System

Abstract

A system is provided.SOLUTION: A system comprising: means for converting a user's voice to text in real-time; means for projecting the converted text to a display; means for displaying a plurality of response options and allowing a user to select any response; and means for speech synthesis and playing the selected response.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] For people who struggle with communication, situations such as face-to-face conversations and ordering are often a major source of stress and a social barrier. Furthermore, existing technologies have too many functions, making them difficult to use and preventing users from focusing on the functions they need. There is a need for a system that can solve these issues and enable users to communicate smoothly. [Means for solving the problem]

[0005] The system includes a means for converting a user's voice into text in real time, a means for projecting the converted text onto a display device, a means for displaying multiple response options and allowing the user to select a desired response, and a means for synthesizing and playing back the selected response, allowing users to communicate smoothly without speaking. Furthermore, the system uses a means for collecting and preprocessing voice data and a sensor for detecting frame swipe operations, enabling more comfortable operation.

[0006] "User" refers to an individual who uses this system.

[0007] "Real-time" refers to near-instant processing with no delay.

[0008] "Text" refers to a string of characters converted from audio data.

[0009] "Conversion" refers to the process of converting audio data into text.

[0010] "Display device" refers to a unit that displays information on a device such as glasses.

[0011] "Projection" refers to the act of displaying text or images on a display device.

[0012] "Response Options" refers to multiple pre-set replies that a User can choose from.

[0013] "Speech synthesis" refers to the technology of converting text data into voice data.

[0014] "Playback" refers to outputting audio data via a speaker or the like.

[0015] "Voice Data" refers to raw data collected from a user's voice.

[0016] "Preprocessing" refers to processing that removes noise and normalizes the volume of audio data.

[0017] "Sensor" refers to the hardware that detects user touches and swipes. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The present invention is a system for supporting users in smoothly carrying out communication. An embodiment of this system and the processing of its program will be described below.

[0040] System Configuration

[0041] This system consists of the following main components:

[0042] 1. Terminal: Smart glasses worn by the user, a device for voice input, display, and swiping operations.

[0043] 2. Speech recognition module: A software component that converts voice data collected on the device into text data.

[0044] 3. Microprojector: A hardware component installed in the device that projects text data onto the glasses' display.

[0045] 4. Speech synthesis module: A software component that converts text data into speech data and plays it through the built-in speaker.

[0046] 5. Sensor: A hardware component that detects user swipes.

[0047] Program processing flow

[0048] The program executes the process in the following steps:

[0049] 1. Collecting voice input

[0050] The device uses a built-in microphone to collect the user's voice. For example, when the user says, "I'd like to order," the voice data is stored in a buffer.

[0051] 2. Preprocessing of audio data

[0052] The terminal performs preprocessing on the collected audio data to remove noise and normalize the volume.

[0053] 3. Voice Recognition

[0054] The device passes the preprocessed voice data to a voice recognition module, which converts it into text data. For example, the generated text is "Please place an order."

[0055] 4. Text Projection

[0056] The device sends the generated text data to a microprojector, which projects it onto the display of the glasses, where the user can see the text "Please place your order."

[0057] 5. Response Selection

[0058] The device displays multiple response options on the Glass, and the user selects the appropriate response by swiping the frame, for example, "Thank you."

[0059] 6. Text-to-speech

[0060] The device passes the user's selected text to a speech synthesis module, converts it into audio data, and plays the voice saying "Thank you" using the built-in speaker.

[0061] 7. Prepare for the next input

[0062] After completing this series of processes, the device returns to standby mode and is ready for the next voice input.

[0063] Specific examples

[0064] Ordering at a restaurant

[0065] 1. Voice Input: The user says, "One cheeseburger, please."

[0066] 2. Speech recognition: The device converts this speech into text, generating the text "One cheeseburger please."

[0067] 3. Text projection: The device projects the generated text onto the glasses' display.

[0068] 4. Response selection: User swipes the frame to select "Thank you."

[0069] 5. Play audio: The device will play "Thank you" aloud.

[0070] 6. Standby: The device returns to standby mode to prepare for the next voice input.

[0071] In this way, the system allows users to communicate naturally without speaking, reducing the stress of face-to-face conversations and ordering.The system can be used in a similar process in other scenarios, making it widely applicable.

[0072] The processing flow will be explained below.

[0073] Step 1:

[0074] The device uses a built-in microphone to collect the user's voice in real time. For example, when the user says, "I'd like to place an order," the voice data is recorded.

[0075] Step 2:

[0076] The device pre-processes the collected audio data for noise cancellation and volume normalization, which improves the accuracy of the audio data.

[0077] Step 3:

[0078] The device then passes the pre-processed voice data to the speech recognition module, which uses the latest edge computing AI to convert the speech into text, such as "Please place an order."

[0079] Step 4:

[0080] The device receives the text data output from the voice recognition module and sends it to a microprojector, which projects the text onto the display of the glasses. The user can see the text "Please place your order" on the display.

[0081] Step 5:

[0082] The device displays multiple response options on the display for the user to choose from, and the user can select the desired response by swiping the frame.

[0083] Step 6:

[0084] The device uses sensors to detect swiping on the frame and identify the response selected by the user, for example, selecting "Thank you."

[0085] Step 7:

[0086] The terminal passes the selected text "Thank you" to the speech synthesis module, which converts it into voice data. The speech synthesis module generates "Thank you" in a natural speaking voice.

[0087] Step 8:

[0088] The device will play a synthesized voice of "Thank you" through the built-in speaker, allowing users to say "Thank you" silently.

[0089] Step 9:

[0090] After the process is complete, the device will return to standby mode for the next voice input. If the user speaks again, the system will be ready to recognize the input immediately.

[0091] Example 1

[0092] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0093] Conventional voice input systems often suffer from poor voice recognition accuracy, especially in noisy environments, preventing users from operating as intended. Users often experience inconvenience when confirmation or manual operation is required after voice input. There is a need for a system that can solve these problems and enable users to use voice input without stress.

[0094] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0095] In this invention, the server includes means for converting a user's voice into text in real time, means for projecting the converted text onto a display device, means for displaying multiple response options and allowing the user to select a desired response, means for synthesizing and playing back the selected response, preprocessing means for noise reduction and volume normalization of the voice data, and projection means for projecting the text onto a display device, thereby enabling highly accurate voice recognition and stress-free usability.

[0096] "User" refers to the individual who operates the system and provides instructions and input.

[0097] "Real-time speech-to-text conversion means" refers to technology or devices that instantly convert a user's voice input into text data.

[0098] "Means for projecting the converted text onto a display device" refers to a device, such as a projector or screen, that visually displays the text data generated by speech.

[0099] "Response options" refers to multiple possible responses or actions that a user can choose from.

[0100] "Means for synthesizing and playing back speech" refers to technology or devices that convert text data into speech and play it back in a format that can be heard by the user.

[0101] "Pre-processing means for noise reduction and volume normalization" refers to techniques and devices for removing unwanted noise from audio signals and for maintaining a consistent volume level.

[0102] "Projection means" refers to the technology or equipment used to project digital data onto a physical display device.

[0103] "Sensor for detecting swiping of a frame" refers to a sensor device for detecting a swipe action performed by a user on a frame.

[0104] This invention is a system for helping users communicate smoothly. It converts the user's speech into text in real time, projects the text on a display device, and displays multiple response options. The response selected by the user is played back as a synthesized voice.

[0105] System Configuration

[0106] This system consists of the following main components:

[0107] 1. Terminal: Smart glasses worn by the user, a device for voice input, display, and swiping operations.

[0108] 2. Speech recognition module: A software component that converts voice data collected on the device into text data.

[0109] 3. Microprojector: A hardware component installed in the device that projects text data onto the glasses' display.

[0110] 4. Speech synthesis module: A software component that converts text data into speech data and plays it through the built-in speaker.

[0111] 5. Sensor: A hardware component that detects user swipes.

[0112] Program processing flow

[0113] Collecting voice input

[0114] The device uses a built-in microphone to collect the user's voice. For example, when the user says, "I'd like to place an order," this voice data is temporarily stored in a buffer.

[0115] Audio data preprocessing

[0116] The device preprocesses the collected audio data to remove noise and normalize the volume, which is important to ensure accurate speech recognition.

[0117] Voice Recognition

[0118] The device passes the preprocessed voice data to a voice recognition module, which converts it into text data. For example, the generated text is "Please place an order."

[0119] Text projection

[0120] The device sends the generated text data to a micro-projector and projects it onto the user's smart glasses display, where the user can see the text "Please place your order."

[0121] Response Selection

[0122] The device displays multiple response options on the display, and the user swipes the frame to select the appropriate response, such as "Thank you."

[0123] Text to speech

[0124] The device passes the user's selected text to a speech synthesis module, converts it into audio data, and plays the voice saying "Thank you" using the built-in speaker.

[0125] Prepare for the next input

[0126] After completing the series of processes, the device returns to a standby state to prepare for the next voice input, and the device is ready for the user to input voice again.

[0127] Specific examples

[0128] Ordering at a restaurant

[0129] 1. Voice Input: The user says, "One cheeseburger, please."

[0130] 2. Speech recognition: The device converts this speech into text, generating the text "One cheeseburger please."

[0131] 3. Text projection: The device projects the generated text onto the glasses' display.

[0132] 4. Response selection: User swipes the frame to select "Thank you."

[0133] 5. Play audio: The device will play "Thank you" aloud.

[0134] 6. Standby: The device returns to standby mode to prepare for the next voice input.

[0135] Prompt Sentence Examples

[0136] "Please explain in detail each processing step of your system, especially how you use voice data preprocessing and speech recognition techniques."

[0137] "Please explain specifically how the device collects and processes voice input and provides feedback to the user."

[0138] In this way, the system allows users to communicate naturally without speaking, reducing the stress of face-to-face conversations and ordering.The system can be used in a similar process in other scenarios, making it widely applicable.

[0139] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0140] Step 1: Collecting voice input

[0141] The device uses a built-in microphone to collect the user's voice.

[0142] Input: User speech (e.g., "Please place my order")

[0143] Data processing: Storing audio data in a buffer

[0144] Output: Accumulated audio data

[0145] Specifically, the device's microphone is always on, and when voice input is detected, the voice data is stored in a buffer.

[0146] Step 2: Preprocessing the audio data

[0147] The device preprocesses the collected audio data to remove noise and normalize the volume.

[0148] Input: Stored voice data

[0149] Data processing: noise removal, volume normalization

[0150] Output: Preprocessed audio data

[0151] Specifically, the terminal uses an audio filtering algorithm to filter out background noise and adjust the volume to a consistent level.

[0152] Step 3: Voice Recognition

[0153] The terminal passes the pre-processed voice data to a voice recognition module and converts it into text data.

[0154] Input: Preprocessed audio data

[0155] Data calculation: The process of converting voice data into text data

[0156] Output: The converted text (e.g. "Please place your order").

[0157] Specifically, the device uses an API (e.g., Google Speech-to-Text API) to convert voice to text.

[0158] Step 4: Projecting text

[0159] The terminal transmits the generated text data to the micro projector and projects it onto the display.

[0160] Input: The converted text (e.g., "Please place my order").

[0161] Data processing: Formatting text data for display

[0162] Output: Text projected on a display

[0163] Specifically, the device formats the text for projection via a dedicated display driver and projects it onto the smart glasses display.

[0164] Step 5: Select a response

[0165] The terminal displays a number of response options on the display, and the user selects any one of them.

[0166] Input: Response options (e.g., "Thank you" or "Cancel my order")

[0167] Data processing: Display response options on the display

[0168] Output: The response selected by the user (e.g. "Thank you")

[0169] Specifically, sensors embedded in the frame detect the user's swiping actions and select a response based on that information.

[0170] Step 6: Read text aloud

[0171] The terminal passes the selected text to a speech synthesis module to convert it into voice data and play it back.

[0172] Input: Selected response text (e.g. "Thank you")

[0173] Data calculation: The process of converting text data into audio data

[0174] Output: The audio to be played (e.g. "Thank you")

[0175] Specifically, the device uses an API (e.g., Google Text-to-Speech API) to convert text into speech and plays it through the built-in speaker.

[0176] Step 7: Prepare for the next input

[0177] After completing the series of processes, the terminal returns to a standby state to prepare for the next voice input.

[0178] Input: None (standby)

[0179] Data processing: Wait for next voice input

[0180] Output: Ready and waiting

[0181] Specifically, the system returns to the voice input mode and waits for the input trigger word again.

[0182] (Application example 1)

[0183] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0184] Conventional communication support systems have the problem that the process of converting speech to text and allowing users to select their own responses places a heavy burden on users, making it difficult to maintain a smooth dialogue. Furthermore, the limited knowledge required to generate appropriate responses makes it difficult to respond flexibly. For these reasons, there is a growing need for a system that allows users to continue a dialogue naturally and provides appropriate responses instantly.

[0185] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0186] In this invention, the server includes means for converting a user's voice into text in real time, means for projecting the converted text onto a display device, means for displaying multiple response options and allowing the user to select a desired response, means for synthesizing the selected response and playing it back, and means for generating a response based on a prompt sentence, including a generative AI model for generating an appropriate response based on the content of the user's voice. This allows the user to instantly obtain an appropriate response while smoothly engaging in dialogue.

[0187] "User" refers to the person who operates the system and provides voice input.

[0188] "Means for converting speech to text in real time" refers to a component that has the functionality to instantly convert a user's speech data into text data.

[0189] "Display device" means a device for visually presenting textual data and response options to a user.

[0190] The "means for projecting" refers to a function for displaying text data on a display device.

[0191] "Multiple response options" refers to a set of selectable responses that the system offers to the user.

[0192] "Means for selection" refers to a component that has the functionality to allow a user to select any response from among multiple response options.

[0193] "Means for synthesizing and reproducing speech" refers to a component that has the function of converting selected text data into speech data and reproducing it.

[0194] "Generative AI model" refers to an artificial intelligence module that generates appropriate responses based on the content of a user's voice.

[0195] A "prompt sentence" is text data input into a generative AI model, and refers to an instruction sentence used to derive an appropriate response.

[0196] A system for realizing this application example includes the following components and means:

[0197] First, the user wears a display device called smart glasses. These smart glasses have built-in microphones and speakers, and are equipped with a means for collecting voice input. Voice data spoken by the user is collected through the built-in microphone and converted into text in real time. High recognition accuracy is achieved by using the Google Speech Recognition API for voice recognition. The converted text data is instantly projected onto the smart glasses' display. The display device is equipped with a projection means to provide a visual representation of the text data.

[0198] Next, multiple response options are generated based on the speech content and displayed on the display. The response options are generated using a generative AI model. This generative AI model creates a prompt based on the speech content of the user and derives an appropriate response based on the prompt. The user can select any response from the multiple response options by swiping the touch frame of the smart glasses. This selection method prepares the selected response for voice synthesis and playback. The selected response is then converted into voice data using speech synthesis technology and played through the built-in speaker. Software called pyttsx3 is used for speech synthesis.

[0199] In this system, the user's voice data is pre-processed to remove noise and normalize the volume, which allows for more accurate voice recognition, allowing users to continue dialogue easily and quickly, improving work efficiency.

[0200] Hardware and Software Examples

[0201] Smart glasses: Equipped with a display, microphone, speaker, and touch sensor.

[0202] Speech recognition software: Google Speech Recognition API.

[0203] Speech synthesis software: pyttsx3.

[0204] Natural Language Processing model: OpenAI GPT-3.

[0205] Specific examples

[0206] Situation: A customer orders, "One cheeseburger, please."

[0207] System response: "Your order has been accepted. Thank you."

[0208] Prompt Sentence Examples

[0209] Generate appropriate responses to customer orders.

[0210] Order: One cheeseburger, please

[0211] Example response:

[0212] This will generate a natural-sounding response such as "Your order has been accepted. Thank you."

[0213] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0214] Step 1:

[0215] The device collects the user's voice using a built-in microphone. When the user speaks, the device stores the voice data in a buffer. The input of this step is the user's voice, and the output is the collected voice data.

[0216] Step 2:

[0217] The terminal processes the collected audio data using preprocessing means that perform noise reduction and volume normalization. The input to this step is the audio data collected in step 1, and the output is audio data with noise reduction and volume normalization.

[0218] Step 3:

[0219] The server passes the preprocessed audio data to the Google Speech Recognition API and converts it into text data. The input is noise-removed and volume-normalized audio data, and the output is text data.

[0220] Step 4:

[0221] The server inputs a prompt sentence to the generative AI model based on the text data. For example, a prompt sentence such as "Please generate an appropriate response to the customer's order. Order: One cheeseburger, please" is input to the generative AI model. The inputs to this step are the speech-recognized text data and the generated prompt sentence, and the output is the generated response text.

[0222] Step 5:

[0223] The server sends the generated response text to the terminal, which projects multiple response options onto a display device. The user selects an appropriate response by swiping the touch frame of the smart glasses. The input of this step is the generated response text, and the output is the response option selected by the user.

[0224] Step 6:

[0225] The server passes the user-selected response text to a speech synthesis module, which converts it into speech data. The input is the user-selected response text, and the output is the synthesized speech data.

[0226] Step 7:

[0227] The device plays the synthesized voice data over the built-in speaker, allowing the user to confirm the selected response as voice. The input of this step is the synthesized voice data, and the output is the played voice.

[0228] Through the above processing steps, users can have natural conversations and continue communication smoothly.

[0229] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0230] This invention is a system incorporating an emotion engine to assist users in smooth communication. An embodiment of this system and the processing of its program will be described below.

[0231] System Configuration

[0232] This system consists of the following main components:

[0233] 1. Terminal: Smart glasses worn by the user, a device for voice input, display, and swiping operations.

[0234] 2. Speech recognition module: A software component that converts voice data collected on the device into text data.

[0235] 3. Microprojector: A hardware component installed in the device that projects text data onto the glasses' display.

[0236] 4. Speech synthesis module: A software component that converts text data into speech data and plays it through the built-in speaker.

[0237] 5. Sensor: A hardware component that detects user swipes.

[0238] 6. Emotion Engine: A component for real-time emotion recognition from the user's voice, facial expressions, and other biometric data.

[0239] Program processing flow

[0240] The program executes the process in the following steps:

[0241] 1. Collecting voice input

[0242] The device uses a built-in microphone to collect the user's voice in real time. For example, when the user says, "I'd like to place an order," the voice data is recorded.

[0243] 2. Preprocessing of audio data

[0244] The device pre-processes the collected audio data for noise cancellation and volume normalization, which improves the accuracy of the audio data.

[0245] 3. Voice Recognition

[0246] The device then passes the pre-processed voice data to the speech recognition module, which uses the latest edge computing AI to convert the speech into text, such as "Please place an order."

[0247] 4. Text Projection

[0248] The device receives the text data output from the voice recognition module and sends it to a microprojector, which projects the text onto the display of the glasses. The user can see the text "Please place your order" on the display.

[0249] 5. Emotion recognition

[0250] The device sends collected voice and other biometric data to the emotion engine, which analyzes in real time whether the user is expressing any emotion. For example, if the user is likely to be nervous, that emotional state is identified.

[0251] 6. Response Selection

[0252] The device displays appropriate response options based on the user's emotional state, and the user can select the desired response by swiping the frame.

[0253] 7. Text-to-speech

[0254] The device passes the user's selected response to a speech synthesis module, which converts it into voice data. The speech synthesis module then plays the response in a tone that adapts to the user's emotional state. For example, it might play "thank you" in a gentle voice.

[0255] 8. Prepare for the next input

[0256] After the process is complete, the device will return to standby mode for the next voice input. If the user speaks again, the system will be ready to recognize the input immediately.

[0257] Specific examples

[0258] Ordering at a restaurant

[0259] 1. Voice Input: The user says, "One cheeseburger, please."

[0260] 2. Speech recognition: The device converts this speech into text, generating the text "One cheeseburger please."

[0261] 3. Text projection: The device projects the generated text onto the glasses' display.

[0262] 4. Emotion recognition: The device analyzes the user's tense tone of voice, and the emotion engine detects the sense of tension.

[0263] 5. Response selection: User swipes the frame to select "Thank you."

[0264] 6. Voice Playback: The device uses the emotion engine to play back a voice saying "Thank you" in a gentle tone to ease tension.

[0265] 7. Standby: The device returns to standby mode for the next voice input.

[0266] In this way, the system analyzes the user's emotional state and provides appropriate responses, supporting more natural and smooth communication.The same process can be used in other scenarios, making it widely applicable.

[0267] The processing flow will be explained below.

[0268] Step 1:

[0269] The device uses a built-in microphone to collect the user's voice in real time. For example, if the user says, "I'd like a cheeseburger, please," the voice data will be recorded.

[0270] Step 2:

[0271] The device pre-processes the collected audio data for noise cancellation and volume normalization, which improves the accuracy of the audio data.

[0272] Step 3:

[0273] The device then passes the pre-processed voice data to a speech recognition module, which uses the latest edge computing AI to convert the speech into text, such as "One cheeseburger, please."

[0274] Step 4:

[0275] The device receives the text data output from the voice recognition module and sends it to the microprojector, which projects the text onto the display of the glasses. The user can see the text "One cheeseburger, please" on the display.

[0276] Step 5:

[0277] The device sends the collected voice data and other biometric data to the emotion engine, which analyzes the user's emotional state in real time, such as whether they are nervous or relaxed. For example, tension can be detected from the user's tone of voice and speaking style.

[0278] Step 6:

[0279] The device displays multiple appropriate response options based on the emotional state recognized by the emotion engine. If the emotion engine detects the user's tension, it displays response options to relax them. The user swipes the frame to select a response, such as "Thank you."

[0280] Step 7:

[0281] The device uses sensors to detect swiping on the frame, identifies the user's selected response, and passes the selected text "Thank you" to a speech synthesis module for conversion into voice data.

[0282] Step 8:

[0283] The device uses an emotion engine to play a synthesized "thank you" in a tone that adapts to the user's emotional state. For example, if the user is nervous, the device will play a "thank you" in a calm, gentle voice.

[0284] Step 9:

[0285] After the process is complete, the device will return to standby mode for the next voice input. If the user speaks again, the system will be ready to recognize the input immediately.

[0286] This process allows the system to take the user's emotions into account and provide appropriate responses. For example, if the device detects nervousness when ordering at a restaurant, it will respond with a voice response in a relaxing tone, allowing the user to communicate with ease.

[0287] Example 2

[0288] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0289] Conventional speech recognition systems simply convert speech into text without understanding the user's emotions, making it difficult to provide appropriate responses. This can lead to awkward communication and makes it difficult to provide appropriate assistance to users who are particularly nervous. Furthermore, few systems consistently perform advanced processing, such as preprocessing of speech data and real-time emotion analysis.

[0290] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0291] In this invention, the server includes means for converting a user's voice into text in real time, means for projecting the converted text onto a display device, and preprocessing means for collecting the user's voice data and performing noise reduction and volume normalization. This enables the user's voice to be recognized and converted into text with high accuracy. The server also includes means for analyzing the user's emotions in real time and means for providing appropriate response options based on the analyzed emotions. This allows the server to understand the user's emotional state and provide appropriate responses accordingly, enabling smooth communication.

[0292] "User" means any person who uses the System.

[0293] "Voice" refers to the words or voices spoken by the user.

[0294] "Real time" refers to a state in which processing is performed almost simultaneously.

[0295] "Text" refers to the text information converted from audio data.

[0296] "Means" refers to a device or program that performs a particular function or role.

[0297] "Display device" means a device that visually presents information to a user.

[0298] "Projection" refers to displaying the generated text on a display device.

[0299] "Multiple response options" refers to multiple responses that a user can choose from.

[0300] "Selection" refers to the act of a user choosing one option from multiple options.

[0301] "Speech synthesis" refers to the technology of converting text data into voice data.

[0302] "Playback" refers to outputting synthesized sound through a speaker or the like.

[0303] "Emotion" refers to the user's psychological state (e.g., tension, joy, sadness, etc.).

[0304] "Analysis" refers to extracting and understanding information from collected data.

[0305] "Providing" refers to the act of making information or functionality available to users.

[0306] "Noise reduction" refers to the technology of removing unnecessary noise from audio data.

[0307] "Volume normalization" refers to a technique for adjusting the volume of audio data to a constant level.

[0308] "Preprocessing" refers to the preparation and formatting of data before the main processing.

[0309] "Sensor" refers to a device that detects user operations and situations.

[0310] A "swipe" refers to the action of a user sliding their finger across the surface of a display or device.

[0311] This invention is a system that converts voice input into text, recognizes emotions, presents response options, and plays synthesized speech using a smart glass-type device worn by the user. This system uses the following hardware and software to achieve specific functions.

[0312] Hardware Configuration

[0313] 1. Device: Smart glasses worn by the user, containing a built-in microphone, display, microprojector, and sensors.

[0314] 2. Micro-projector: Installed in the device, it projects the converted text onto the glasses' display.

[0315] 3. Sensor: A device that detects swiping operations and recognizes user actions.

[0316] 4. Speaker: Used to play the voice output from the speech synthesis module.

[0317] Software Configuration

[0318] 1. Speech Recognition Module: Software for converting user speech into text, for example, using edge computing AI technology (e.g., Google Speech-to-Text API).

[0319] 2. Emotion Engine: Software for analyzing user emotions in real time, using Affectiva SDK as an example.

[0320] 3. Speech synthesis module: Software for converting text data into speech data. We use Amazon Polly as an example.

[0321] 4. Preprocessing program: Software that denoises and normalizes the volume of audio data, using the Spectral Subtraction algorithm as an example.

[0322] Specific examples of processing

[0323] The operation of this system will be explained based on a specific example.

[0324] Ordering at a restaurant

[0325] 1. Voice Input: The user says, "One cheeseburger, please."

[0326] 2. Audio data preprocessing: The device performs noise cancellation and volume normalization on this audio data.

[0327] 3. Speech recognition: The device passes the preprocessed speech data to the Google Speech-to-Text API, generating text data such as "One cheeseburger, please."

[0328] 4. Text projection: The device sends the generated text data to the micro-projector, which projects it onto the smart glasses display.

[0329] 5. Emotion Recognition: The device analyzes voice and biometric data using the Affectiva SDK to detect when the user is nervous.

[0330] 6. Response selection: The user swipes to select "Thank you" from the response options displayed on the display.

[0331] 7. Play Voice: The device converts the text to speech using Amazon Polly and plays "Thank you" in a calming tone to ease tension.

[0332] 8. Prepare for next input: The device returns to standby mode for the next voice input.

[0333] Prompt Sentence Examples

[0334] "Please explain in detail the processing steps of an emotion recognition and response system for a nervous user when ordering at a restaurant."

[0335] As described above, this system integrates hardware and software to convert a user's voice into text with high accuracy and provide appropriate responses according to their emotions.

[0336] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0337] Step 1:

[0338] Collecting voice input

[0339] The device uses a built-in microphone to collect the user's voice in real time. For example, when the user says, "I'd like to place an order," the voice data is collected and input into the next step.

[0340] Step 2:

[0341] Audio data preprocessing

[0342] The device performs noise reduction and volume normalization on the collected audio data. Specifically, it applies a noise reduction algorithm (e.g., Spectral Subtraction) to reduce background noise. At the same time, it normalizes volume peaks to ensure that all sounds are at a comfortable level to hear. This preprocessed audio data is then input to the next step.

[0343] Step 3:

[0344] Voice Recognition

[0345] The device passes the preprocessed voice data to a speech recognition module (e.g., Google Speech-to-Text API) to convert the voice into text data. This conversion generates the text data "Please place your order." This text data is input to the next step.

[0346] Step 4:

[0347] Text projection

[0348] The terminal sends the text data obtained from the voice recognition module to the micro-projector, which projects the text onto the smart glasses' display. Specifically, the text "Please place your order" is visually displayed on the display. This displayed text is input into the next step.

[0349] Step 5:

[0350] emotion recognition

[0351] The device sends collected voice data and other biometric data (e.g., heart rate, galvanic skin response) to an emotion engine (e.g., Affectiva SDK). The emotion engine analyzes this data and identifies the user's emotional state (e.g., nervousness, joy, sadness, etc.) in real time. For example, if the user is nervous, that emotional state is identified. This emotional state data is input into the next step.

[0352] Step 6:

[0353] Response Selection

[0354] Based on the analysis results from the emotion engine, the device displays appropriate response options on the display. Specifically, options such as "Thank you" or "Please wait a little longer" are displayed on the display. The user selects the desired response option by swiping the frame. This selected response option is input into the next step.

[0355] Step 7:

[0356] Text to speech

[0357] The device sends the selected response to a speech synthesis module (e.g., Amazon Polly) to convert the text data into speech. Based on the analysis results of the emotion engine, the speech synthesis module plays a response with the corresponding emotional tone. Specifically, it plays "Thank you" in a gentle tone to ease tension. This played speech is then output to the next step.

[0358] Step 8:

[0359] Prepare for the next input

[0360] After all processing is completed, the device returns to standby mode for the next voice input. When the user speaks again, the system immediately collects the voice and is ready to resume processing from the first step.

[0361] (Application example 2)

[0362] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0363] Conventional communication support systems provided the ability to convert a user's voice into text in real time and display the text, but lacked the ability to identify the emotions of the user or the person they were speaking to and select and provide an appropriate response. This resulted in issues such as communication not proceeding smoothly when the user or customer was nervous or an inappropriate response was selected. Furthermore, the process of synthesizing and playing back the selected response was often unnatural, which could detract from the user experience.

[0364] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for converting a user's voice into text in real time, means for projecting the converted text on a display device, means for displaying multiple response options and allowing the user to select any response, means for synthesizing the selected response and playing it back, means for analyzing the voice data of the user and the conversation partner and identifying emotions, and means for providing appropriate response options based on the identified emotions. This makes it possible to grasp the emotional states of the user and the conversation partner and select and provide appropriate responses, thereby making communication smoother and improving the user experience.

[0365] "User" means any person who operates the System.

[0366] "Real-time speech-to-text means" means a technical method for instantly converting speech uttered by a User into text form.

[0367] "Means for projecting the converted text onto a display device" refers to a technical method for displaying audio data converted into text format on a display device such as a display.

[0368] "A means of displaying multiple response options and allowing the user to select any response" refers to a technical method by which the system displays several pre-prepared response options and the user selects the one they deem appropriate.

[0369] "Means for synthesizing and playing the selected response" means a technical means for synthesizing a text response selected by a user as speech and playing that speech.

[0370] "Means for analyzing the voice data of the user and the conversation partner and identifying emotions" refers to a technical method for analyzing the voice data uttered by the user and the conversation partner and identifying the emotions contained in the voice.

[0371] "Means for providing appropriate response options based on the identified emotion" refers to a technical method by which the system presents response options appropriate to the emotional state determined by the analysis.

[0372] This invention is a system designed to assist users in smoothly carrying out communication. A specific implementation method of this system will be described below.

[0373] System hardware configuration

[0374] The system mainly consists of the following hardware components:

[0375] 1. Smart Glasses:

[0376] Built-in microphone

[0377] Display (micro projector)

[0378] Sensor that detects swipe operations

[0379] 2. Server:

[0380] Voice Recognition Module

[0381] Emotion Engine

[0382] Speech Synthesis Module

[0383] Software Components

[0384] The system consists of the following software components:

[0385] 1. Speech Recognition Module:

[0386] Software for converting speech to text in real time. For example, you can use the speech_recognition library.

[0387] 2. Emotion Engine:

[0388] Software that identifies emotions from voice and other biometric data, using machine learning models and APIs.

[0389] 3. Speech synthesis module:

[0390] Software for converting text to speech. For example, the gTTS library can be used.

[0391] System Operation

[0392] 1. Collecting voice input:

[0393] The server collects the user's voice in real time using the smart glasses' built-in microphone.

[0394] 2. Audio preprocessing:

[0395] The server pre-processes the collected audio data for noise cancellation and volume normalization.

[0396] 3. Speech Recognition:

[0397] The server passes the preprocessed speech data to a speech recognition module and converts it into text.

[0398] 4. Emotion recognition:

[0399] The server passes the voice data to an emotion engine to identify the emotions of the user and the conversation partner.

[0400] 5. Response Selection:

[0401] The server displays appropriate response options on the smart glasses display based on the identified emotion.

[0402] 6. Response speech synthesis:

[0403] The server passes the user's selected text response to a speech synthesis module, which converts it into speech and plays it back.

[0404] Specific examples

[0405] Restaurant service

[0406] 1. The staff member says, "I have a question about the menu."

[0407] 2. The smart glasses collect the audio and send it to the server.

[0408] 3. The server converts the speech into text and displays, "I have a question about the menu."

[0409] 4. The emotion engine detects customer tension.

[0410] 5. The server suggests appropriate response options: "Please feel free to ask any questions" and "Don't be nervous, it's okay."

[0411] 6. The staff member will select "Please ask any questions" and play it back using voice synthesis.

[0412] Prompt Sentence Examples

[0413] Customer: "I have a question about the menu."

[0414] System: "Tension detected"

[0415] System: "You have the following response options: 1. Please feel free to ask any questions. 2. Don't be nervous, it's okay."

[0416] The staff member selected "Please ask questions."

[0417] The system will play a voice message saying "Please feel free to ask any questions."

[0418] In this way, the system analyzes the emotional state of the user and the person they are talking to in real time and provides appropriate response options, thereby enabling more natural and smooth communication.

[0419] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0420] Step 1:

[0421] The server collects the user's voice in real time using the smart glasses' built-in microphone. The input is the user's voice data, and the output is the raw voice data. This voice data becomes the basis for subsequent processing.

[0422] Step 2:

[0423] The server performs preprocessing of noise cancellation and volume normalization on the collected audio data. The input is the audio data obtained in step 1, and the output is the preprocessed audio data. Specifically, it removes background noise from the audio data and adjusts the volume to a certain level.

[0424] Step 3:

[0425] The server passes the preprocessed voice data to a voice recognition module, which converts the voice into text in real time. The input is the preprocessed voice data, and the output is text data. Specifically, a voice recognition library (e.g., speech_recognition) is used to generate text such as "I have a question about the menu."

[0426] Step 4:

[0427] The server passes the text data to the emotion engine to identify the emotions of the user and the person interacting with them. The input is text data and voice data, and the output is the identified emotional state (e.g., nervous). Specifically, an emotion recognition algorithm is used to determine emotions from the tone and content of the voice.

[0428] Step 5:

[0429] The server then displays appropriate response options on the smart glasses display based on the identified emotional state. The input is the emotional state and pre-prepared response options, and the output is the response options displayed on the display. Specifically, options such as "Please feel free to ask any questions" and "Don't be nervous, it's okay" are displayed.

[0430] Step 6:

[0431] The user swipes the frame of the smart glasses to select an appropriate response from the displayed response options. The input is the response option on the display, and the output is the selected response option. Specifically, a sensor detects the swipe action, and the selected option is sent to the server.

[0432] Step 7:

[0433] The server passes the selected response option to the speech synthesis module, converts the text into speech, and plays it back. The input is the text data of the selected response option, and the output is audio data. Specifically, the gTTS library is used to play back the phrase "Please ask any questions."

[0434] Step 8:

[0435] The server returns to standby mode to prepare for the next voice input. There is no particular input, and the output is in the system standby state. Specifically, the system returns to the state of waiting for voice input again.

[0436] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0437] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0438] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0439] [Second embodiment]

[0440] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0441] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0442] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0443] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0444] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0445] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0446] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0447] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0448] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0449] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0450] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0451] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0452] The present invention is a system for supporting users in smoothly carrying out communication. An embodiment of this system and the processing of its program will be described below.

[0453] System Configuration

[0454] This system consists of the following main components:

[0455] 1. Terminal: Smart glasses worn by the user, a device for voice input, display, and swiping operations.

[0456] 2. Speech recognition module: A software component that converts voice data collected on the device into text data.

[0457] 3. Microprojector: A hardware component installed in the device that projects text data onto the glasses' display.

[0458] 4. Speech synthesis module: A software component that converts text data into speech data and plays it through the built-in speaker.

[0459] 5. Sensor: A hardware component that detects user swipes.

[0460] Program processing flow

[0461] The program executes the process in the following steps:

[0462] 1. Collecting voice input

[0463] The device uses a built-in microphone to collect the user's voice. For example, when the user says, "I'd like to order," the voice data is stored in a buffer.

[0464] 2. Preprocessing of audio data

[0465] The terminal performs preprocessing on the collected audio data to remove noise and normalize the volume.

[0466] 3. Voice Recognition

[0467] The device passes the preprocessed voice data to a voice recognition module, which converts it into text data. For example, the generated text is "Please place an order."

[0468] 4. Text Projection

[0469] The device sends the generated text data to a microprojector, which projects it onto the display of the glasses, where the user can see the text "Please place your order."

[0470] 5. Response Selection

[0471] The device displays multiple response options on the Glass, and the user selects the appropriate response by swiping the frame, for example, "Thank you."

[0472] 6. Text-to-speech

[0473] The device passes the user's selected text to a speech synthesis module, converts it into audio data, and plays the voice saying "Thank you" using the built-in speaker.

[0474] 7. Prepare for the next input

[0475] After completing this series of processes, the device returns to standby mode and is ready for the next voice input.

[0476] Specific examples

[0477] Ordering at a restaurant

[0478] 1. Voice Input: The user says, "One cheeseburger, please."

[0479] 2. Speech recognition: The device converts this speech into text, generating the text "One cheeseburger please."

[0480] 3. Text projection: The device projects the generated text onto the glasses' display.

[0481] 4. Response selection: User swipes the frame to select "Thank you."

[0482] 5. Play audio: The device will play "Thank you" aloud.

[0483] 6. Standby: The device returns to standby mode to prepare for the next voice input.

[0484] In this way, the system allows users to communicate naturally without speaking, reducing the stress of face-to-face conversations and ordering.The system can be used in a similar process in other scenarios, making it widely applicable.

[0485] The processing flow will be explained below.

[0486] Step 1:

[0487] The device uses a built-in microphone to collect the user's voice in real time. For example, when the user says, "I'd like to place an order," the voice data is recorded.

[0488] Step 2:

[0489] The device pre-processes the collected audio data for noise cancellation and volume normalization, which improves the accuracy of the audio data.

[0490] Step 3:

[0491] The device then passes the pre-processed voice data to the speech recognition module, which uses the latest edge computing AI to convert the speech into text, such as "Please place an order."

[0492] Step 4:

[0493] The device receives the text data output from the voice recognition module and sends it to a microprojector, which projects the text onto the display of the glasses. The user can see the text "Please place your order" on the display.

[0494] Step 5:

[0495] The device displays multiple response options on the display for the user to choose from, and the user can select the desired response by swiping the frame.

[0496] Step 6:

[0497] The device uses sensors to detect swiping on the frame and identify the response selected by the user, for example, selecting "Thank you."

[0498] Step 7:

[0499] The terminal passes the selected text "Thank you" to the speech synthesis module, which converts it into voice data. The speech synthesis module generates "Thank you" in a natural speaking voice.

[0500] Step 8:

[0501] The device will play a synthesized voice of "Thank you" through the built-in speaker, allowing users to say "Thank you" silently.

[0502] Step 9:

[0503] After the process is complete, the device will return to standby mode for the next voice input. If the user speaks again, the system will be ready to recognize the input immediately.

[0504] Example 1

[0505] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0506] Conventional voice input systems often suffer from poor voice recognition accuracy, especially in noisy environments, preventing users from operating as intended. Users often experience inconvenience when confirmation or manual operation is required after voice input. There is a need for a system that can solve these problems and enable users to use voice input without stress.

[0507] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0508] In this invention, the server includes means for converting a user's voice into text in real time, means for projecting the converted text onto a display device, means for displaying multiple response options and allowing the user to select a desired response, means for synthesizing and playing back the selected response, preprocessing means for noise reduction and volume normalization of the voice data, and projection means for projecting the text onto a display device, thereby enabling highly accurate voice recognition and stress-free usability.

[0509] "User" refers to the individual who operates the system and provides instructions and input.

[0510] "Real-time speech-to-text conversion means" refers to technology or devices that instantly convert a user's voice input into text data.

[0511] "Means for projecting the converted text onto a display device" refers to a device, such as a projector or screen, that visually displays the text data generated by speech.

[0512] "Response options" refers to multiple possible responses or actions that a user can choose from.

[0513] "Means for synthesizing and playing back speech" refers to technology or devices that convert text data into speech and play it back in a format that can be heard by the user.

[0514] "Pre-processing means for noise reduction and volume normalization" refers to techniques and devices for removing unwanted noise from audio signals and for maintaining a consistent volume level.

[0515] "Projection means" refers to the technology or equipment used to project digital data onto a physical display device.

[0516] "Sensor for detecting swiping of a frame" refers to a sensor device for detecting a swipe action performed by a user on a frame.

[0517] This invention is a system for helping users communicate smoothly. It converts the user's speech into text in real time, projects the text on a display device, and displays multiple response options. The response selected by the user is played back as a synthesized voice.

[0518] System Configuration

[0519] This system consists of the following main components:

[0520] 1. Terminal: Smart glasses worn by the user, a device for voice input, display, and swiping operations.

[0521] 2. Speech recognition module: A software component that converts voice data collected on the device into text data.

[0522] 3. Microprojector: A hardware component installed in the device that projects text data onto the glasses' display.

[0523] 4. Speech synthesis module: A software component that converts text data into speech data and plays it through the built-in speaker.

[0524] 5. Sensor: A hardware component that detects user swipes.

[0525] Program processing flow

[0526] Collecting voice input

[0527] The device uses a built-in microphone to collect the user's voice. For example, when the user says, "I'd like to place an order," this voice data is temporarily stored in a buffer.

[0528] Audio data preprocessing

[0529] The device preprocesses the collected audio data to remove noise and normalize the volume, which is important to ensure accurate speech recognition.

[0530] Voice Recognition

[0531] The device passes the preprocessed voice data to a voice recognition module, which converts it into text data. For example, the generated text is "Please place an order."

[0532] Text projection

[0533] The device sends the generated text data to a micro-projector and projects it onto the user's smart glasses display, where the user can see the text "Please place your order."

[0534] Response Selection

[0535] The device displays multiple response options on the display, and the user swipes the frame to select the appropriate response, such as "Thank you."

[0536] Text to speech

[0537] The device passes the user's selected text to a speech synthesis module, converts it into audio data, and plays the voice saying "Thank you" using the built-in speaker.

[0538] Prepare for the next input

[0539] After completing the series of processes, the device returns to a standby state to prepare for the next voice input, and the device is ready for the user to input voice again.

[0540] Specific examples

[0541] Ordering at a restaurant

[0542] 1. Voice Input: The user says, "One cheeseburger, please."

[0543] 2. Speech recognition: The device converts this speech into text, generating the text "One cheeseburger please."

[0544] 3. Text projection: The device projects the generated text onto the glasses' display.

[0545] 4. Response selection: User swipes the frame to select "Thank you."

[0546] 5. Play audio: The device will play "Thank you" aloud.

[0547] 6. Standby: The device returns to standby mode to prepare for the next voice input.

[0548] Prompt Sentence Examples

[0549] "Please explain in detail each processing step of your system, especially how you use voice data preprocessing and speech recognition techniques."

[0550] "Please explain specifically how the device collects and processes voice input and provides feedback to the user."

[0551] In this way, the system allows users to communicate naturally without speaking, reducing the stress of face-to-face conversations and ordering.The system can be used in a similar process in other scenarios, making it widely applicable.

[0552] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0553] Step 1: Collecting voice input

[0554] The device uses a built-in microphone to collect the user's voice.

[0555] Input: User speech (e.g., "Please place my order")

[0556] Data processing: Storing audio data in a buffer

[0557] Output: Accumulated audio data

[0558] Specifically, the device's microphone is always on, and when voice input is detected, the voice data is stored in a buffer.

[0559] Step 2: Preprocessing the audio data

[0560] The device preprocesses the collected audio data to remove noise and normalize the volume.

[0561] Input: Stored voice data

[0562] Data processing: noise removal, volume normalization

[0563] Output: Preprocessed audio data

[0564] Specifically, the terminal uses an audio filtering algorithm to filter out background noise and adjust the volume to a consistent level.

[0565] Step 3: Voice Recognition

[0566] The terminal passes the pre-processed voice data to a voice recognition module and converts it into text data.

[0567] Input: Preprocessed audio data

[0568] Data calculation: The process of converting voice data into text data

[0569] Output: The converted text (e.g. "Please place your order").

[0570] Specifically, the device uses an API (e.g., Google Speech-to-Text API) to convert voice to text.

[0571] Step 4: Projecting text

[0572] The terminal transmits the generated text data to the micro projector and projects it onto the display.

[0573] Input: The converted text (e.g., "Please place my order").

[0574] Data processing: Formatting text data for display

[0575] Output: Text projected on a display

[0576] Specifically, the device formats the text for projection via a dedicated display driver and projects it onto the smart glasses display.

[0577] Step 5: Select a response

[0578] The terminal displays a number of response options on the display, and the user selects any one of them.

[0579] Input: Response options (e.g., "Thank you" or "Cancel my order")

[0580] Data processing: Display response options on the display

[0581] Output: The response selected by the user (e.g. "Thank you")

[0582] Specifically, sensors embedded in the frame detect the user's swiping actions and select a response based on that information.

[0583] Step 6: Read text aloud

[0584] The terminal passes the selected text to a speech synthesis module to convert it into voice data and play it back.

[0585] Input: Selected response text (e.g. "Thank you")

[0586] Data calculation: The process of converting text data into audio data

[0587] Output: The audio to be played (e.g. "Thank you")

[0588] Specifically, the device uses an API (e.g., Google Text-to-Speech API) to convert text into speech and plays it through the built-in speaker.

[0589] Step 7: Prepare for the next input

[0590] After completing the series of processes, the terminal returns to a standby state to prepare for the next voice input.

[0591] Input: None (standby)

[0592] Data processing: Wait for next voice input

[0593] Output: Ready and waiting

[0594] Specifically, the system returns to the voice input mode and waits for the input trigger word again.

[0595] (Application example 1)

[0596] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0597] Conventional communication support systems have the problem that the process of converting speech to text and allowing users to select their own responses places a heavy burden on users, making it difficult to maintain a smooth dialogue. Furthermore, the limited knowledge required to generate appropriate responses makes it difficult to respond flexibly. For these reasons, there is a growing need for a system that allows users to continue a dialogue naturally and provides appropriate responses instantly.

[0598] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0599] In this invention, the server includes means for converting a user's voice into text in real time, means for projecting the converted text onto a display device, means for displaying multiple response options and allowing the user to select a desired response, means for synthesizing the selected response and playing it back, and means for generating a response based on a prompt sentence, including a generative AI model for generating an appropriate response based on the content of the user's voice. This allows the user to instantly obtain an appropriate response while smoothly engaging in dialogue.

[0600] "User" refers to the person who operates the system and provides voice input.

[0601] "Means for converting speech to text in real time" refers to a component that has the functionality to instantly convert a user's speech data into text data.

[0602] "Display device" means a device for visually presenting textual data and response options to a user.

[0603] The "means for projecting" refers to a function for displaying text data on a display device.

[0604] "Multiple response options" refers to a set of selectable responses that the system offers to the user.

[0605] "Means for selection" refers to a component that has the functionality to allow a user to select any response from among multiple response options.

[0606] "Means for synthesizing and reproducing speech" refers to a component that has the function of converting selected text data into speech data and reproducing it.

[0607] "Generative AI model" refers to an artificial intelligence module that generates appropriate responses based on the content of a user's voice.

[0608] A "prompt sentence" is text data input into a generative AI model, and refers to an instruction sentence used to derive an appropriate response.

[0609] A system for realizing this application example includes the following components and means:

[0610] First, the user wears a display device called smart glasses. These smart glasses have built-in microphones and speakers, and are equipped with a means for collecting voice input. Voice data spoken by the user is collected through the built-in microphone and converted into text in real time. High recognition accuracy is achieved by using the Google Speech Recognition API for voice recognition. The converted text data is instantly projected onto the smart glasses' display. The display device is equipped with a projection means to provide a visual representation of the text data.

[0611] Next, multiple response options are generated based on the speech content and displayed on the display. The response options are generated using a generative AI model. This generative AI model creates a prompt based on the speech content of the user and derives an appropriate response based on the prompt. The user can select any response from the multiple response options by swiping the touch frame of the smart glasses. This selection method prepares the selected response for voice synthesis and playback. The selected response is then converted into voice data using speech synthesis technology and played through the built-in speaker. Software called pyttsx3 is used for speech synthesis.

[0612] In this system, the user's voice data is pre-processed to remove noise and normalize the volume, which allows for more accurate voice recognition, allowing users to continue dialogue easily and quickly, improving work efficiency.

[0613] Hardware and Software Examples

[0614] Smart glasses: Equipped with a display, microphone, speaker, and touch sensor.

[0615] Speech recognition software: Google Speech Recognition API.

[0616] Speech synthesis software: pyttsx3.

[0617] Natural Language Processing model: OpenAI GPT-3.

[0618] Specific examples

[0619] Situation: A customer orders, "One cheeseburger, please."

[0620] System response: "Your order has been accepted. Thank you."

[0621] Prompt Sentence Examples

[0622] Generate appropriate responses to customer orders.

[0623] Order: One cheeseburger, please

[0624] Example response:

[0625] This will generate a natural-sounding response such as "Your order has been accepted. Thank you."

[0626] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0627] Step 1:

[0628] The device collects the user's voice using a built-in microphone. When the user speaks, the device stores the voice data in a buffer. The input of this step is the user's voice, and the output is the collected voice data.

[0629] Step 2:

[0630] The terminal processes the collected audio data using preprocessing means that perform noise reduction and volume normalization. The input to this step is the audio data collected in step 1, and the output is audio data with noise reduction and volume normalization.

[0631] Step 3:

[0632] The server passes the preprocessed audio data to the Google Speech Recognition API and converts it into text data. The input is noise-removed and volume-normalized audio data, and the output is text data.

[0633] Step 4:

[0634] The server inputs a prompt sentence to the generative AI model based on the text data. For example, a prompt sentence such as "Please generate an appropriate response to the customer's order. Order: One cheeseburger, please" is input to the generative AI model. The inputs to this step are the speech-recognized text data and the generated prompt sentence, and the output is the generated response text.

[0635] Step 5:

[0636] The server sends the generated response text to the terminal, which projects multiple response options onto a display device. The user selects an appropriate response by swiping the touch frame of the smart glasses. The input of this step is the generated response text, and the output is the response option selected by the user.

[0637] Step 6:

[0638] The server passes the user-selected response text to a speech synthesis module, which converts it into speech data. The input is the user-selected response text, and the output is the synthesized speech data.

[0639] Step 7:

[0640] The device plays the synthesized voice data over the built-in speaker, allowing the user to confirm the selected response as voice. The input of this step is the synthesized voice data, and the output is the played voice.

[0641] Through the above processing steps, users can have natural conversations and continue communication smoothly.

[0642] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0643] This invention is a system incorporating an emotion engine to assist users in smooth communication. An embodiment of this system and the processing of its program will be described below.

[0644] System Configuration

[0645] This system consists of the following main components:

[0646] 1. Terminal: Smart glasses worn by the user, a device for voice input, display, and swiping operations.

[0647] 2. Speech recognition module: A software component that converts voice data collected on the device into text data.

[0648] 3. Microprojector: A hardware component installed in the device that projects text data onto the glasses' display.

[0649] 4. Speech synthesis module: A software component that converts text data into speech data and plays it through the built-in speaker.

[0650] 5. Sensor: A hardware component that detects user swipes.

[0651] 6. Emotion Engine: A component for real-time emotion recognition from the user's voice, facial expressions, and other biometric data.

[0652] Program processing flow

[0653] The program executes the process in the following steps:

[0654] 1. Collecting voice input

[0655] The device uses a built-in microphone to collect the user's voice in real time. For example, when the user says, "I'd like to place an order," the voice data is recorded.

[0656] 2. Preprocessing of audio data

[0657] The device pre-processes the collected audio data for noise cancellation and volume normalization, which improves the accuracy of the audio data.

[0658] 3. Voice Recognition

[0659] The device then passes the pre-processed voice data to the speech recognition module, which uses the latest edge computing AI to convert the speech into text, such as "Please place an order."

[0660] 4. Text Projection

[0661] The device receives the text data output from the voice recognition module and sends it to a microprojector, which projects the text onto the display of the glasses. The user can see the text "Please place your order" on the display.

[0662] 5. Emotion recognition

[0663] The device sends collected voice and other biometric data to the emotion engine, which analyzes in real time whether the user is expressing any emotion. For example, if the user is likely to be nervous, that emotional state is identified.

[0664] 6. Response Selection

[0665] The device displays appropriate response options based on the user's emotional state, and the user can select the desired response by swiping the frame.

[0666] 7. Text-to-speech

[0667] The device passes the user's selected response to a speech synthesis module, which converts it into voice data. The speech synthesis module then plays the response in a tone that adapts to the user's emotional state. For example, it might play "thank you" in a gentle voice.

[0668] 8. Prepare for the next input

[0669] After the process is complete, the device will return to standby mode for the next voice input. If the user speaks again, the system will be ready to recognize the input immediately.

[0670] Specific examples

[0671] Ordering at a restaurant

[0672] 1. Voice Input: The user says, "One cheeseburger, please."

[0673] 2. Speech recognition: The device converts this speech into text, generating the text "One cheeseburger please."

[0674] 3. Text projection: The device projects the generated text onto the glasses' display.

[0675] 4. Emotion recognition: The device analyzes the user's tense tone of voice, and the emotion engine detects the sense of tension.

[0676] 5. Response selection: User swipes the frame to select "Thank you."

[0677] 6. Voice Playback: The device uses the emotion engine to play back a voice saying "Thank you" in a gentle tone to ease tension.

[0678] 7. Standby: The device returns to standby mode for the next voice input.

[0679] In this way, the system analyzes the user's emotional state and provides appropriate responses, supporting more natural and smooth communication.The same process can be used in other scenarios, making it widely applicable.

[0680] The processing flow will be explained below.

[0681] Step 1:

[0682] The device uses a built-in microphone to collect the user's voice in real time. For example, if the user says, "I'd like a cheeseburger, please," the voice data will be recorded.

[0683] Step 2:

[0684] The device pre-processes the collected audio data for noise cancellation and volume normalization, which improves the accuracy of the audio data.

[0685] Step 3:

[0686] The device then passes the pre-processed voice data to a speech recognition module, which uses the latest edge computing AI to convert the speech into text, such as "One cheeseburger, please."

[0687] Step 4:

[0688] The device receives the text data output from the voice recognition module and sends it to the microprojector, which projects the text onto the display of the glasses. The user can see the text "One cheeseburger, please" on the display.

[0689] Step 5:

[0690] The device sends the collected voice data and other biometric data to the emotion engine, which analyzes the user's emotional state in real time, such as whether they are nervous or relaxed. For example, tension can be detected from the user's tone of voice and speaking style.

[0691] Step 6:

[0692] The device displays multiple appropriate response options based on the emotional state recognized by the emotion engine. If the emotion engine detects the user's tension, it displays response options to relax them. The user swipes the frame to select a response, such as "Thank you."

[0693] Step 7:

[0694] The device uses sensors to detect swiping on the frame, identifies the user's selected response, and passes the selected text "Thank you" to a speech synthesis module for conversion into voice data.

[0695] Step 8:

[0696] The device uses an emotion engine to play a synthesized "thank you" in a tone that adapts to the user's emotional state. For example, if the user is nervous, the device will play a "thank you" in a calm, gentle voice.

[0697] Step 9:

[0698] After the process is complete, the device will return to standby mode for the next voice input. If the user speaks again, the system will be ready to recognize the input immediately.

[0699] This process allows the system to take the user's emotions into account and provide appropriate responses. For example, if the device detects nervousness when ordering at a restaurant, it will respond with a voice response in a relaxing tone, allowing the user to communicate with ease.

[0700] Example 2

[0701] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0702] Conventional speech recognition systems simply convert speech into text without understanding the user's emotions, making it difficult to provide appropriate responses. This can lead to awkward communication and makes it difficult to provide appropriate assistance to users who are particularly nervous. Furthermore, few systems consistently perform advanced processing, such as preprocessing of speech data and real-time emotion analysis.

[0703] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0704] In this invention, the server includes means for converting a user's voice into text in real time, means for projecting the converted text onto a display device, and preprocessing means for collecting the user's voice data and performing noise reduction and volume normalization. This enables the user's voice to be recognized and converted into text with high accuracy. The server also includes means for analyzing the user's emotions in real time and means for providing appropriate response options based on the analyzed emotions. This allows the server to understand the user's emotional state and provide appropriate responses accordingly, enabling smooth communication.

[0705] "User" means any person who uses the System.

[0706] "Voice" refers to the words or voices spoken by the user.

[0707] "Real time" refers to a state in which processing is performed almost simultaneously.

[0708] "Text" refers to the text information converted from audio data.

[0709] "Means" refers to a device or program that performs a particular function or role.

[0710] "Display device" means a device that visually presents information to a user.

[0711] "Projection" refers to displaying the generated text on a display device.

[0712] "Multiple response options" refers to multiple responses that a user can choose from.

[0713] "Selection" refers to the act of a user choosing one option from multiple options.

[0714] "Speech synthesis" refers to the technology of converting text data into voice data.

[0715] "Playback" refers to outputting synthesized sound through a speaker or the like.

[0716] "Emotion" refers to the user's psychological state (e.g., tension, joy, sadness, etc.).

[0717] "Analysis" refers to extracting and understanding information from collected data.

[0718] "Providing" refers to the act of making information or functionality available to users.

[0719] "Noise reduction" refers to the technology of removing unnecessary noise from audio data.

[0720] "Volume normalization" refers to a technique for adjusting the volume of audio data to a constant level.

[0721] "Preprocessing" refers to the preparation and formatting of data before the main processing.

[0722] "Sensor" refers to a device that detects user operations and situations.

[0723] A "swipe" refers to the action of a user sliding their finger across the surface of a display or device.

[0724] This invention is a system that converts voice input into text, recognizes emotions, presents response options, and plays synthesized speech using a smart glass-type device worn by the user. This system uses the following hardware and software to achieve specific functions.

[0725] Hardware Configuration

[0726] 1. Device: Smart glasses worn by the user, containing a built-in microphone, display, microprojector, and sensors.

[0727] 2. Micro-projector: Installed in the device, it projects the converted text onto the glasses' display.

[0728] 3. Sensor: A device that detects swiping operations and recognizes user actions.

[0729] 4. Speaker: Used to play the voice output from the speech synthesis module.

[0730] Software Configuration

[0731] 1. Speech Recognition Module: Software for converting user speech into text, for example, using edge computing AI technology (e.g., Google Speech-to-Text API).

[0732] 2. Emotion Engine: Software for analyzing user emotions in real time, using Affectiva SDK as an example.

[0733] 3. Speech synthesis module: Software for converting text data into speech data. We use Amazon Polly as an example.

[0734] 4. Preprocessing program: Software that denoises and normalizes the volume of audio data, using the Spectral Subtraction algorithm as an example.

[0735] Specific examples of processing

[0736] The operation of this system will be explained based on a specific example.

[0737] Ordering at a restaurant

[0738] 1. Voice Input: The user says, "One cheeseburger, please."

[0739] 2. Audio data preprocessing: The device performs noise cancellation and volume normalization on this audio data.

[0740] 3. Speech recognition: The device passes the preprocessed speech data to the Google Speech-to-Text API, generating text data such as "One cheeseburger, please."

[0741] 4. Text projection: The device sends the generated text data to the micro-projector, which projects it onto the smart glasses display.

[0742] 5. Emotion Recognition: The device analyzes voice and biometric data using the Affectiva SDK to detect when the user is nervous.

[0743] 6. Response selection: The user swipes to select "Thank you" from the response options displayed on the display.

[0744] 7. Play Voice: The device converts the text to speech using Amazon Polly and plays "Thank you" in a calming tone to ease tension.

[0745] 8. Prepare for next input: The device returns to standby mode for the next voice input.

[0746] Prompt Sentence Examples

[0747] "Please explain in detail the processing steps of an emotion recognition and response system for a nervous user when ordering at a restaurant."

[0748] As described above, this system integrates hardware and software to convert a user's voice into text with high accuracy and provide appropriate responses according to their emotions.

[0749] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0750] Step 1:

[0751] Collecting voice input

[0752] The device uses a built-in microphone to collect the user's voice in real time. For example, when the user says, "I'd like to place an order," the voice data is collected and input into the next step.

[0753] Step 2:

[0754] Audio data preprocessing

[0755] The device performs noise reduction and volume normalization on the collected audio data. Specifically, it applies a noise reduction algorithm (e.g., Spectral Subtraction) to reduce background noise. At the same time, it normalizes volume peaks to ensure that all sounds are at a comfortable level to hear. This preprocessed audio data is then input to the next step.

[0756] Step 3:

[0757] Voice Recognition

[0758] The device passes the preprocessed voice data to a speech recognition module (e.g., Google Speech-to-Text API) to convert the voice into text data. This conversion generates the text data "Please place your order." This text data is input to the next step.

[0759] Step 4:

[0760] Text projection

[0761] The terminal sends the text data obtained from the voice recognition module to the micro-projector, which projects the text onto the smart glasses' display. Specifically, the text "Please place your order" is visually displayed on the display. This displayed text is input into the next step.

[0762] Step 5:

[0763] emotion recognition

[0764] The device sends collected voice data and other biometric data (e.g., heart rate, galvanic skin response) to an emotion engine (e.g., Affectiva SDK). The emotion engine analyzes this data and identifies the user's emotional state (e.g., nervousness, joy, sadness, etc.) in real time. For example, if the user is nervous, that emotional state is identified. This emotional state data is input into the next step.

[0765] Step 6:

[0766] Response Selection

[0767] Based on the analysis results from the emotion engine, the device displays appropriate response options on the display. Specifically, options such as "Thank you" or "Please wait a little longer" are displayed on the display. The user selects the desired response option by swiping the frame. This selected response option is input into the next step.

[0768] Step 7:

[0769] Text to speech

[0770] The device sends the selected response to a speech synthesis module (e.g., Amazon Polly) to convert the text data into speech. Based on the analysis results of the emotion engine, the speech synthesis module plays a response with the corresponding emotional tone. Specifically, it plays "Thank you" in a gentle tone to ease tension. This played speech is then output to the next step.

[0771] Step 8:

[0772] Prepare for the next input

[0773] After all processing is completed, the device returns to standby mode for the next voice input. When the user speaks again, the system immediately collects the voice and is ready to resume processing from the first step.

[0774] (Application example 2)

[0775] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0776] Conventional communication support systems provided the ability to convert a user's voice into text in real time and display the text, but lacked the ability to identify the emotions of the user or the person they were speaking to and select and provide an appropriate response. This resulted in issues such as communication not proceeding smoothly when the user or customer was nervous or an inappropriate response was selected. Furthermore, the process of synthesizing and playing back the selected response was often unnatural, which could detract from the user experience.

[0777] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for converting a user's voice into text in real time, means for projecting the converted text on a display device, means for displaying multiple response options and allowing the user to select any response, means for synthesizing the selected response and playing it back, means for analyzing the voice data of the user and the conversation partner and identifying emotions, and means for providing appropriate response options based on the identified emotions. This makes it possible to grasp the emotional states of the user and the conversation partner and select and provide appropriate responses, thereby making communication smoother and improving the user experience.

[0778] "User" means any person who operates the System.

[0779] "Real-time speech-to-text means" means a technical method for instantly converting speech uttered by a User into text form.

[0780] "Means for projecting the converted text onto a display device" refers to a technical method for displaying audio data converted into text format on a display device such as a display.

[0781] "A means of displaying multiple response options and allowing the user to select any response" refers to a technical method by which the system displays several pre-prepared response options and the user selects the one they deem appropriate.

[0782] "Means for synthesizing and playing the selected response" means a technical means for synthesizing a text response selected by a user as speech and playing that speech.

[0783] "Means for analyzing the voice data of the user and the conversation partner and identifying emotions" refers to a technical method for analyzing the voice data uttered by the user and the conversation partner and identifying the emotions contained in the voice.

[0784] "Means for providing appropriate response options based on the identified emotion" refers to a technical method by which the system presents response options appropriate to the emotional state determined by the analysis.

[0785] This invention is a system designed to assist users in smoothly carrying out communication. A specific implementation method of this system will be described below.

[0786] System hardware configuration

[0787] The system mainly consists of the following hardware components:

[0788] 1. Smart Glasses:

[0789] Built-in microphone

[0790] Display (micro projector)

[0791] Sensor that detects swipe operations

[0792] 2. Server:

[0793] Voice Recognition Module

[0794] Emotion Engine

[0795] Speech Synthesis Module

[0796] Software Components

[0797] The system consists of the following software components:

[0798] 1. Speech Recognition Module:

[0799] Software for converting speech to text in real time. For example, you can use the speech_recognition library.

[0800] 2. Emotion Engine:

[0801] Software that identifies emotions from voice and other biometric data, using machine learning models and APIs.

[0802] 3. Speech synthesis module:

[0803] Software for converting text to speech. For example, the gTTS library can be used.

[0804] System Operation

[0805] 1. Collecting voice input:

[0806] The server collects the user's voice in real time using the smart glasses' built-in microphone.

[0807] 2. Audio preprocessing:

[0808] The server pre-processes the collected audio data for noise cancellation and volume normalization.

[0809] 3. Speech Recognition:

[0810] The server passes the preprocessed speech data to a speech recognition module and converts it into text.

[0811] 4. Emotion recognition:

[0812] The server passes the voice data to an emotion engine to identify the emotions of the user and the conversation partner.

[0813] 5. Response Selection:

[0814] The server displays appropriate response options on the smart glasses display based on the identified emotion.

[0815] 6. Response speech synthesis:

[0816] The server passes the user's selected text response to a speech synthesis module, which converts it into speech and plays it back.

[0817] Specific examples

[0818] Restaurant service

[0819] 1. The staff member says, "I have a question about the menu."

[0820] 2. The smart glasses collect the audio and send it to the server.

[0821] 3. The server converts the speech into text and displays, "I have a question about the menu."

[0822] 4. The emotion engine detects customer tension.

[0823] 5. The server suggests appropriate response options: "Please feel free to ask any questions" and "Don't be nervous, it's okay."

[0824] 6. The staff member will select "Please ask any questions" and play it back using voice synthesis.

[0825] Prompt Sentence Examples

[0826] Customer: "I have a question about the menu."

[0827] System: "Tension detected"

[0828] System: "You have the following response options: 1. Please feel free to ask any questions. 2. Don't be nervous, it's okay."

[0829] The staff member selected "Please ask questions."

[0830] The system will play a voice message saying "Please feel free to ask any questions."

[0831] In this way, the system analyzes the emotional state of the user and the person they are talking to in real time and provides appropriate response options, thereby enabling more natural and smooth communication.

[0832] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0833] Step 1:

[0834] The server collects the user's voice in real time using the smart glasses' built-in microphone. The input is the user's voice data, and the output is the raw voice data. This voice data becomes the basis for subsequent processing.

[0835] Step 2:

[0836] The server performs preprocessing of noise cancellation and volume normalization on the collected audio data. The input is the audio data obtained in step 1, and the output is the preprocessed audio data. Specifically, it removes background noise from the audio data and adjusts the volume to a certain level.

[0837] Step 3:

[0838] The server passes the preprocessed voice data to a voice recognition module, which converts the voice into text in real time. The input is the preprocessed voice data, and the output is text data. Specifically, a voice recognition library (e.g., speech_recognition) is used to generate text such as "I have a question about the menu."

[0839] Step 4:

[0840] The server passes the text data to the emotion engine to identify the emotions of the user and the person interacting with them. The input is text data and voice data, and the output is the identified emotional state (e.g., nervous). Specifically, an emotion recognition algorithm is used to determine emotions from the tone and content of the voice.

[0841] Step 5:

[0842] The server then displays appropriate response options on the smart glasses display based on the identified emotional state. The input is the emotional state and pre-prepared response options, and the output is the response options displayed on the display. Specifically, options such as "Please feel free to ask any questions" and "Don't be nervous, it's okay" are displayed.

[0843] Step 6:

[0844] The user swipes the frame of the smart glasses to select an appropriate response from the displayed response options. The input is the response option on the display, and the output is the selected response option. Specifically, a sensor detects the swipe action, and the selected option is sent to the server.

[0845] Step 7:

[0846] The server passes the selected response option to the speech synthesis module, converts the text into speech, and plays it back. The input is the text data of the selected response option, and the output is audio data. Specifically, the gTTS library is used to play back the phrase "Please ask any questions."

[0847] Step 8:

[0848] The server returns to standby mode to prepare for the next voice input. There is no particular input, and the output is in the system standby state. Specifically, the system returns to the state of waiting for voice input again.

[0849] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0850] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0851] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0852] [Third embodiment]

[0853] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0854] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0855] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0856] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0857] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0858] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0859] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0860] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0861] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0862] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0863] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0864] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0865] The present invention is a system for supporting users in smoothly carrying out communication. An embodiment of this system and the processing of its program will be described below.

[0866] System Configuration

[0867] This system consists of the following main components:

[0868] 1. Terminal: Smart glasses worn by the user, a device for voice input, display, and swiping operations.

[0869] 2. Speech recognition module: A software component that converts voice data collected on the device into text data.

[0870] 3. Microprojector: A hardware component installed in the device that projects text data onto the glasses' display.

[0871] 4. Speech synthesis module: A software component that converts text data into speech data and plays it through the built-in speaker.

[0872] 5. Sensor: A hardware component that detects user swipes.

[0873] Program processing flow

[0874] The program executes the process in the following steps:

[0875] 1. Collecting voice input

[0876] The device uses a built-in microphone to collect the user's voice. For example, when the user says, "I'd like to order," the voice data is stored in a buffer.

[0877] 2. Preprocessing of audio data

[0878] The terminal performs preprocessing on the collected audio data to remove noise and normalize the volume.

[0879] 3. Voice Recognition

[0880] The device passes the preprocessed voice data to a voice recognition module, which converts it into text data. For example, the generated text is "Please place an order."

[0881] 4. Text Projection

[0882] The device sends the generated text data to a microprojector, which projects it onto the display of the glasses, where the user can see the text "Please place your order."

[0883] 5. Response Selection

[0884] The device displays multiple response options on the Glass, and the user selects the appropriate response by swiping the frame, for example, "Thank you."

[0885] 6. Text-to-speech

[0886] The device passes the user's selected text to a speech synthesis module, converts it into audio data, and plays the voice saying "Thank you" using the built-in speaker.

[0887] 7. Prepare for the next input

[0888] After completing this series of processes, the device returns to standby mode and is ready for the next voice input.

[0889] Specific examples

[0890] Ordering at a restaurant

[0891] 1. Voice Input: The user says, "One cheeseburger, please."

[0892] 2. Speech recognition: The device converts this speech into text, generating the text "One cheeseburger please."

[0893] 3. Text projection: The device projects the generated text onto the glasses' display.

[0894] 4. Response selection: User swipes the frame to select "Thank you."

[0895] 5. Play audio: The device will play "Thank you" aloud.

[0896] 6. Standby: The device returns to standby mode to prepare for the next voice input.

[0897] In this way, the system allows users to communicate naturally without speaking, reducing the stress of face-to-face conversations and ordering.The system can be used in a similar process in other scenarios, making it widely applicable.

[0898] The processing flow will be explained below.

[0899] Step 1:

[0900] The device uses a built-in microphone to collect the user's voice in real time. For example, when the user says, "I'd like to place an order," the voice data is recorded.

[0901] Step 2:

[0902] The device pre-processes the collected audio data for noise cancellation and volume normalization, which improves the accuracy of the audio data.

[0903] Step 3:

[0904] The device then passes the pre-processed voice data to the speech recognition module, which uses the latest edge computing AI to convert the speech into text, such as "Please place an order."

[0905] Step 4:

[0906] The device receives the text data output from the voice recognition module and sends it to a microprojector, which projects the text onto the display of the glasses. The user can see the text "Please place your order" on the display.

[0907] Step 5:

[0908] The device displays multiple response options on the display for the user to choose from, and the user can select the desired response by swiping the frame.

[0909] Step 6:

[0910] The device uses sensors to detect swiping on the frame and identify the response selected by the user, for example, selecting "Thank you."

[0911] Step 7:

[0912] The terminal passes the selected text "Thank you" to the speech synthesis module, which converts it into voice data. The speech synthesis module generates "Thank you" in a natural speaking voice.

[0913] Step 8:

[0914] The device will play a synthesized voice of "Thank you" through the built-in speaker, allowing users to say "Thank you" silently.

[0915] Step 9:

[0916] After the process is complete, the device will return to standby mode for the next voice input. If the user speaks again, the system will be ready to recognize the input immediately.

[0917] Example 1

[0918] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0919] Conventional voice input systems often suffer from poor voice recognition accuracy, especially in noisy environments, preventing users from operating as intended. Users often experience inconvenience when confirmation or manual operation is required after voice input. There is a need for a system that can solve these problems and enable users to use voice input without stress.

[0920] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0921] In this invention, the server includes means for converting a user's voice into text in real time, means for projecting the converted text onto a display device, means for displaying multiple response options and allowing the user to select a desired response, means for synthesizing and playing back the selected response, preprocessing means for noise reduction and volume normalization of the voice data, and projection means for projecting the text onto a display device, thereby enabling highly accurate voice recognition and stress-free usability.

[0922] "User" refers to the individual who operates the system and provides instructions and input.

[0923] "Real-time speech-to-text conversion means" refers to technology or devices that instantly convert a user's voice input into text data.

[0924] "Means for projecting the converted text onto a display device" refers to a device, such as a projector or screen, that visually displays the text data generated by speech.

[0925] "Response options" refers to multiple possible responses or actions that a user can choose from.

[0926] "Means for synthesizing and playing back speech" refers to technology or devices that convert text data into speech and play it back in a format that can be heard by the user.

[0927] "Pre-processing means for noise reduction and volume normalization" refers to techniques and devices for removing unwanted noise from audio signals and for maintaining a consistent volume level.

[0928] "Projection means" refers to the technology or equipment used to project digital data onto a physical display device.

[0929] "Sensor for detecting swiping of a frame" refers to a sensor device for detecting a swipe action performed by a user on a frame.

[0930] This invention is a system for helping users communicate smoothly. It converts the user's speech into text in real time, projects the text on a display device, and displays multiple response options. The response selected by the user is played back as a synthesized voice.

[0931] System Configuration

[0932] This system consists of the following main components:

[0933] 1. Terminal: Smart glasses worn by the user, a device for voice input, display, and swiping operations.

[0934] 2. Speech recognition module: A software component that converts voice data collected on the device into text data.

[0935] 3. Microprojector: A hardware component installed in the device that projects text data onto the glasses' display.

[0936] 4. Speech synthesis module: A software component that converts text data into speech data and plays it through the built-in speaker.

[0937] 5. Sensor: A hardware component that detects user swipes.

[0938] Program processing flow

[0939] Collecting voice input

[0940] The device uses a built-in microphone to collect the user's voice. For example, when the user says, "I'd like to place an order," this voice data is temporarily stored in a buffer.

[0941] Audio data preprocessing

[0942] The device preprocesses the collected audio data to remove noise and normalize the volume, which is important to ensure accurate speech recognition.

[0943] Voice Recognition

[0944] The device passes the preprocessed voice data to a voice recognition module, which converts it into text data. For example, the generated text is "Please place an order."

[0945] Text projection

[0946] The device sends the generated text data to a micro-projector and projects it onto the user's smart glasses display, where the user can see the text "Please place your order."

[0947] Response Selection

[0948] The device displays multiple response options on the display, and the user swipes the frame to select the appropriate response, such as "Thank you."

[0949] Text to speech

[0950] The device passes the user's selected text to a speech synthesis module, converts it into audio data, and plays the voice saying "Thank you" using the built-in speaker.

[0951] Prepare for the next input

[0952] After completing the series of processes, the device returns to a standby state to prepare for the next voice input, and the device is ready for the user to input voice again.

[0953] Specific examples

[0954] Ordering at a restaurant

[0955] 1. Voice Input: The user says, "One cheeseburger, please."

[0956] 2. Speech recognition: The device converts this speech into text, generating the text "One cheeseburger please."

[0957] 3. Text projection: The device projects the generated text onto the glasses' display.

[0958] 4. Response selection: User swipes the frame to select "Thank you."

[0959] 5. Play audio: The device will play "Thank you" aloud.

[0960] 6. Standby: The device returns to standby mode to prepare for the next voice input.

[0961] Prompt Sentence Examples

[0962] "Please explain in detail each processing step of your system, especially how you use voice data preprocessing and speech recognition techniques."

[0963] "Please explain specifically how the device collects and processes voice input and provides feedback to the user."

[0964] In this way, the system allows users to communicate naturally without speaking, reducing the stress of face-to-face conversations and ordering.The system can be used in a similar process in other scenarios, making it widely applicable.

[0965] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0966] Step 1: Collecting voice input

[0967] The device uses a built-in microphone to collect the user's voice.

[0968] Input: User speech (e.g., "Please place my order")

[0969] Data processing: Storing audio data in a buffer

[0970] Output: Accumulated audio data

[0971] Specifically, the device's microphone is always on, and when voice input is detected, the voice data is stored in a buffer.

[0972] Step 2: Preprocessing the audio data

[0973] The device preprocesses the collected audio data to remove noise and normalize the volume.

[0974] Input: Stored voice data

[0975] Data processing: noise removal, volume normalization

[0976] Output: Preprocessed audio data

[0977] Specifically, the terminal uses an audio filtering algorithm to filter out background noise and adjust the volume to a consistent level.

[0978] Step 3: Voice Recognition

[0979] The terminal passes the pre-processed voice data to a voice recognition module and converts it into text data.

[0980] Input: Preprocessed audio data

[0981] Data calculation: The process of converting voice data into text data

[0982] Output: The converted text (e.g. "Please place your order").

[0983] Specifically, the device uses an API (e.g., Google Speech-to-Text API) to convert voice to text.

[0984] Step 4: Projecting text

[0985] The terminal transmits the generated text data to the micro projector and projects it onto the display.

[0986] Input: The converted text (e.g., "Please place my order").

[0987] Data processing: Formatting text data for display

[0988] Output: Text projected on a display

[0989] Specifically, the device formats the text for projection via a dedicated display driver and projects it onto the smart glasses display.

[0990] Step 5: Select a response

[0991] The terminal displays a number of response options on the display, and the user selects any one of them.

[0992] Input: Response options (e.g., "Thank you" or "Cancel my order")

[0993] Data processing: Display response options on the display

[0994] Output: The response selected by the user (e.g. "Thank you")

[0995] Specifically, sensors embedded in the frame detect the user's swiping actions and select a response based on that information.

[0996] Step 6: Read text aloud

[0997] The terminal passes the selected text to a speech synthesis module to convert it into voice data and play it back.

[0998] Input: Selected response text (e.g. "Thank you")

[0999] Data calculation: The process of converting text data into audio data

[1000] Output: The audio to be played (e.g. "Thank you")

[1001] Specifically, the device uses an API (e.g., Google Text-to-Speech API) to convert text into speech and plays it through the built-in speaker.

[1002] Step 7: Prepare for the next input

[1003] After completing the series of processes, the terminal returns to a standby state to prepare for the next voice input.

[1004] Input: None (standby)

[1005] Data processing: Wait for next voice input

[1006] Output: Ready and waiting

[1007] Specifically, the system returns to the voice input mode and waits for the input trigger word again.

[1008] (Application example 1)

[1009] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1010] Conventional communication support systems have the problem that the process of converting speech to text and allowing users to select their own responses places a heavy burden on users, making it difficult to maintain a smooth dialogue. Furthermore, the limited knowledge required to generate appropriate responses makes it difficult to respond flexibly. For these reasons, there is a growing need for a system that allows users to continue a dialogue naturally and provides appropriate responses instantly.

[1011] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1012] In this invention, the server includes means for converting a user's voice into text in real time, means for projecting the converted text onto a display device, means for displaying multiple response options and allowing the user to select a desired response, means for synthesizing the selected response and playing it back, and means for generating a response based on a prompt sentence, including a generative AI model for generating an appropriate response based on the content of the user's voice. This allows the user to instantly obtain an appropriate response while smoothly engaging in dialogue.

[1013] "User" refers to the person who operates the system and provides voice input.

[1014] "Means for converting speech to text in real time" refers to a component that has the functionality to instantly convert a user's speech data into text data.

[1015] "Display device" means a device for visually presenting textual data and response options to a user.

[1016] The "means for projecting" refers to a function for displaying text data on a display device.

[1017] "Multiple response options" refers to a set of selectable responses that the system offers to the user.

[1018] "Means for selection" refers to a component that has the functionality to allow a user to select any response from among multiple response options.

[1019] "Means for synthesizing and reproducing speech" refers to a component that has the function of converting selected text data into speech data and reproducing it.

[1020] "Generative AI model" refers to an artificial intelligence module that generates appropriate responses based on the content of a user's voice.

[1021] A "prompt sentence" is text data input into a generative AI model, and refers to an instruction sentence used to derive an appropriate response.

[1022] A system for realizing this application example includes the following components and means:

[1023] First, the user wears a display device called smart glasses. These smart glasses have built-in microphones and speakers, and are equipped with a means for collecting voice input. Voice data spoken by the user is collected through the built-in microphone and converted into text in real time. High recognition accuracy is achieved by using the Google Speech Recognition API for voice recognition. The converted text data is instantly projected onto the smart glasses' display. The display device is equipped with a projection means to provide a visual representation of the text data.

[1024] Next, multiple response options are generated based on the speech content and displayed on the display. The response options are generated using a generative AI model. This generative AI model creates a prompt based on the speech content of the user and derives an appropriate response based on the prompt. The user can select any response from the multiple response options by swiping the touch frame of the smart glasses. This selection method prepares the selected response for voice synthesis and playback. The selected response is then converted into voice data using speech synthesis technology and played through the built-in speaker. Software called pyttsx3 is used for speech synthesis.

[1025] In this system, the user's voice data is pre-processed to remove noise and normalize the volume, which allows for more accurate voice recognition, allowing users to continue dialogue easily and quickly, improving work efficiency.

[1026] Hardware and Software Examples

[1027] Smart glasses: Equipped with a display, microphone, speaker, and touch sensor.

[1028] Speech recognition software: Google Speech Recognition API.

[1029] Speech synthesis software: pyttsx3.

[1030] Natural Language Processing model: OpenAI GPT-3.

[1031] Specific examples

[1032] Situation: A customer orders, "One cheeseburger, please."

[1033] System response: "Your order has been accepted. Thank you."

[1034] Prompt Sentence Examples

[1035] Generate appropriate responses to customer orders.

[1036] Order: One cheeseburger, please

[1037] Example response:

[1038] This will generate a natural-sounding response such as "Your order has been accepted. Thank you."

[1039] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1040] Step 1:

[1041] The device collects the user's voice using a built-in microphone. When the user speaks, the device stores the voice data in a buffer. The input of this step is the user's voice, and the output is the collected voice data.

[1042] Step 2:

[1043] The terminal processes the collected audio data using preprocessing means that perform noise reduction and volume normalization. The input to this step is the audio data collected in step 1, and the output is audio data with noise reduction and volume normalization.

[1044] Step 3:

[1045] The server passes the preprocessed audio data to the Google Speech Recognition API and converts it into text data. The input is noise-removed and volume-normalized audio data, and the output is text data.

[1046] Step 4:

[1047] The server inputs a prompt sentence to the generative AI model based on the text data. For example, a prompt sentence such as "Please generate an appropriate response to the customer's order. Order: One cheeseburger, please" is input to the generative AI model. The inputs to this step are the speech-recognized text data and the generated prompt sentence, and the output is the generated response text.

[1048] Step 5:

[1049] The server sends the generated response text to the terminal, which projects multiple response options onto a display device. The user selects an appropriate response by swiping the touch frame of the smart glasses. The input of this step is the generated response text, and the output is the response option selected by the user.

[1050] Step 6:

[1051] The server passes the user-selected response text to a speech synthesis module, which converts it into speech data. The input is the user-selected response text, and the output is the synthesized speech data.

[1052] Step 7:

[1053] The device plays the synthesized voice data over the built-in speaker, allowing the user to confirm the selected response as voice. The input of this step is the synthesized voice data, and the output is the played voice.

[1054] Through the above processing steps, users can have natural conversations and continue communication smoothly.

[1055] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1056] This invention is a system incorporating an emotion engine to assist users in smooth communication. An embodiment of this system and the processing of its program will be described below.

[1057] System Configuration

[1058] This system consists of the following main components:

[1059] 1. Terminal: Smart glasses worn by the user, a device for voice input, display, and swiping operations.

[1060] 2. Speech recognition module: A software component that converts voice data collected on the device into text data.

[1061] 3. Microprojector: A hardware component installed in the device that projects text data onto the glasses' display.

[1062] 4. Speech synthesis module: A software component that converts text data into speech data and plays it through the built-in speaker.

[1063] 5. Sensor: A hardware component that detects user swipes.

[1064] 6. Emotion Engine: A component for real-time emotion recognition from the user's voice, facial expressions, and other biometric data.

[1065] Program processing flow

[1066] The program executes the process in the following steps:

[1067] 1. Collecting voice input

[1068] The device uses a built-in microphone to collect the user's voice in real time. For example, when the user says, "I'd like to place an order," the voice data is recorded.

[1069] 2. Preprocessing of audio data

[1070] The device pre-processes the collected audio data for noise cancellation and volume normalization, which improves the accuracy of the audio data.

[1071] 3. Voice Recognition

[1072] The device then passes the pre-processed voice data to the speech recognition module, which uses the latest edge computing AI to convert the speech into text, such as "Please place an order."

[1073] 4. Text Projection

[1074] The device receives the text data output from the voice recognition module and sends it to a microprojector, which projects the text onto the display of the glasses. The user can see the text "Please place your order" on the display.

[1075] 5. Emotion recognition

[1076] The device sends collected voice and other biometric data to the emotion engine, which analyzes in real time whether the user is expressing any emotion. For example, if the user is likely to be nervous, that emotional state is identified.

[1077] 6. Response Selection

[1078] The device displays appropriate response options based on the user's emotional state, and the user can select the desired response by swiping the frame.

[1079] 7. Text-to-speech

[1080] The device passes the user's selected response to a speech synthesis module, which converts it into voice data. The speech synthesis module then plays the response in a tone that adapts to the user's emotional state. For example, it might play "thank you" in a gentle voice.

[1081] 8. Prepare for the next input

[1082] After the process is complete, the device will return to standby mode for the next voice input. If the user speaks again, the system will be ready to recognize the input immediately.

[1083] Specific examples

[1084] Ordering at a restaurant

[1085] 1. Voice Input: The user says, "One cheeseburger, please."

[1086] 2. Speech recognition: The device converts this speech into text, generating the text "One cheeseburger please."

[1087] 3. Text projection: The device projects the generated text onto the glasses' display.

[1088] 4. Emotion recognition: The device analyzes the user's tense tone of voice, and the emotion engine detects the sense of tension.

[1089] 5. Response selection: User swipes the frame to select "Thank you."

[1090] 6. Voice Playback: The device uses the emotion engine to play back a voice saying "Thank you" in a gentle tone to ease tension.

[1091] 7. Standby: The device returns to standby mode for the next voice input.

[1092] In this way, the system analyzes the user's emotional state and provides appropriate responses, supporting more natural and smooth communication.The same process can be used in other scenarios, making it widely applicable.

[1093] The processing flow will be explained below.

[1094] Step 1:

[1095] The device uses a built-in microphone to collect the user's voice in real time. For example, if the user says, "I'd like a cheeseburger, please," the voice data will be recorded.

[1096] Step 2:

[1097] The device pre-processes the collected audio data for noise cancellation and volume normalization, which improves the accuracy of the audio data.

[1098] Step 3:

[1099] The device then passes the pre-processed voice data to a speech recognition module, which uses the latest edge computing AI to convert the speech into text, such as "One cheeseburger, please."

[1100] Step 4:

[1101] The device receives the text data output from the voice recognition module and sends it to the microprojector, which projects the text onto the display of the glasses. The user can see the text "One cheeseburger, please" on the display.

[1102] Step 5:

[1103] The device sends the collected voice data and other biometric data to the emotion engine, which analyzes the user's emotional state in real time, such as whether they are nervous or relaxed. For example, tension can be detected from the user's tone of voice and speaking style.

[1104] Step 6:

[1105] The device displays multiple appropriate response options based on the emotional state recognized by the emotion engine. If the emotion engine detects the user's tension, it displays response options to relax them. The user swipes the frame to select a response, such as "Thank you."

[1106] Step 7:

[1107] The device uses sensors to detect swiping on the frame, identifies the user's selected response, and passes the selected text "Thank you" to a speech synthesis module for conversion into voice data.

[1108] Step 8:

[1109] The device uses an emotion engine to play a synthesized "thank you" in a tone that adapts to the user's emotional state. For example, if the user is nervous, the device will play a "thank you" in a calm, gentle voice.

[1110] Step 9:

[1111] After the process is complete, the device will return to standby mode for the next voice input. If the user speaks again, the system will be ready to recognize the input immediately.

[1112] This process allows the system to take the user's emotions into account and provide appropriate responses. For example, if the device detects nervousness when ordering at a restaurant, it will respond with a voice response in a relaxing tone, allowing the user to communicate with ease.

[1113] Example 2

[1114] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1115] Conventional speech recognition systems simply convert speech into text without understanding the user's emotions, making it difficult to provide appropriate responses. This can lead to awkward communication and makes it difficult to provide appropriate assistance to users who are particularly nervous. Furthermore, few systems consistently perform advanced processing, such as preprocessing of speech data and real-time emotion analysis.

[1116] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1117] In this invention, the server includes means for converting a user's voice into text in real time, means for projecting the converted text onto a display device, and preprocessing means for collecting the user's voice data and performing noise reduction and volume normalization. This enables the user's voice to be recognized and converted into text with high accuracy. The server also includes means for analyzing the user's emotions in real time and means for providing appropriate response options based on the analyzed emotions. This allows the server to understand the user's emotional state and provide appropriate responses accordingly, enabling smooth communication.

[1118] "User" means any person who uses the System.

[1119] "Voice" refers to the words or voices spoken by the user.

[1120] "Real time" refers to a state in which processing is performed almost simultaneously.

[1121] "Text" refers to the text information converted from audio data.

[1122] "Means" refers to a device or program that performs a particular function or role.

[1123] "Display device" means a device that visually presents information to a user.

[1124] "Projection" refers to displaying the generated text on a display device.

[1125] "Multiple response options" refers to multiple responses that a user can choose from.

[1126] "Selection" refers to the act of a user choosing one option from multiple options.

[1127] "Speech synthesis" refers to the technology of converting text data into voice data.

[1128] "Playback" refers to outputting synthesized sound through a speaker or the like.

[1129] "Emotion" refers to the user's psychological state (e.g., tension, joy, sadness, etc.).

[1130] "Analysis" refers to extracting and understanding information from collected data.

[1131] "Providing" refers to the act of making information or functionality available to users.

[1132] "Noise reduction" refers to the technology of removing unnecessary noise from audio data.

[1133] "Volume normalization" refers to a technique for adjusting the volume of audio data to a constant level.

[1134] "Preprocessing" refers to the preparation and formatting of data before the main processing.

[1135] "Sensor" refers to a device that detects user operations and situations.

[1136] A "swipe" refers to the action of a user sliding their finger across the surface of a display or device.

[1137] This invention is a system that converts voice input into text, recognizes emotions, presents response options, and plays synthesized speech using a smart glass-type device worn by the user. This system uses the following hardware and software to achieve specific functions.

[1138] Hardware Configuration

[1139] 1. Device: Smart glasses worn by the user, containing a built-in microphone, display, microprojector, and sensors.

[1140] 2. Micro-projector: Installed in the device, it projects the converted text onto the glasses' display.

[1141] 3. Sensor: A device that detects swiping operations and recognizes user actions.

[1142] 4. Speaker: Used to play the voice output from the speech synthesis module.

[1143] Software Configuration

[1144] 1. Speech Recognition Module: Software for converting user speech into text, for example, using edge computing AI technology (e.g., Google Speech-to-Text API).

[1145] 2. Emotion Engine: Software for analyzing user emotions in real time, using Affectiva SDK as an example.

[1146] 3. Speech synthesis module: Software for converting text data into speech data. We use Amazon Polly as an example.

[1147] 4. Preprocessing program: Software that denoises and normalizes the volume of audio data, using the Spectral Subtraction algorithm as an example.

[1148] Specific examples of processing

[1149] The operation of this system will be explained based on a specific example.

[1150] Ordering at a restaurant

[1151] 1. Voice Input: The user says, "One cheeseburger, please."

[1152] 2. Audio data preprocessing: The device performs noise cancellation and volume normalization on this audio data.

[1153] 3. Speech recognition: The device passes the preprocessed speech data to the Google Speech-to-Text API, generating text data such as "One cheeseburger, please."

[1154] 4. Text projection: The device sends the generated text data to the micro-projector, which projects it onto the smart glasses display.

[1155] 5. Emotion Recognition: The device analyzes voice and biometric data using the Affectiva SDK to detect when the user is nervous.

[1156] 6. Response selection: The user swipes to select "Thank you" from the response options displayed on the display.

[1157] 7. Play Voice: The device converts the text to speech using Amazon Polly and plays "Thank you" in a calming tone to ease tension.

[1158] 8. Prepare for next input: The device returns to standby mode for the next voice input.

[1159] Prompt Sentence Examples

[1160] "Please explain in detail the processing steps of an emotion recognition and response system for a nervous user when ordering at a restaurant."

[1161] As described above, this system integrates hardware and software to convert a user's voice into text with high accuracy and provide appropriate responses according to their emotions.

[1162] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1163] Step 1:

[1164] Collecting voice input

[1165] The device uses a built-in microphone to collect the user's voice in real time. For example, when the user says, "I'd like to place an order," the voice data is collected and input into the next step.

[1166] Step 2:

[1167] Audio data preprocessing

[1168] The device performs noise reduction and volume normalization on the collected audio data. Specifically, it applies a noise reduction algorithm (e.g., Spectral Subtraction) to reduce background noise. At the same time, it normalizes volume peaks to ensure that all sounds are at a comfortable level to hear. This preprocessed audio data is then input to the next step.

[1169] Step 3:

[1170] Voice Recognition

[1171] The device passes the preprocessed voice data to a speech recognition module (e.g., Google Speech-to-Text API) to convert the voice into text data. This conversion generates the text data "Please place your order." This text data is input to the next step.

[1172] Step 4:

[1173] Text projection

[1174] The terminal sends the text data obtained from the voice recognition module to the micro-projector, which projects the text onto the smart glasses' display. Specifically, the text "Please place your order" is visually displayed on the display. This displayed text is input into the next step.

[1175] Step 5:

[1176] emotion recognition

[1177] The device sends collected voice data and other biometric data (e.g., heart rate, galvanic skin response) to an emotion engine (e.g., Affectiva SDK). The emotion engine analyzes this data and identifies the user's emotional state (e.g., nervousness, joy, sadness, etc.) in real time. For example, if the user is nervous, that emotional state is identified. This emotional state data is input into the next step.

[1178] Step 6:

[1179] Response Selection

[1180] Based on the analysis results from the emotion engine, the device displays appropriate response options on the display. Specifically, options such as "Thank you" or "Please wait a little longer" are displayed on the display. The user selects the desired response option by swiping the frame. This selected response option is input into the next step.

[1181] Step 7:

[1182] Text to speech

[1183] The device sends the selected response to a speech synthesis module (e.g., Amazon Polly) to convert the text data into speech. Based on the analysis results of the emotion engine, the speech synthesis module plays a response with the corresponding emotional tone. Specifically, it plays "Thank you" in a gentle tone to ease tension. This played speech is then output to the next step.

[1184] Step 8:

[1185] Prepare for the next input

[1186] After all processing is completed, the device returns to standby mode for the next voice input. When the user speaks again, the system immediately collects the voice and is ready to resume processing from the first step.

[1187] (Application example 2)

[1188] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1189] Conventional communication support systems provided the ability to convert a user's voice into text in real time and display the text, but lacked the ability to identify the emotions of the user or the person they were speaking to and select and provide an appropriate response. This resulted in issues such as communication not proceeding smoothly when the user or customer was nervous or an inappropriate response was selected. Furthermore, the process of synthesizing and playing back the selected response was often unnatural, which could detract from the user experience.

[1190] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for converting a user's voice into text in real time, means for projecting the converted text on a display device, means for displaying multiple response options and allowing the user to select any response, means for synthesizing the selected response and playing it back, means for analyzing the voice data of the user and the conversation partner and identifying emotions, and means for providing appropriate response options based on the identified emotions. This makes it possible to grasp the emotional states of the user and the conversation partner and select and provide appropriate responses, thereby making communication smoother and improving the user experience.

[1191] "User" means any person who operates the System.

[1192] "Real-time speech-to-text means" means a technical method for instantly converting speech uttered by a User into text form.

[1193] "Means for projecting the converted text onto a display device" refers to a technical method for displaying audio data converted into text format on a display device such as a display.

[1194] "A means of displaying multiple response options and allowing the user to select any response" refers to a technical method by which the system displays several pre-prepared response options and the user selects the one they deem appropriate.

[1195] "Means for synthesizing and playing the selected response" means a technical means for synthesizing a text response selected by a user as speech and playing that speech.

[1196] "Means for analyzing the voice data of the user and the conversation partner and identifying emotions" refers to a technical method for analyzing the voice data uttered by the user and the conversation partner and identifying the emotions contained in the voice.

[1197] "Means for providing appropriate response options based on the identified emotion" refers to a technical method by which the system presents response options appropriate to the emotional state determined by the analysis.

[1198] This invention is a system designed to assist users in smoothly carrying out communication. A specific implementation method of this system will be described below.

[1199] System hardware configuration

[1200] The system mainly consists of the following hardware components:

[1201] 1. Smart Glasses:

[1202] Built-in microphone

[1203] Display (micro projector)

[1204] Sensor that detects swipe operations

[1205] 2. Server:

[1206] Voice Recognition Module

[1207] Emotion Engine

[1208] Speech Synthesis Module

[1209] Software Components

[1210] The system consists of the following software components:

[1211] 1. Speech Recognition Module:

[1212] Software for converting speech to text in real time. For example, you can use the speech_recognition library.

[1213] 2. Emotion Engine:

[1214] Software that identifies emotions from voice and other biometric data, using machine learning models and APIs.

[1215] 3. Speech synthesis module:

[1216] Software for converting text to speech. For example, the gTTS library can be used.

[1217] System Operation

[1218] 1. Collecting voice input:

[1219] The server collects the user's voice in real time using the smart glasses' built-in microphone.

[1220] 2. Audio preprocessing:

[1221] The server pre-processes the collected audio data for noise cancellation and volume normalization.

[1222] 3. Speech Recognition:

[1223] The server passes the preprocessed speech data to a speech recognition module and converts it into text.

[1224] 4. Emotion recognition:

[1225] The server passes the voice data to an emotion engine to identify the emotions of the user and the conversation partner.

[1226] 5. Response Selection:

[1227] The server displays appropriate response options on the smart glasses display based on the identified emotion.

[1228] 6. Response speech synthesis:

[1229] The server passes the user's selected text response to a speech synthesis module, which converts it into speech and plays it back.

[1230] Specific examples

[1231] Restaurant service

[1232] 1. The staff member says, "I have a question about the menu."

[1233] 2. The smart glasses collect the audio and send it to the server.

[1234] 3. The server converts the speech into text and displays, "I have a question about the menu."

[1235] 4. The emotion engine detects customer tension.

[1236] 5. The server suggests appropriate response options: "Please feel free to ask any questions" and "Don't be nervous, it's okay."

[1237] 6. The staff member will select "Please ask any questions" and play it back using voice synthesis.

[1238] Prompt Sentence Examples

[1239] Customer: "I have a question about the menu."

[1240] System: "Tension detected"

[1241] System: "You have the following response options: 1. Please feel free to ask any questions. 2. Don't be nervous, it's okay."

[1242] The staff member selected "Please ask questions."

[1243] The system will play a voice message saying "Please feel free to ask any questions."

[1244] In this way, the system analyzes the emotional state of the user and the person they are talking to in real time and provides appropriate response options, thereby enabling more natural and smooth communication.

[1245] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1246] Step 1:

[1247] The server collects the user's voice in real time using the smart glasses' built-in microphone. The input is the user's voice data, and the output is the raw voice data. This voice data becomes the basis for subsequent processing.

[1248] Step 2:

[1249] The server performs preprocessing of noise cancellation and volume normalization on the collected audio data. The input is the audio data obtained in step 1, and the output is the preprocessed audio data. Specifically, it removes background noise from the audio data and adjusts the volume to a certain level.

[1250] Step 3:

[1251] The server passes the preprocessed voice data to a voice recognition module, which converts the voice into text in real time. The input is the preprocessed voice data, and the output is text data. Specifically, a voice recognition library (e.g., speech_recognition) is used to generate text such as "I have a question about the menu."

[1252] Step 4:

[1253] The server passes the text data to the emotion engine to identify the emotions of the user and the person interacting with them. The input is text data and voice data, and the output is the identified emotional state (e.g., nervous). Specifically, an emotion recognition algorithm is used to determine emotions from the tone and content of the voice.

[1254] Step 5:

[1255] The server then displays appropriate response options on the smart glasses display based on the identified emotional state. The input is the emotional state and pre-prepared response options, and the output is the response options displayed on the display. Specifically, options such as "Please feel free to ask any questions" and "Don't be nervous, it's okay" are displayed.

[1256] Step 6:

[1257] The user swipes the frame of the smart glasses to select an appropriate response from the displayed response options. The input is the response option on the display, and the output is the selected response option. Specifically, a sensor detects the swipe action, and the selected option is sent to the server.

[1258] Step 7:

[1259] The server passes the selected response option to the speech synthesis module, converts the text into speech, and plays it back. The input is the text data of the selected response option, and the output is audio data. Specifically, the gTTS library is used to play back the phrase "Please ask any questions."

[1260] Step 8:

[1261] The server returns to standby mode to prepare for the next voice input. There is no particular input, and the output is in the system standby state. Specifically, the system returns to the state of waiting for voice input again.

[1262] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1263] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1264] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1265] [Fourth embodiment]

[1266] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1267] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1268] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1269] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1270] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1271] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1272] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1273] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1274] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1275] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1276] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1277] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1278] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1279] The present invention is a system for supporting users in smoothly carrying out communication. An embodiment of this system and the processing of its program will be described below.

[1280] System Configuration

[1281] This system consists of the following main components:

[1282] 1. Terminal: Smart glasses worn by the user, a device for voice input, display, and swiping operations.

[1283] 2. Speech recognition module: A software component that converts voice data collected on the device into text data.

[1284] 3. Microprojector: A hardware component installed in the device that projects text data onto the glasses' display.

[1285] 4. Speech synthesis module: A software component that converts text data into speech data and plays it through the built-in speaker.

[1286] 5. Sensor: A hardware component that detects user swipes.

[1287] Program processing flow

[1288] The program executes the process in the following steps:

[1289] 1. Collecting voice input

[1290] The device uses a built-in microphone to collect the user's voice. For example, when the user says, "I'd like to order," the voice data is stored in a buffer.

[1291] 2. Preprocessing of audio data

[1292] The terminal performs preprocessing on the collected audio data to remove noise and normalize the volume.

[1293] 3. Voice Recognition

[1294] The device passes the preprocessed voice data to a voice recognition module, which converts it into text data. For example, the generated text is "Please place an order."

[1295] 4. Text Projection

[1296] The device sends the generated text data to a microprojector, which projects it onto the display of the glasses, where the user can see the text "Please place your order."

[1297] 5. Response Selection

[1298] The device displays multiple response options on the Glass, and the user selects the appropriate response by swiping the frame, for example, "Thank you."

[1299] 6. Text-to-speech

[1300] The device passes the user's selected text to a speech synthesis module, converts it into audio data, and plays the voice saying "Thank you" using the built-in speaker.

[1301] 7. Prepare for the next input

[1302] After completing this series of processes, the device returns to standby mode and is ready for the next voice input.

[1303] Specific examples

[1304] Ordering at a restaurant

[1305] 1. Voice Input: The user says, "One cheeseburger, please."

[1306] 2. Speech recognition: The device converts this speech into text, generating the text "One cheeseburger please."

[1307] 3. Text projection: The device projects the generated text onto the glasses' display.

[1308] 4. Response selection: User swipes the frame to select "Thank you."

[1309] 5. Play audio: The device will play "Thank you" aloud.

[1310] 6. Standby: The device returns to standby mode to prepare for the next voice input.

[1311] In this way, the system allows users to communicate naturally without speaking, reducing the stress of face-to-face conversations and ordering.The system can be used in a similar process in other scenarios, making it widely applicable.

[1312] The processing flow will be explained below.

[1313] Step 1:

[1314] The device uses a built-in microphone to collect the user's voice in real time. For example, when the user says, "I'd like to place an order," the voice data is recorded.

[1315] Step 2:

[1316] The device pre-processes the collected audio data for noise cancellation and volume normalization, which improves the accuracy of the audio data.

[1317] Step 3:

[1318] The device then passes the pre-processed voice data to the speech recognition module, which uses the latest edge computing AI to convert the speech into text, such as "Please place an order."

[1319] Step 4:

[1320] The device receives the text data output from the voice recognition module and sends it to a microprojector, which projects the text onto the display of the glasses. The user can see the text "Please place your order" on the display.

[1321] Step 5:

[1322] The device displays multiple response options on the display for the user to choose from, and the user can select the desired response by swiping the frame.

[1323] Step 6:

[1324] The device uses sensors to detect swiping on the frame and identify the response selected by the user, for example, selecting "Thank you."

[1325] Step 7:

[1326] The terminal passes the selected text "Thank you" to the speech synthesis module, which converts it into voice data. The speech synthesis module generates "Thank you" in a natural speaking voice.

[1327] Step 8:

[1328] The device will play a synthesized voice of "Thank you" through the built-in speaker, allowing users to say "Thank you" silently.

[1329] Step 9:

[1330] After the process is complete, the device will return to standby mode for the next voice input. If the user speaks again, the system will be ready to recognize the input immediately.

[1331] Example 1

[1332] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1333] Conventional voice input systems often suffer from poor voice recognition accuracy, especially in noisy environments, preventing users from operating as intended. Users often experience inconvenience when confirmation or manual operation is required after voice input. There is a need for a system that can solve these problems and enable users to use voice input without stress.

[1334] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1335] In this invention, the server includes means for converting a user's voice into text in real time, means for projecting the converted text onto a display device, means for displaying multiple response options and allowing the user to select a desired response, means for synthesizing and playing back the selected response, preprocessing means for noise reduction and volume normalization of the voice data, and projection means for projecting the text onto a display device, thereby enabling highly accurate voice recognition and stress-free usability.

[1336] "User" refers to the individual who operates the system and provides instructions and input.

[1337] "Real-time speech-to-text conversion means" refers to technology or devices that instantly convert a user's voice input into text data.

[1338] "Means for projecting the converted text onto a display device" refers to a device, such as a projector or screen, that visually displays the text data generated by speech.

[1339] "Response options" refers to multiple possible responses or actions that a user can choose from.

[1340] "Means for synthesizing and playing back speech" refers to technology or devices that convert text data into speech and play it back in a format that can be heard by the user.

[1341] "Pre-processing means for noise reduction and volume normalization" refers to techniques and devices for removing unwanted noise from audio signals and for maintaining a consistent volume level.

[1342] "Projection means" refers to the technology or equipment used to project digital data onto a physical display device.

[1343] "Sensor for detecting swiping of a frame" refers to a sensor device for detecting a swipe action performed by a user on a frame.

[1344] This invention is a system for helping users communicate smoothly. It converts the user's speech into text in real time, projects the text on a display device, and displays multiple response options. The response selected by the user is played back as a synthesized voice.

[1345] System Configuration

[1346] This system consists of the following main components:

[1347] 1. Terminal: Smart glasses worn by the user, a device for voice input, display, and swiping operations.

[1348] 2. Speech recognition module: A software component that converts voice data collected on the device into text data.

[1349] 3. Microprojector: A hardware component installed in the device that projects text data onto the glasses' display.

[1350] 4. Speech synthesis module: A software component that converts text data into speech data and plays it through the built-in speaker.

[1351] 5. Sensor: A hardware component that detects user swipes.

[1352] Program processing flow

[1353] Collecting voice input

[1354] The device uses a built-in microphone to collect the user's voice. For example, when the user says, "I'd like to place an order," this voice data is temporarily stored in a buffer.

[1355] Audio data preprocessing

[1356] The device preprocesses the collected audio data to remove noise and normalize the volume, which is important to ensure accurate speech recognition.

[1357] Voice Recognition

[1358] The device passes the preprocessed voice data to a voice recognition module, which converts it into text data. For example, the generated text is "Please place an order."

[1359] Text projection

[1360] The device sends the generated text data to a micro-projector and projects it onto the user's smart glasses display, where the user can see the text "Please place your order."

[1361] Response Selection

[1362] The device displays multiple response options on the display, and the user swipes the frame to select the appropriate response, such as "Thank you."

[1363] Text to speech

[1364] The device passes the user's selected text to a speech synthesis module, converts it into audio data, and plays the voice saying "Thank you" using the built-in speaker.

[1365] Prepare for the next input

[1366] After completing the series of processes, the device returns to a standby state to prepare for the next voice input, and the device is ready for the user to input voice again.

[1367] Specific examples

[1368] Ordering at a restaurant

[1369] 1. Voice Input: The user says, "One cheeseburger, please."

[1370] 2. Speech recognition: The device converts this speech into text, generating the text "One cheeseburger please."

[1371] 3. Text projection: The device projects the generated text onto the glasses' display.

[1372] 4. Response selection: User swipes the frame to select "Thank you."

[1373] 5. Play audio: The device will play "Thank you" aloud.

[1374] 6. Standby: The device returns to standby mode to prepare for the next voice input.

[1375] Prompt Sentence Examples

[1376] "Please explain in detail each processing step of your system, especially how you use voice data preprocessing and speech recognition techniques."

[1377] "Please explain specifically how the device collects and processes voice input and provides feedback to the user."

[1378] In this way, the system allows users to communicate naturally without speaking, reducing the stress of face-to-face conversations and ordering.The system can be used in a similar process in other scenarios, making it widely applicable.

[1379] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1380] Step 1: Collecting voice input

[1381] The device uses a built-in microphone to collect the user's voice.

[1382] Input: User speech (e.g., "Please place my order")

[1383] Data processing: Storing audio data in a buffer

[1384] Output: Accumulated audio data

[1385] Specifically, the device's microphone is always on, and when voice input is detected, the voice data is stored in a buffer.

[1386] Step 2: Preprocessing the audio data

[1387] The device preprocesses the collected audio data to remove noise and normalize the volume.

[1388] Input: Stored voice data

[1389] Data processing: noise removal, volume normalization

[1390] Output: Preprocessed audio data

[1391] Specifically, the terminal uses an audio filtering algorithm to filter out background noise and adjust the volume to a consistent level.

[1392] Step 3: Voice Recognition

[1393] The terminal passes the pre-processed voice data to a voice recognition module and converts it into text data.

[1394] Input: Preprocessed audio data

[1395] Data calculation: The process of converting voice data into text data

[1396] Output: The converted text (e.g. "Please place your order").

[1397] Specifically, the device uses an API (e.g., Google Speech-to-Text API) to convert voice to text.

[1398] Step 4: Projecting text

[1399] The terminal transmits the generated text data to the micro projector and projects it onto the display.

[1400] Input: The converted text (e.g., "Please place my order").

[1401] Data processing: Formatting text data for display

[1402] Output: Text projected on a display

[1403] Specifically, the device formats the text for projection via a dedicated display driver and projects it onto the smart glasses display.

[1404] Step 5: Select a response

[1405] The terminal displays a number of response options on the display, and the user selects any one of them.

[1406] Input: Response options (e.g., "Thank you" or "Cancel my order")

[1407] Data processing: Display response options on the display

[1408] Output: The response selected by the user (e.g. "Thank you")

[1409] Specifically, sensors embedded in the frame detect the user's swiping actions and select a response based on that information.

[1410] Step 6: Read text aloud

[1411] The terminal passes the selected text to a speech synthesis module to convert it into voice data and play it back.

[1412] Input: Selected response text (e.g. "Thank you")

[1413] Data calculation: The process of converting text data into audio data

[1414] Output: The audio to be played (e.g. "Thank you")

[1415] Specifically, the device uses an API (e.g., Google Text-to-Speech API) to convert text into speech and plays it through the built-in speaker.

[1416] Step 7: Prepare for the next input

[1417] After completing the series of processes, the terminal returns to a standby state to prepare for the next voice input.

[1418] Input: None (standby)

[1419] Data processing: Wait for next voice input

[1420] Output: Ready and waiting

[1421] Specifically, the system returns to the voice input mode and waits for the input trigger word again.

[1422] (Application example 1)

[1423] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1424] Conventional communication support systems have the problem that the process of converting speech to text and allowing users to select their own responses places a heavy burden on users, making it difficult to maintain a smooth dialogue. Furthermore, the limited knowledge required to generate appropriate responses makes it difficult to respond flexibly. For these reasons, there is a growing need for a system that allows users to continue a dialogue naturally and provides appropriate responses instantly.

[1425] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1426] In this invention, the server includes means for converting a user's voice into text in real time, means for projecting the converted text onto a display device, means for displaying multiple response options and allowing the user to select a desired response, means for synthesizing the selected response and playing it back, and means for generating a response based on a prompt sentence, including a generative AI model for generating an appropriate response based on the content of the user's voice. This allows the user to instantly obtain an appropriate response while smoothly engaging in dialogue.

[1427] "User" refers to the person who operates the system and provides voice input.

[1428] "Means for converting speech to text in real time" refers to a component that has the functionality to instantly convert a user's speech data into text data.

[1429] "Display device" means a device for visually presenting textual data and response options to a user.

[1430] The "means for projecting" refers to a function for displaying text data on a display device.

[1431] "Multiple response options" refers to a set of selectable responses that the system offers to the user.

[1432] "Means for selection" refers to a component that has the functionality to allow a user to select any response from among multiple response options.

[1433] "Means for synthesizing and reproducing speech" refers to a component that has the function of converting selected text data into speech data and reproducing it.

[1434] "Generative AI model" refers to an artificial intelligence module that generates appropriate responses based on the content of a user's voice.

[1435] A "prompt sentence" is text data input into a generative AI model, and refers to an instruction sentence used to derive an appropriate response.

[1436] A system for realizing this application example includes the following components and means:

[1437] First, the user wears a display device called smart glasses. These smart glasses have built-in microphones and speakers, and are equipped with a means for collecting voice input. Voice data spoken by the user is collected through the built-in microphone and converted into text in real time. High recognition accuracy is achieved by using the Google Speech Recognition API for voice recognition. The converted text data is instantly projected onto the smart glasses' display. The display device is equipped with a projection means to provide a visual representation of the text data.

[1438] Next, multiple response options are generated based on the speech content and displayed on the display. The response options are generated using a generative AI model. This generative AI model creates a prompt based on the speech content of the user and derives an appropriate response based on the prompt. The user can select any response from the multiple response options by swiping the touch frame of the smart glasses. This selection method prepares the selected response for voice synthesis and playback. The selected response is then converted into voice data using speech synthesis technology and played through the built-in speaker. Software called pyttsx3 is used for speech synthesis.

[1439] In this system, the user's voice data is pre-processed to remove noise and normalize the volume, which allows for more accurate voice recognition, allowing users to continue dialogue easily and quickly, improving work efficiency.

[1440] Hardware and Software Examples

[1441] Smart glasses: Equipped with a display, microphone, speaker, and touch sensor.

[1442] Speech recognition software: Google Speech Recognition API.

[1443] Speech synthesis software: pyttsx3.

[1444] Natural Language Processing model: OpenAI GPT-3.

[1445] Specific examples

[1446] Situation: A customer orders, "One cheeseburger, please."

[1447] System response: "Your order has been accepted. Thank you."

[1448] Prompt Sentence Examples

[1449] Generate appropriate responses to customer orders.

[1450] Order: One cheeseburger, please

[1451] Example response:

[1452] This will generate a natural-sounding response such as "Your order has been accepted. Thank you."

[1453] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1454] Step 1:

[1455] The device collects the user's voice using a built-in microphone. When the user speaks, the device stores the voice data in a buffer. The input of this step is the user's voice, and the output is the collected voice data.

[1456] Step 2:

[1457] The terminal processes the collected audio data using preprocessing means that perform noise reduction and volume normalization. The input to this step is the audio data collected in step 1, and the output is audio data with noise reduction and volume normalization.

[1458] Step 3:

[1459] The server passes the preprocessed audio data to the Google Speech Recognition API and converts it into text data. The input is noise-removed and volume-normalized audio data, and the output is text data.

[1460] Step 4:

[1461] The server inputs a prompt sentence to the generative AI model based on the text data. For example, a prompt sentence such as "Please generate an appropriate response to the customer's order. Order: One cheeseburger, please" is input to the generative AI model. The inputs to this step are the speech-recognized text data and the generated prompt sentence, and the output is the generated response text.

[1462] Step 5:

[1463] The server sends the generated response text to the terminal, which projects multiple response options onto a display device. The user selects an appropriate response by swiping the touch frame of the smart glasses. The input of this step is the generated response text, and the output is the response option selected by the user.

[1464] Step 6:

[1465] The server passes the user-selected response text to a speech synthesis module, which converts it into speech data. The input is the user-selected response text, and the output is the synthesized speech data.

[1466] Step 7:

[1467] The device plays the synthesized voice data over the built-in speaker, allowing the user to confirm the selected response as voice. The input of this step is the synthesized voice data, and the output is the played voice.

[1468] Through the above processing steps, users can have natural conversations and continue communication smoothly.

[1469] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1470] This invention is a system incorporating an emotion engine to assist users in smooth communication. An embodiment of this system and the processing of its program will be described below.

[1471] System Configuration

[1472] This system consists of the following main components:

[1473] 1. Terminal: Smart glasses worn by the user, a device for voice input, display, and swiping operations.

[1474] 2. Speech recognition module: A software component that converts voice data collected on the device into text data.

[1475] 3. Microprojector: A hardware component installed in the device that projects text data onto the glasses' display.

[1476] 4. Speech synthesis module: A software component that converts text data into speech data and plays it through the built-in speaker.

[1477] 5. Sensor: A hardware component that detects user swipes.

[1478] 6. Emotion Engine: A component for real-time emotion recognition from the user's voice, facial expressions, and other biometric data.

[1479] Program processing flow

[1480] The program executes the process in the following steps:

[1481] 1. Collecting voice input

[1482] The device uses a built-in microphone to collect the user's voice in real time. For example, when the user says, "I'd like to place an order," the voice data is recorded.

[1483] 2. Preprocessing of audio data

[1484] The device pre-processes the collected audio data for noise cancellation and volume normalization, which improves the accuracy of the audio data.

[1485] 3. Voice Recognition

[1486] The device then passes the pre-processed voice data to the speech recognition module, which uses the latest edge computing AI to convert the speech into text, such as "Please place an order."

[1487] 4. Text Projection

[1488] The device receives the text data output from the voice recognition module and sends it to a microprojector, which projects the text onto the display of the glasses. The user can see the text "Please place your order" on the display.

[1489] 5. Emotion recognition

[1490] The device sends collected voice and other biometric data to the emotion engine, which analyzes in real time whether the user is expressing any emotion. For example, if the user is likely to be nervous, that emotional state is identified.

[1491] 6. Response Selection

[1492] The device displays appropriate response options based on the user's emotional state, and the user can select the desired response by swiping the frame.

[1493] 7. Text-to-speech

[1494] The device passes the user's selected response to a speech synthesis module, which converts it into voice data. The speech synthesis module then plays the response in a tone that adapts to the user's emotional state. For example, it might play "thank you" in a gentle voice.

[1495] 8. Prepare for the next input

[1496] After the process is complete, the device will return to standby mode for the next voice input. If the user speaks again, the system will be ready to recognize the input immediately.

[1497] Specific examples

[1498] Ordering at a restaurant

[1499] 1. Voice Input: The user says, "One cheeseburger, please."

[1500] 2. Speech recognition: The device converts this speech into text, generating the text "One cheeseburger please."

[1501] 3. Text projection: The device projects the generated text onto the glasses' display.

[1502] 4. Emotion recognition: The device analyzes the user's tense tone of voice, and the emotion engine detects the sense of tension.

[1503] 5. Response selection: User swipes the frame to select "Thank you."

[1504] 6. Voice Playback: The device uses the emotion engine to play back a voice saying "Thank you" in a gentle tone to ease tension.

[1505] 7. Standby: The device returns to standby mode for the next voice input.

[1506] In this way, the system analyzes the user's emotional state and provides appropriate responses, supporting more natural and smooth communication.The same process can be used in other scenarios, making it widely applicable.

[1507] The processing flow will be explained below.

[1508] Step 1:

[1509] The device uses a built-in microphone to collect the user's voice in real time. For example, if the user says, "I'd like a cheeseburger, please," the voice data will be recorded.

[1510] Step 2:

[1511] The device pre-processes the collected audio data for noise cancellation and volume normalization, which improves the accuracy of the audio data.

[1512] Step 3:

[1513] The device then passes the pre-processed voice data to a speech recognition module, which uses the latest edge computing AI to convert the speech into text, such as "One cheeseburger, please."

[1514] Step 4:

[1515] The device receives the text data output from the voice recognition module and sends it to the microprojector, which projects the text onto the display of the glasses. The user can see the text "One cheeseburger, please" on the display.

[1516] Step 5:

[1517] The device sends the collected voice data and other biometric data to the emotion engine, which analyzes the user's emotional state in real time, such as whether they are nervous or relaxed. For example, tension can be detected from the user's tone of voice and speaking style.

[1518] Step 6:

[1519] The device displays multiple appropriate response options based on the emotional state recognized by the emotion engine. If the emotion engine detects the user's tension, it displays response options to relax them. The user swipes the frame to select a response, such as "Thank you."

[1520] Step 7:

[1521] The device uses sensors to detect swiping on the frame, identifies the user's selected response, and passes the selected text "Thank you" to a speech synthesis module for conversion into voice data.

[1522] Step 8:

[1523] The device uses an emotion engine to play a synthesized "thank you" in a tone that adapts to the user's emotional state. For example, if the user is nervous, the device will play a "thank you" in a calm, gentle voice.

[1524] Step 9:

[1525] After the process is complete, the device will return to standby mode for the next voice input. If the user speaks again, the system will be ready to recognize the input immediately.

[1526] This process allows the system to take the user's emotions into account and provide appropriate responses. For example, if the device detects nervousness when ordering at a restaurant, it will respond with a voice response in a relaxing tone, allowing the user to communicate with ease.

[1527] Example 2

[1528] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1529] Conventional speech recognition systems simply convert speech into text without understanding the user's emotions, making it difficult to provide appropriate responses. This can lead to awkward communication and makes it difficult to provide appropriate assistance to users who are particularly nervous. Furthermore, few systems consistently perform advanced processing, such as preprocessing of speech data and real-time emotion analysis.

[1530] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1531] In this invention, the server includes means for converting a user's voice into text in real time, means for projecting the converted text onto a display device, and preprocessing means for collecting the user's voice data and performing noise reduction and volume normalization. This enables the user's voice to be recognized and converted into text with high accuracy. The server also includes means for analyzing the user's emotions in real time and means for providing appropriate response options based on the analyzed emotions. This allows the server to understand the user's emotional state and provide appropriate responses accordingly, enabling smooth communication.

[1532] "User" means any person who uses the System.

[1533] "Voice" refers to the words or voices spoken by the user.

[1534] "Real time" refers to a state in which processing is performed almost simultaneously.

[1535] "Text" refers to the text information converted from audio data.

[1536] "Means" refers to a device or program that performs a particular function or role.

[1537] "Display device" means a device that visually presents information to a user.

[1538] "Projection" refers to displaying the generated text on a display device.

[1539] "Multiple response options" refers to multiple responses that a user can choose from.

[1540] "Selection" refers to the act of a user choosing one option from multiple options.

[1541] "Speech synthesis" refers to the technology of converting text data into voice data.

[1542] "Playback" refers to outputting synthesized sound through a speaker or the like.

[1543] "Emotion" refers to the user's psychological state (e.g., tension, joy, sadness, etc.).

[1544] "Analysis" refers to extracting and understanding information from collected data.

[1545] "Providing" refers to the act of making information or functionality available to users.

[1546] "Noise reduction" refers to the technology of removing unnecessary noise from audio data.

[1547] "Volume normalization" refers to a technique for adjusting the volume of audio data to a constant level.

[1548] "Preprocessing" refers to the preparation and formatting of data before the main processing.

[1549] "Sensor" refers to a device that detects user operations and situations.

[1550] A "swipe" refers to the action of a user sliding their finger across the surface of a display or device.

[1551] This invention is a system that converts voice input into text, recognizes emotions, presents response options, and plays synthesized speech using a smart glass-type device worn by the user. This system uses the following hardware and software to achieve specific functions.

[1552] Hardware Configuration

[1553] 1. Device: Smart glasses worn by the user, containing a built-in microphone, display, microprojector, and sensors.

[1554] 2. Micro-projector: Installed in the device, it projects the converted text onto the glasses' display.

[1555] 3. Sensor: A device that detects swiping operations and recognizes user actions.

[1556] 4. Speaker: Used to play the voice output from the speech synthesis module.

[1557] Software Configuration

[1558] 1. Speech Recognition Module: Software for converting user speech into text, for example, using edge computing AI technology (e.g., Google Speech-to-Text API).

[1559] 2. Emotion Engine: Software for analyzing user emotions in real time, using Affectiva SDK as an example.

[1560] 3. Speech synthesis module: Software for converting text data into speech data. We use Amazon Polly as an example.

[1561] 4. Preprocessing program: Software that denoises and normalizes the volume of audio data, using the Spectral Subtraction algorithm as an example.

[1562] Specific examples of processing

[1563] The operation of this system will be explained based on a specific example.

[1564] Ordering at a restaurant

[1565] 1. Voice Input: The user says, "One cheeseburger, please."

[1566] 2. Audio data preprocessing: The device performs noise cancellation and volume normalization on this audio data.

[1567] 3. Speech recognition: The device passes the preprocessed speech data to the Google Speech-to-Text API, generating text data such as "One cheeseburger, please."

[1568] 4. Text projection: The device sends the generated text data to the micro-projector, which projects it onto the smart glasses display.

[1569] 5. Emotion Recognition: The device analyzes voice and biometric data using the Affectiva SDK to detect when the user is nervous.

[1570] 6. Response selection: The user swipes to select "Thank you" from the response options displayed on the display.

[1571] 7. Play Voice: The device converts the text to speech using Amazon Polly and plays "Thank you" in a calming tone to ease tension.

[1572] 8. Prepare for next input: The device returns to standby mode for the next voice input.

[1573] Prompt Sentence Examples

[1574] "Please explain in detail the processing steps of an emotion recognition and response system for a nervous user when ordering at a restaurant."

[1575] As described above, this system integrates hardware and software to convert a user's voice into text with high accuracy and provide appropriate responses according to their emotions.

[1576] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1577] Step 1:

[1578] Collecting voice input

[1579] The device uses a built-in microphone to collect the user's voice in real time. For example, when the user says, "I'd like to place an order," the voice data is collected and input into the next step.

[1580] Step 2:

[1581] Audio data preprocessing

[1582] The device performs noise reduction and volume normalization on the collected audio data. Specifically, it applies a noise reduction algorithm (e.g., Spectral Subtraction) to reduce background noise. At the same time, it normalizes volume peaks to ensure that all sounds are at a comfortable level to hear. This preprocessed audio data is then input to the next step.

[1583] Step 3:

[1584] Voice Recognition

[1585] The device passes the preprocessed voice data to a speech recognition module (e.g., Google Speech-to-Text API) to convert the voice into text data. This conversion generates the text data "Please place your order." This text data is input to the next step.

[1586] Step 4:

[1587] Text projection

[1588] The terminal sends the text data obtained from the voice recognition module to the micro-projector, which projects the text onto the smart glasses' display. Specifically, the text "Please place your order" is visually displayed on the display. This displayed text is input into the next step.

[1589] Step 5:

[1590] emotion recognition

[1591] The device sends collected voice data and other biometric data (e.g., heart rate, galvanic skin response) to an emotion engine (e.g., Affectiva SDK). The emotion engine analyzes this data and identifies the user's emotional state (e.g., nervousness, joy, sadness, etc.) in real time. For example, if the user is nervous, that emotional state is identified. This emotional state data is input into the next step.

[1592] Step 6:

[1593] Response Selection

[1594] Based on the analysis results from the emotion engine, the device displays appropriate response options on the display. Specifically, options such as "Thank you" or "Please wait a little longer" are displayed on the display. The user selects the desired response option by swiping the frame. This selected response option is input into the next step.

[1595] Step 7:

[1596] Text to speech

[1597] The device sends the selected response to a speech synthesis module (e.g., Amazon Polly) to convert the text data into speech. Based on the analysis results of the emotion engine, the speech synthesis module plays a response with the corresponding emotional tone. Specifically, it plays "Thank you" in a gentle tone to ease tension. This played speech is then output to the next step.

[1598] Step 8:

[1599] Prepare for the next input

[1600] After all processing is completed, the device returns to standby mode for the next voice input. When the user speaks again, the system immediately collects the voice and is ready to resume processing from the first step.

[1601] (Application example 2)

[1602] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1603] Conventional communication support systems provided the ability to convert a user's voice into text in real time and display the text, but lacked the ability to identify the emotions of the user or the person they were speaking to and select and provide an appropriate response. This resulted in issues such as communication not proceeding smoothly when the user or customer was nervous or an inappropriate response was selected. Furthermore, the process of synthesizing and playing back the selected response was often unnatural, which could detract from the user experience.

[1604] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for converting a user's voice into text in real time, means for projecting the converted text on a display device, means for displaying multiple response options and allowing the user to select any response, means for synthesizing the selected response and playing it back, means for analyzing the voice data of the user and the conversation partner and identifying emotions, and means for providing appropriate response options based on the identified emotions. This makes it possible to grasp the emotional states of the user and the conversation partner and select and provide appropriate responses, thereby making communication smoother and improving the user experience.

[1605] "User" means any person who operates the System.

[1606] "Real-time speech-to-text means" means a technical method for instantly converting speech uttered by a User into text form.

[1607] "Means for projecting the converted text onto a display device" refers to a technical method for displaying audio data converted into text format on a display device such as a display.

[1608] "A means of displaying multiple response options and allowing the user to select any response" refers to a technical method by which the system displays several pre-prepared response options and the user selects the one they deem appropriate.

[1609] "Means for synthesizing and playing the selected response" means a technical means for synthesizing a text response selected by a user as speech and playing that speech.

[1610] "Means for analyzing the voice data of the user and the conversation partner and identifying emotions" refers to a technical method for analyzing the voice data uttered by the user and the conversation partner and identifying the emotions contained in the voice.

[1611] "Means for providing appropriate response options based on the identified emotion" refers to a technical method by which the system presents response options appropriate to the emotional state determined by the analysis.

[1612] This invention is a system designed to assist users in smoothly carrying out communication. A specific implementation method of this system will be described below.

[1613] System hardware configuration

[1614] The system mainly consists of the following hardware components:

[1615] 1. Smart Glasses:

[1616] Built-in microphone

[1617] Display (micro projector)

[1618] Sensor that detects swipe operations

[1619] 2. Server:

[1620] Voice Recognition Module

[1621] Emotion Engine

[1622] Speech Synthesis Module

[1623] Software Components

[1624] The system consists of the following software components:

[1625] 1. Speech Recognition Module:

[1626] Software for converting speech to text in real time. For example, you can use the speech_recognition library.

[1627] 2. Emotion Engine:

[1628] Software that identifies emotions from voice and other biometric data, using machine learning models and APIs.

[1629] 3. Speech synthesis module:

[1630] Software for converting text to speech. For example, the gTTS library can be used.

[1631] System Operation

[1632] 1. Collecting voice input:

[1633] The server collects the user's voice in real time using the smart glasses' built-in microphone.

[1634] 2. Audio preprocessing:

[1635] The server pre-processes the collected audio data for noise cancellation and volume normalization.

[1636] 3. Speech Recognition:

[1637] The server passes the preprocessed speech data to a speech recognition module and converts it into text.

[1638] 4. Emotion recognition:

[1639] The server passes the voice data to an emotion engine to identify the emotions of the user and the conversation partner.

[1640] 5. Response Selection:

[1641] The server displays appropriate response options on the smart glasses display based on the identified emotion.

[1642] 6. Response speech synthesis:

[1643] The server passes the user's selected text response to a speech synthesis module, which converts it into speech and plays it back.

[1644] Specific examples

[1645] Restaurant service

[1646] 1. The staff member says, "I have a question about the menu."

[1647] 2. The smart glasses collect the audio and send it to the server.

[1648] 3. The server converts the speech into text and displays, "I have a question about the menu."

[1649] 4. The emotion engine detects customer tension.

[1650] 5. The server suggests appropriate response options: "Please feel free to ask any questions" and "Don't be nervous, it's okay."

[1651] 6. The staff member will select "Please ask any questions" and play it back using voice synthesis.

[1652] Prompt Sentence Examples

[1653] Customer: "I have a question about the menu."

[1654] System: "Tension detected"

[1655] System: "You have the following response options: 1. Please feel free to ask any questions. 2. Don't be nervous, it's okay."

[1656] The staff member selected "Please ask questions."

[1657] The system will play a voice message saying "Please feel free to ask any questions."

[1658] In this way, the system analyzes the emotional state of the user and the person they are talking to in real time and provides appropriate response options, thereby enabling more natural and smooth communication.

[1659] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1660] Step 1:

[1661] The server collects the user's voice in real time using the smart glasses' built-in microphone. The input is the user's voice data, and the output is the raw voice data. This voice data becomes the basis for subsequent processing.

[1662] Step 2:

[1663] The server performs preprocessing of noise cancellation and volume normalization on the collected audio data. The input is the audio data obtained in step 1, and the output is the preprocessed audio data. Specifically, it removes background noise from the audio data and adjusts the volume to a certain level.

[1664] Step 3:

[1665] The server passes the preprocessed voice data to a voice recognition module, which converts the voice into text in real time. The input is the preprocessed voice data, and the output is text data. Specifically, a voice recognition library (e.g., speech_recognition) is used to generate text such as "I have a question about the menu."

[1666] Step 4:

[1667] The server passes the text data to the emotion engine to identify the emotions of the user and the person interacting with them. The input is text data and voice data, and the output is the identified emotional state (e.g., nervous). Specifically, an emotion recognition algorithm is used to determine emotions from the tone and content of the voice.

[1668] Step 5:

[1669] The server then displays appropriate response options on the smart glasses display based on the identified emotional state. The input is the emotional state and pre-prepared response options, and the output is the response options displayed on the display. Specifically, options such as "Please feel free to ask any questions" and "Don't be nervous, it's okay" are displayed.

[1670] Step 6:

[1671] The user swipes the frame of the smart glasses to select an appropriate response from the displayed response options. The input is the response option on the display, and the output is the selected response option. Specifically, a sensor detects the swipe action, and the selected option is sent to the server.

[1672] Step 7:

[1673] The server passes the selected response option to the speech synthesis module, converts the text into speech, and plays it back. The input is the text data of the selected response option, and the output is audio data. Specifically, the gTTS library is used to play back the phrase "Please ask any questions."

[1674] Step 8:

[1675] The server returns to standby mode to prepare for the next voice input. There is no particular input, and the output is in the system standby state. Specifically, the system returns to the state of waiting for voice input again.

[1676] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1677] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1678] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1679] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1680] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1681] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1682] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1683] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, motorcycles, and other devices, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1684] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1685] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1686] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1687] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1688] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1689] 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.

[1690] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1691] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1692] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1693] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1694] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1695] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1696] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1697] The following is further disclosed regarding the above embodiment.

[1698] (Claim 1)

[1699] A means of converting the user's speech into text in real time; and

[1700] means for projecting the converted text onto a display device;

[1701] a means for displaying multiple response options and allowing the user to select any one of the responses;

[1702] means for synthesizing and playing back the selected response;

[1703] A system including:

[1704] (Claim 2)

[1705] 10. The system of claim 1, further comprising pre-processing means for collecting user voice data and performing noise removal and volume normalization.

[1706] (Claim 3)

[1707] 10. The system of claim 1, further comprising a sensor for detecting a swipe of the frame and means for displaying the selected response on a display device.

[1708] "Example 1"

[1709] (Claim 1)

[1710] A means of converting the user's speech into text in real time; and

[1711] means for projecting the converted text onto a display device;

[1712] a means for displaying multiple response options and allowing the user to select any one of the responses;

[1713] means for synthesizing and playing back the selected response;

[1714] A pre-processing means for noise reduction and volume normalization of the audio data;

[1715] projection means for projecting text onto the display device;

[1716] A system including:

[1717] (Claim 2)

[1718] and further comprising a pre-processing means for collecting user voice data and performing noise removal and volume normalization.

[1719] 10. The system of claim 1.

[1720] (Claim 3)

[1721] a sensor for detecting a swipe of the frame, and further comprising means for displaying a selected response on a display device;

[1722] 10. The system of claim 1.

[1723] "Application Example 1"

[1724] (Claim 1)

[1725] A means of converting the user's speech into text in real time; and

[1726] means for projecting the converted text onto a display device;

[1727] a means for displaying multiple response options and allowing the user to select any one of the responses;

[1728] means for synthesizing and playing back the selected response;

[1729] a means for generating a response based on a prompt sentence, the means including a generative AI model for generating an appropriate response based on speech content uttered by the user;

[1730] A system including:

[1731] (Claim 2)

[1732] 10. The system of claim 1, further comprising pre-processing means for collecting user voice data and performing noise removal and volume normalization.

[1733] (Claim 3)

[1734] 10. The system of claim 1, further comprising a sensor for detecting a swipe of the frame and means for displaying the selected response on a display device.

[1735] "Example 2: Combining Emotion Engines"

[1736] (Claim 1)

[1737] A means of converting the user's speech into text in real time; and

[1738] means for projecting the converted text onto a display device;

[1739] a means for displaying multiple response options and allowing the user to select any one of the responses;

[1740] means for synthesizing and playing back the selected response;

[1741] A means of analyzing user emotions in real time,

[1742] a means for providing appropriate response options based on the analyzed emotions;

[1743] A system including:

[1744] (Claim 2)

[1745] 10. The system of claim 1, further comprising pre-processing means for collecting user voice data and performing noise removal and volume normalization.

[1746] (Claim 3)

[1747] 10. The system of claim 1, further comprising a sensor for detecting a swipe of the frame and means for displaying the selected response on a display device.

[1748] "Application example 2 when combining emotion engines"

[1749] (Claim 1)

[1750] A means of converting the user's speech into text in real time; and

[1751] means for projecting the converted text onto a display device;

[1752] a means for displaying multiple response options and allowing the user to select any one of the responses;

[1753] means for synthesizing and playing back the selected response;

[1754] means for analyzing voice data of a user and a conversation partner to identify emotions;

[1755] a means for providing appropriate response options based on the identified emotion;

[1756] A system including:

[1757] (Claim 2)

[1758] 10. The system of claim 1, further comprising pre-processing means for collecting user voice data and performing noise removal and volume normalization.

[1759] (Claim 3)

[1760] 10. The system of claim 1, further comprising a sensor for detecting a swipe of the frame and means for displaying the selected response on a display device. [Explanation of symbols]

[1761] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of converting the user's speech into text in real time; and means for projecting the converted text onto a display device; a means for displaying multiple response options and allowing the user to select any one of the responses; means for synthesizing and playing back the selected response; A system including:

2. The system of claim 1 , further comprising pre-processing means for collecting user voice data and performing noise removal and volume normalization.

3. 10. The system of claim 1, further comprising a sensor for detecting a swipe of the frame and means for displaying the selected response on a display device.

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