and sent to the server in real time.

[1591] Input: Eye-tracking data

[1592] Data processing: Identifying and capturing video of objects the user is focusing on.

[1593] Output: Video data

[1594] Specific operation: The server analyzes the gaze data to identify the object the user is looking at. The terminal's high-resolution camera captures a specified area and sends the video data to the server.

[1595] Step 3:

[1596] The server inputs the received video data into a generative AI model. This generative AI model analyzes the changes between video frames and recognizes lip movements. Based on the recognition results, it generates text data of what is being said. This text data is temporarily stored in a Redis buffer.

[1597] Input: Video data

[1598] Data processing: Recognition of lip movements and conversion to text.

[1599] Output: Text data

[1600] Specific operation: Video data is input into an AI model that generates text data by analyzing lip movements.

[1601] Step 4:

[1602] The server inputs the text data stored in the buffer into a speech synthesis engine. This speech synthesis engine (for example, the Google Text-to-Speech API) converts the text data into natural, fluent speech data. This converted speech data is also stored back into the buffer.

[1603] Input: Text data

[1604] Data processing: Converting text data to audio data

[1605] Output: Audio data

[1606] Specific operation: The server inputs text data into the speech synthesis engine and stores the generated audio file in a buffer.

[1607] Step 5:

[1608] The server sends the audio data from the buffer to the terminal. The terminal decodes the received audio data and plays it back to the user through a headset or speaker (e.g., Bluetooth earphones).

[1609] Input: Audio data

[1610] Data processing: Sending and playing back audio data.

[1611] Output: Audio to be played

[1612] Specific operation: The server sends audio data to the terminal, and the terminal plays it back, allowing the server to hear what the user is saying.

[1613] (Application Example 1)

[1614] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1615] Conventional systems presented challenges in human-machine communication within noisy factory environments, hindering smooth work instructions and information exchange. Furthermore, despite advancements in eye-tracking and lip-syncing technologies, a reliable method for accurately and quickly sending instructions to machines using these techniques remained unestablished. This resulted in decreased work efficiency and an increased risk of operational errors.

[1616] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1617] In this invention, the server is

[1618] A means of tracking the user's gaze,

[1619] A means for acquiring video footage of an object that the user is focusing on,

[1620] A lip motion analysis means analyzes the movement of the target's lips from video acquired by a video acquisition means and generates text data,

[1621] A speech synthesis means that converts generated text data into speech,

[1622] A voice output means that provides the user with voice generated by a voice synthesis means,

[1623] An automated means that transmits generated voice data to a machine and executes instructions,

[1624] This includes enabling accurate and efficient instruction transmission from the user to the machine, even in high-noise environments.

[1625] "Eye-tracking means" refers to a device or system that captures the movement of a user's eyes and calculates its direction.

[1626] "Image acquisition means" refers to a device or system for acquiring images of an object that the user is focusing on.

[1627] A "lip movement analysis means" is a device or system that analyzes the movement of a subject's lips from acquired video footage and generates text data.

[1628] "Speech synthesis means" refers to a device or system that converts generated text data into speech.

[1629] "Speech output means" refers to a device or system that provides the user with speech generated by speech synthesis means.

[1630] "Automation means" refers to a device or system for transmitting generated voice data to a machine and executing instructions.

[1631] A "wearable display" is a general term for a device that a user can wear.

[1632] A "generative AI model" is an artificial intelligence model used to generate specific information (in this case, the content of speech based on lip movements) from video data.

[1633] Modes for carrying out the invention

[1634] The present invention is a system for facilitating communication between a user and a machine in a high-noise environment, and is composed of a combination of eye-tracking means, video acquisition means, lip-movement analysis means, a generation AI model, speech synthesis means, speech output means, and automation means. The embodiments for carrying out the present invention will be described in detail below.

[1635] System Configuration

[1636] Eye-tracking methods

[1637] The device (wearable display) is equipped with a built-in camera to capture the user's eye movements. This camera can track the user's gaze and execute algorithms to calculate its direction. For example, Tobii Eye Tracker is used.

[1638] Video acquisition method

[1639] The device identifies the object the user is looking at through eye-tracking technology and acquires video footage of that object using a high-resolution camera. This video data is transmitted to the server in real time.

[1640] Lip motion analysis means

[1641] The server receives video data transmitted from the terminal and inputs it into a generative AI model used for lip movement analysis. This generative AI model, such as one from OpenAI, analyzes the movement of the other person's lips from the acquired video and generates text data of what is being said.

[1642] Speech synthesis means

[1643] The generated text data is input into a speech synthesis system on the server and converted into natural, fluent speech data. For example, the Python speech synthesis library pyttsx3 is used. This speech data is then adjusted to a quality that can be heard without discomfort by a user or machine.

[1644] Audio output means

[1645] Finally, the generated audio data is transmitted to a device (wearable display) and then provided to the user through a headset or speaker. This allows the user to accurately hear what the other person is saying, even in noisy environments.

[1646] automated means

[1647] Furthermore, the generated voice data is transmitted to a machine by automated means. The machine then performs predetermined actions based on the voice instructions.

[1648] Program processing

[1649] Each of the above-described mechanisms of the system is implemented using the following hardware and software.

[1650] Eye tracking method: Wearable display with built-in Tobii Eye Tracker

[1651] Video acquisition method: High-resolution camera, OpenCV

[1652] Lip movement analysis method: Generative AI model (OpenAI)

[1653] Speech synthesis method: pyttsx3

[1654] Audio output method: Headset or speaker

[1655] Automation methods: Machines (e.g., Boston Dynamics robots)

[1656] Specific example

[1657] For example, suppose a robot is used to transport parts within a factory. In a high-noise environment, communication using normal voice commands is difficult. In such a situation, a worker uses a wearable display to instruct the robot to move parts to a specific location.

[1658] 1. The worker turns their gaze towards the robot.

[1659] 2. The built-in camera captures the user's gaze and sends the gaze data to the server.

[1660] 3. The server analyzes the gaze data, identifies the robot's image, and acquires the image data.

[1661] 4. The generation AI model analyzes the video and generates text data that says, "Transport part A to line B."

[1662] 5. The speech synthesis engine converts this text data into speech data.

[1663] 6. The generated voice data is sent to the robot, and the robot acts according to the instructions.

[1664] Examples of prompts to input into a generative AI model

[1665] Video data:<video_stream>

[1666] Prompt: Analyze the lip movements in the video and output the spoken content as text data. For example, convert it to "Take part A to line B."

[1667] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1668] Step 1:

[1669] The user uses a wearable display and directs their gaze towards the robot. The device's built-in camera captures the user's gaze and transmits the data to a server in real time. Specifically, the Tobii Eye Tracker detects the user's eye movements and outputs gaze data. This gaze data indicates the direction and object the user is focusing on.

[1670] Step 2:

[1671] The server receives gaze data and performs data calculations based on that data to identify the image of the object the user is fixated on. The server acquires a video stream from a high-resolution camera and identifies the image of the object the user is fixated on. This video data includes the robot or work environment that the user is looking at. The identified video data is then stored on the server.

[1672] Step 3:

[1673] The server inputs the received video data into a generative AI model, which then performs lip-movement analysis. This generative AI model uses OpenAI technology to analyze lip movements in the video and convert the spoken content into text data. Specifically, it analyzes lip movements in the video data frame by frame and outputs the spoken content in text format in real time.

[1674] Step 4:

[1675] The generated text data is input into a speech synthesis system and converted into natural, fluent speech data on the server. Using the Python speech synthesis library pyttsx3, the generated text data is converted into an audio file. This audio data is in a format that can be understood by both users and machines.

[1676] Step 5:

[1677] The generated audio data is transmitted from the server to the terminal and provided to the user through the terminal's headset or speakers. By listening to this audio data, the user can accurately understand what the other person is saying, even in noisy environments. Specifically, the generated audio data is output from the headset and reaches the user's ears.

[1678] Step 6:

[1679] Furthermore, the generated voice data is transmitted from the server to the machine, and instructions are executed by automated means. The machine (for example, a Boston Dynamics robot) performs predetermined actions based on these voice instructions. Specifically, a robot that receives the voice instruction "Move part A to line B" will move the part to the designated location accordingly.

[1680] This allows users to give precise instructions to robots even in noisy environments, enabling efficient work.

[1681] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1682] The present invention is a system for facilitating communication in high-noise environments, and is comprised of a combination of eye-tracking means, video acquisition means, lip-movement analysis means, a generative AI model, speech synthesis means, speech output means, and an emotion engine. The embodiments for carrying out the invention will be described in detail below.

[1683] System Configuration

[1684] Eye-tracking methods

[1685] The device (smart glasses) uses a built-in camera to capture the user's eye movements and calculate their gaze direction. This data is transmitted to a server in real time.

[1686] Video acquisition method

[1687] The system uses eye-tracking technology to capture video of the object the user is focusing on using a high-resolution camera. The video data is transmitted to a server in real time and used for analysis.

[1688] Lip motion analysis means

[1689] The server receives video data transmitted from the terminal and analyzes the lip movements using a generation AI model. This generates text data of what the subject is saying.

[1690] Speech synthesis means

[1691] The generated text data is input into a speech synthesis engine and converted into natural, fluent speech data. This speech data is then adjusted for listening.

[1692] Audio output means

[1693] The generated audio data is sent to the terminal and provided to the user through a headset or speaker. This allows the user to accurately hear what the other person is saying, even in noisy environments.

[1694] Emotional Engine

[1695] This system also incorporates an emotion engine that analyzes the user's facial expressions and voice tone to recognize their emotions. This enables the system to provide feedback and responses tailored to the user's emotional state.

[1696] Program processing

[1697] The following describes, in natural language, how the system of the present invention works.

[1698] Eye Tracking

[1699] The device continuously captures the user's gaze using its built-in camera and calculates its direction. This gaze data is transmitted to the server in real time.

[1700] Video acquisition

[1701] The server receives gaze data and identifies the video of the object the user is fixated on based on that data. The identified video is acquired in high resolution and further analyzed.

[1702] Lip movement analysis

[1703] The acquired video data is input into an AI model, which generates text data from the lip movements to understand what is being said. This text data is temporarily stored in a buffer.

[1704] Speech synthesis

[1705] Text data is input into a speech synthesis engine and converted into natural-sounding speech data. The generated speech data is then stored back in a buffer.

[1706] Audio output

[1707] The audio data stored in the buffer is transmitted to the terminal and provided to the user through a headset or speaker. By listening to this, the user can understand what the other person is saying, even in noisy environments.

[1708] emotion recognition

[1709] The emotion engine analyzes the user's facial expressions and voice tone to recognize their emotional state. This information is sent to the server in real time, and appropriate feedback is provided as needed.

[1710] Specific example

[1711] For example, suppose User A is working in a noisy factory. In this situation, User B is giving User A visual instructions, but User A cannot hear the instructions due to the surrounding noise. The following is the processing flow for this situation.

[1712] 1. User A wears smart glasses and directs their gaze towards User B. The device's camera captures User A's gaze and sends the data to the server.

[1713] 2. The server receives the gaze data, identifies user B's video, and acquires the video in high resolution.

[1714] 3. The server uses a generated AI model to generate text data from the lip movements of user B, indicating what is being said.

[1715] 4. The server inputs the text data into the speech synthesis engine and converts it into natural-sounding speech data.

[1716] 5. The generated audio data is sent to the terminal and played back through User A's headset. This allows User A to accurately hear the instructions.

[1717] 6. Simultaneously, the emotion engine analyzes user A's facial expressions and voice tone to recognize their emotional state. If necessary, the server provides appropriate feedback.

[1718] Thus, the system of the present invention enables efficient communication even in noisy environments and can also respond according to the user's emotional state.

[1719] The following describes the processing flow.

[1720] Processing flow

[1721] Step 1:

[1722] The device captures the user's gaze.

[1723] A camera built into the device (smart glasses) continuously captures the user's eye movements.

[1724] The captured video data is processed in real time to calculate the user's gaze direction.

[1725] Step 2:

[1726] The device sends eye-tracking data to the server.

[1727] The calculated line-of-sight direction data is sent to the server at regular time intervals.

[1728] Eye-tracking data includes information such as the coordinates of the user's gaze point.

[1729] Step 3:

[1730] The server receives gaze data and acquires video footage of the object the user is looking at.

[1731] The server receives the gaze data transmitted from the terminal.

[1732] Based on the received gaze data, the system identifies the video area of ​​the object the user is fixated on.

[1733] Detailed video data of the identified video area is obtained from the device.

[1734] Step 4:

[1735] The server analyzes lip movements and generates text data.

[1736] The acquired video data is input into the AI ​​model on the server.

[1737] The generative AI model analyzes lip movements and converts what is being said into text data.

[1738] The generated text data is temporarily stored in a buffer.

[1739] Step 5:

[1740] The server inputs text data into the speech synthesis engine.

[1741] The text data stored in the buffer is input to the speech synthesis engine.

[1742] A speech synthesis engine converts input text data into natural-sounding speech data.

[1743] The generated audio data is stored back into the buffer.

[1744] Step 6:

[1745] The server sends the audio data to the terminal.

[1746] The audio data stored in the buffer is sent to the terminal as soon as it is ready for playback.

[1747] Step 7:

[1748] The device plays the audio data.

[1749] The device plays the received audio data through a headset or speaker.

[1750] The user listens to the generated audio through a headset or similar device.

[1751] Step 8:

[1752] The device acquires the user's facial expression data.

[1753] A camera built into the device (smart glasses) captures the user's facial expressions.

[1754] The captured facial expression data is sent to the server in real time.

[1755] Step 9:

[1756] The server analyzes the user's emotions.

[1757] The server receives facial expression data from the terminal and inputs it into the emotion engine.

[1758] The emotion engine analyzes facial expression data to identify the user's emotional state.

[1759] The analysis results are processed in real time, and feedback is provided as needed.

[1760] Specific example

[1761] For example, the following is a processing flow for a situation where User A is working in a noisy factory, and User B is giving instructions to User A, but User B cannot hear User A due to the surrounding noise.

[1762] Step 1:

[1763] User A wears smart glasses and directs their gaze towards User B. The device's built-in camera captures User A's eye movements and sends the data to the server.

[1764] Step 2:

[1765] The terminal sends calculated gaze data to the server. This includes coordinate information for the direction in which user A is looking.

[1766] Step 3:

[1767] The server receives gaze data and identifies user B's video area based on it. The identified video area is then retrieved from the terminal.

[1768] Step 4:

[1769] The server inputs the acquired video data into a generating AI model, which analyzes lip movements and converts them into text data. The generated text data is then temporarily stored in a buffer.

[1770] Step 5:

[1771] The server inputs the text data stored in the buffer into the speech synthesis engine and converts it into natural-sounding speech data. The generated speech data is then stored back into the buffer.

[1772] Step 6:

[1773] The server sends the generated audio data to the terminal.

[1774] Step 7:

[1775] The terminal plays the received audio data through the headset, and user A listens to the audio.

[1776] Step 8:

[1777] The device's built-in camera captures user A's facial expressions. The captured facial expression data is sent to the server in real time.

[1778] Step 9:

[1779] The server receives facial expression data and inputs it into the emotion engine. The emotion engine analyzes user A's facial expressions and recognizes their emotional state. The analysis results are processed in real time, and appropriate feedback is provided as needed.

[1780] This allows User A to accurately hear User B's instructions even in a noisy environment, and also enables responses tailored to User A's emotional state.

[1781] (Example 2)

[1782] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1783] In noisy environments, communication between users becomes difficult, hindering the accurate transmission of information. Furthermore, the inability to understand users' emotional states can lead to delays in responding appropriately to stressful or difficult situations. Solving these problems is essential.

[1784] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1785] In this invention, the server includes eye-tracking means, video acquisition means, lip-movement analysis means, speech synthesis means, speech output means, and emotion recognition means. This enables accurate information transmission between users even in high-noise environments, and further enables responses that correspond to the user's emotional state.

[1786] "Eye-tracking means" refers to a device or technology for detecting a user's gaze and determining its direction.

[1787] "Image acquisition means" refers to a device or technology for acquiring images of an object that a user is focusing on.

[1788] "Lip movement analysis means" refers to a device or technology for analyzing the movement of a subject's lips from video data and generating text data based on that movement.

[1789] "Speech synthesis means" refers to a device or technology for converting generated text data into speech data.

[1790] "Audio output means" refers to a device or technology for providing generated audio data to a user.

[1791] "Emotion recognition means" refers to a device or technology that analyzes a user's facial expressions and voice tone to recognize the user's emotional state.

[1792] A "head-mounted display device" is a device worn on the user's head that provides functions such as eye tracking and image acquisition.

[1793] An "image sensor" is an electronic component or technology used to capture images or videos.

[1794] A "generative model" is an algorithm or technique that generates new data from specific data based on AI technology.

[1795] The present invention is a system for facilitating communication in high-noise environments, and is comprised of a combination of eye-tracking means, video acquisition means, lip-movement analysis means, a generation AI model, speech synthesis means, speech output means, and emotion recognition means. The embodiments for carrying out the invention will be described in detail below.

[1796] System Configuration

[1797] Eye-tracking methods

[1798] The device (head-mounted display device) uses an image sensor to capture the user's eye movements and calculate their gaze direction. This data is transmitted to the server in real time.

[1799] Video acquisition method

[1800] The system uses eye-tracking technology to capture video of the object the user is focusing on using a high-resolution camera. The video data is transmitted to a server in real time and used for analysis.

[1801] Lip motion analysis means

[1802] The server receives video data transmitted from the terminal and analyzes the lip movements using a generation AI model. This generates text data of what the subject is saying.

[1803] Speech synthesis means

[1804] The generated text data is input into a speech synthesis engine and converted into natural, fluent speech data. This speech data is then adjusted for listening.

[1805] Audio output means

[1806] The generated audio data is sent to the terminal and provided to the user through a headset or speaker. This allows the user to accurately hear what the other person is saying, even in noisy environments.

[1807] emotion recognition means

[1808] This system also incorporates emotion recognition capabilities, analyzing the user's facial expressions and voice tone to recognize their emotions. This enables the system to provide feedback and responses tailored to the user's emotional state.

[1809] Specific example

[1810] For example, suppose User A is working in a noisy factory. In this situation, User B is giving User A visual instructions, but User A cannot hear the instructions due to the surrounding noise. The following is the processing flow for this situation.

[1811] 1. User A wears a head-mounted display device and directs their gaze towards User B. The terminal's image sensor captures User A's gaze and transmits the data to the server.

[1812] 2. The server receives the gaze data, identifies user B's video, and acquires the video in high resolution.

[1813] 3. The server uses a generated AI model to generate text data from the lip movements of user B, indicating what is being said.

[1814] 4. The server inputs the text data into the speech synthesis engine and converts it into natural-sounding speech data.

[1815] 5. The generated audio data is sent to the terminal and played back through User A's headset. This allows User A to accurately hear the instructions.

[1816] 6. Simultaneously, the emotion recognition system analyzes user A's facial expressions and voice tone to recognize their emotional state. If necessary, the server provides appropriate feedback.

[1817] Example of a prompt

[1818] The following are specific examples of prompt statements to be input to the generating AI model.

[1819] "Please explain the procedure for performing real-time lip-sync analysis on text content specified by User B and converting it into speech data."

[1820] "Please provide an example of how a system can support smooth communication between user A and user B in a high-noise environment."

[1821] "Please provide examples of feedback tailored to the user's emotional state."

[1822] Thus, the system of the present invention enables efficient communication even in high-noise environments and can also respond according to the user's emotional state.

[1823] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1824] Step 1:

[1825] Eye Tracking

[1826] Specific actions:

[1827] User: Wear the head-mounted display device and direct your gaze towards the object you wish to observe.

[1828] Terminal: An image sensor built into a head-mounted display device captures the user's eye movements.

[1829] Input: Eye movement data captured by the image sensor.

[1830] Data processing / calculation: Calculates the direction of gaze from eye movement data.

[1831] Output: Calculated line-of-sight direction data.

[1832] Terminal: Sends captured eye-tracking data to the server in real time.

[1833] Step 2:

[1834] Video acquisition

[1835] Specific actions:

[1836] Server: Receives gaze data and identifies the object in the direction the user is looking.

[1837] Device: Acquires video footage of the identified target using a high-resolution camera.

[1838] Input: Actual viewing direction data.

[1839] Data processing / calculation: Adjust the camera's field of view based on the direction of gaze and capture the video.

[1840] Output: Acquired video data.

[1841] Terminal: Transmits acquired video data to the server in real time.

[1842] Step 3:

[1843] Lip movement analysis

[1844] Specific actions:

[1845] Server: Receives video data sent from the terminal.

[1846] Input: Video data.

[1847] Data processing / calculation: Video data is input into an AI model to analyze lip movements.

[1848] Output: Text data of words generated based on lip movements.

[1849] Server: Based on lip-movement analysis, it identifies fragments of spoken language and generates them as text data.

[1850] Step 4:

[1851] Text generation

[1852] Specific actions:

[1853] Server: Converts words identified from lip movements into text data.

[1854] Input: Lip movement analysis data.

[1855] Data processing / calculation: Based on the analyzed lip-sync data, text data is generated through natural language processing.

[1856] Output: Text data.

[1857] Server: Temporarily stores text data in a buffer.

[1858] Step 5:

[1859] Speech synthesis

[1860] Specific actions:

[1861] Server: Inputs text data into the speech synthesis engine.

[1862] Input: Text data.

[1863] Data processing / calculation: Perform a speech synthesis process to convert text data into natural-sounding speech data.

[1864] Output: Audio data.

[1865] Server: The speech synthesis engine converts text data into natural-sounding speech data.

[1866] Server: Stores the generated audio data in a buffer.

[1867] Step 6:

[1868] Audio output

[1869] Specific actions:

[1870] Server: Sends the audio data in the buffer to the terminal.

[1871] Input: Audio data in the buffer.

[1872] Data processing / calculation: Adjust data into a format that can be provided to users.

[1873] Output: Adjusted audio data.

[1874] Device: Plays audio data through a headset or speaker.

[1875] User: Listen to the audio coming from the headset and understand the instructions.

[1876] Step 7:

[1877] emotion recognition

[1878] Specific actions:

[1879] Device: The camera captures the user's facial expressions, and the microphone records their voice tone.

[1880] Input: Facial expression data and voice tone data.

[1881] Data processing / calculation: Facial expressions and voice tone are analyzed using an emotion recognition algorithm.

[1882] Output: Emotional state data.

[1883] Terminal: Sends data on facial expressions and voice tone to the server.

[1884] Server: The emotion engine analyzes facial expressions and voice tone to recognize the emotional state.

[1885] Server: Based on the recognized emotional state, it sends appropriate feedback and notifications to the device.

[1886] User: Receive and respond to the feedback provided.

[1887] (Application Example 2)

[1888] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1889] A major challenge is the difficulty of communication in high-noise environments. In such environments, verbal communication is hindered, leading to increased errors in instructions and guidance. Furthermore, accurately understanding and appropriately responding to users' emotional states becomes difficult. Therefore, a system is needed that enables effective communication and recognition of user emotions even in noisy environments.

[1890] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes eye-tracking means for tracking the user's gaze, image acquisition means for acquiring an image of an object the user is fixated on, lip movement analysis means for analyzing the movement of the object's lips from the image acquired by the image acquisition means and generating text data, speech synthesis means for converting the generated text data into speech, speech output means for providing the speech generated by the speech synthesis means to the user, and emotion recognition means for analyzing the user's facial expressions and voice tone to recognize their emotional state. This makes it possible to accurately transmit information visually and audibly even in high-noise environments, and furthermore, to provide appropriate feedback according to the user's emotional state.

[1891] "Eye-tracking means" refers to a method of tracking a user's gaze using a camera built into a wearable device and calculating its direction.

[1892] "Video acquisition means" refers to means of acquiring video footage of an object that a user is focusing on, as identified by eye-tracking means.

[1893] A "lip movement analysis means" is a means of analyzing the movement of a subject's lips from acquired video footage and converting the spoken content into text data.

[1894] A "speech synthesis method" is a means of converting generated text data into natural-sounding speech data.

[1895] "Audio output means" refers to means such as headsets or speakers for providing the generated audio data to the user.

[1896] An "emotion recognition method" is a means of recognizing a user's emotional state by analyzing their facial expressions and voice tone.

[1897] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to analyze data such as lip movements and generate text data and other output data.

[1898] The present invention is a system that enables smooth communication between users in high-noise environments. The main components are eye-tracking means, video acquisition means, lip-movement analysis means, generation AI model, speech synthesis means, speech output means, and emotion recognition means.

[1899] Eye-tracking methods

[1900] The device uses its built-in camera to track the user's gaze. This calculates the user's gaze direction, and this data is sent to the server in real time. To achieve this functionality, an eye-tracking algorithm (e.g., Tobii Pro SDK) is used.

[1901] Video acquisition method

[1902] The server receives data transmitted from the eye-tracking device and identifies and acquires the video of the object the user is fixated on. The identified video is captured by a high-resolution camera (e.g., OpenCV) and transmitted as data for analysis.

[1903] Lip motion analysis means

[1904] The server uses a generative AI model to analyze the video acquired by the video acquisition device. This generative AI model has the ability to analyze the movement of the subject's lips and convert it into text data (e.g., OpenAI GPT, DALL-E). An example of a prompt message is: "Analyze lip movements from video taken in a high-noise environment and convert what is being said into text data. Video data: {video data} Response format: text format."

[1905] Speech synthesis means

[1906] To convert the generated text data into speech, the server uses a speech synthesis engine (e.g., Google Text-to-Speech API, IBM Watson Text to Speech). This produces natural-sounding speech data.

[1907] Audio output means

[1908] The generated audio data is transmitted to the terminal in real time and output through the user's headset or speakers. This allows the user to accurately hear what the other person is saying, even in noisy environments.

[1909] emotion recognition means

[1910] Furthermore, the emotion recognition system analyzes the user's facial expressions and voice tone. An emotion analysis engine (e.g., Microsoft Azure Emotion API) is used to recognize the user's emotional state. This information is transmitted to the server in real time, providing appropriate feedback based on the user's emotional state.

[1911] Specific example

[1912] For example, this system is useful when a user operating heavy machinery in a factory needs to exchange visual and auditory information with a colleague at a distance. The user identifies the colleague by their gaze and analyzes their lip movements, converting them into text data. This text data is then converted into speech and output through a headset, ensuring clear communication of instructions even in noisy environments. Furthermore, the system recognizes the user's emotional state in real time, providing appropriate feedback.

[1913] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1914] Step 1:

[1915] The device uses its built-in camera to continuously capture the user's eye movements and calculate their gaze direction. The input is video data of the user's eyes, and the output is gaze direction data. This gaze direction data is sent to the server in real time. Specifically, the gaze direction is determined using an eye-tracking algorithm (e.g., Tobii Pro SDK).

[1916] Step 2:

[1917] The server receives the transmitted gaze direction data and identifies the object the user is looking at based on that data. The input is gaze direction data, and the output is the coordinate information of the object being gazed at. Based on this information, a high-resolution camera is used to acquire video of the object being gazed at. Specifically, a video processing library (e.g., OpenCV) is used to capture the video data.

[1918] Step 3:

[1919] The server inputs video data acquired by a high-resolution camera into a generative AI model to analyze the movement of the subject's lips. The input is video data, and the output is the analyzed text data. As a specific example, a prompt message using a generative AI model (e.g., OpenAI GPT, DALL-E) would be: "Analyze lip movements from video footage taken in a high-noise environment and convert what is being said into text data. Video data: {video data} Response format: text format."

[1920] Step 4:

[1921] The server inputs text data generated by lip-sync analysis into a speech synthesis engine and converts it into natural-sounding speech data. The input is text data, and the output is speech data. Specifically, it utilizes a speech synthesis engine (e.g., Google Text-to-Speech API, IBM Watson Text to Speech).

[1922] Step 5:

[1923] The server sends the generated audio data to the terminal, which then provides it to the user through a headset or speaker. The input is the audio data, and the output is the audio provided to the user. Specifically, the process involves outputting the audio data through the terminal's headset or speaker.

[1924] Step 6:

[1925] The server uses an emotion analysis engine to analyze the user's facial expressions and voice tone to recognize their emotional state. The input is the user's facial expression data and voice tone data, and the output is the recognized emotional state data. Specifically, it performs real-time analysis using an emotion analysis engine (e.g., Microsoft Azure Emotion API). This emotional state data is sent to the server, and appropriate feedback is provided as needed.

[1926] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1927] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1928] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1929] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1930] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1931] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1932] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1933] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1934] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1935] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1936] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1937] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1938] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1939] 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.

[1940] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1941] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1942] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1943] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1944] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1945] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1946] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1947] The following is further disclosed regarding the embodiments described above.

[1948] (Claim 1)

[1949] A means of tracking the user's gaze,

[1950] A means for acquiring video footage of an object that the user is focusing on,

[1951] A lip motion analysis means analyzes the movement of the target's lips from video acquired by a video acquisition means and generates text data,

[1952] A speech synthesis means that converts generated text data into speech,

[1953] A voice output means that provides the user with voice generated by a voice synthesis means,

[1954] A system that includes this.

[1955] (Claim 2)

[1956] The eye-tracking means is comprised of a camera built into smart glasses, according to claim 1.

[1957] (Claim 3)

[1958] The lip movement analysis means analyzes lip movements using a generative AI model and generates text data according to claim 1.

[1959] (Claim 4)

[1960] The system according to claim 1, wherein the audio output means is comprised of a headset or a speaker.

[1961] "Example 1"

[1962] (Claim 1)

[1963] A means of tracking the user's gaze,

[1964] A means for acquiring video footage of an object that the user is focusing on,

[1965] A lip motion analysis means analyzes the movement of the target's lips from video acquired by a video acquisition means and generates text data,

[1966] A speech synthesis means that converts generated text data into speech,

[1967] A voice output means that provides the user with voice generated by a voice synthesis means,

[1968] A system that includes this.

[1969] (Claim 2)

[1970] The eye-tracking means is comprised of a camera built into a wearable device, according to claim 1.

[1971] (Claim 3)

[1972] The lip movement analysis means analyzes lip movements using a machine learning model and generates text data according to claim 1.

[1973] "Application Example 1"

[1974] (Claim 1)

[1975] A means of tracking the user's gaze,

[1976] A means for acquiring video footage of an object that the user is focusing on,

[1977] A lip motion analysis means analyzes the movement of the target's lips from video acquired by a video acquisition means and generates text data,

[1978] A speech synthesis means that converts generated text data into speech,

[1979] A voice output means that provides the user with voice generated by a voice synthesis means,

[1980] An automated means that transmits generated voice data to a machine and executes instructions,

[1981] A system that includes this.

[1982] (Claim 2)

[1983] The eye-tracking means is comprised of a camera embedded in a wearable display, according to claim 1.

[1984] (Claim 3)

[1985] The lip movement analysis means analyzes lip movements using a generative AI model and generates text data according to claim 1.

[1986] "Example 2 of combining an emotion engine"

[1987] (Claim 1)

[1988] A means of tracking the user's gaze,

[1989] A means for acquiring video footage of an object that the user is focusing on,

[1990] A lip motion analysis means analyzes the movement of the target's lips from video acquired by a video acquisition means and generates text data,

[1991] A speech synthesis means that converts generated text data into speech,

[1992] A voice output means that provides the user with voice generated by a voice synthesis means,

[1993] A means of emotion recognition that analyzes emotions,

[1994] A system that includes this.

[1995] (Claim 2)

[1996] The eye-tracking means is comprised of an image sensor built into a head-mounted display device, according to claim 1.

[1997] (Claim 3)

[1998] The lip motion analysis means analyzes lip movements using a generative model and generates text data according to claim 1.

[1999] "Application example 2 of combining emotional engines"

[2000] (Claim 1)

[2001] A means of tracking the user's gaze,

[2002] A means for acquiring video footage of an object that the user is focusing on,

[2003] A lip motion analysis means analyzes the movement of the target's lips from video acquired by a video acquisition means and generates text data,

[2004] A speech synthesis means that converts generated text data into speech,

[2005] A voice output means that provides the user with voice generated by a voice synthesis means,

[2006] An emotion recognition method that analyzes the user's facial expressions and voice tone to recognize their emotional state,

[2007] A system that includes this.

[2008] (Claim 2)

[2009] The eye-tracking means is comprised of a camera built into a wearable device, according to claim 1.

[2010] (Claim 3)

[2011] The lip movement analysis means analyzes lip movements using a generative AI model and generates text data according to claim 1. [Explanation of Symbols]

[2012] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of tracking the user's gaze, A means for acquiring video footage of an object that the user is focusing on, A lip motion analysis means analyzes the movement of the target's lips from video acquired by a video acquisition means and generates text data, A speech synthesis means that converts generated text data into speech, A voice output means that provides the user with voice generated by a voice synthesis means, A system that includes this.

2. The eye-tracking means is comprised of a camera built into smart glasses, according to claim 1.

3. The lip movement analysis means analyzes lip movements using a generative AI model and generates text data according to claim 1.

4. The system according to claim 1, wherein the audio output means is comprised of a headset or a speaker.

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