system

Abstract

To provide an easy-to-use, environmentally responsive, and cost-effective hearing aid system. [Solution] The system includes means for acquiring sound, means for analyzing the sound as data, means for separating sound sources and reducing noise, means for optimizing the sound based on the user's hearing information, means for converting the optimized sound into text, means for displaying the optimized sound and the converted text, and means for adjusting the volume and acoustic characteristics through the terminal's user interface.

Description

Technical Field

[0005]

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional hearing aids often do not spread widely because of their high purchase price, and also have problems such as the need for manual adjustment for users, difficulty in conversation in a noisy environment, dissatisfaction in terms of design that users feel during use, and an increased risk of dementia faced by people with hearing problems. In view of such a situation, there is a demand for providing an easily usable, environment sound-responsive, and cost-effective hearing system.

Means for Solving the Problems

[0006] "Means for acquiring sound" refers to a device or function for collecting sound information from the environment.

[0007] "Means of analysis" refers to a device or function used to analyze acquired data and extract necessary information.

[0008] "Means for separating sound sources" refers to a device or function that performs the process of separating different sounds from audio data.

[0009] "Means for reducing noise" refers to a device or function for reducing unwanted audio components.

[0010] "Optimization means" refers to a device or function that adjusts data or processes according to a specific purpose in order to achieve maximum effectiveness.

[0011] "Means for converting speech to text" refers to a device or function that performs the process of converting an audio signal into textual information.

[0012] "Means of display" refers to a device or function for visually presenting information.

[0013] "Means for transmitting and receiving over a network" refers to means of communication for transferring data between one or more nodes.

[0014] A "user interface" is a mechanism or tool for a user to interact with a system. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a storage with a reference number is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention provides a system for advanced voice assistance to users, aiming to acquire voice, reduce noise, and then optimize and deliver it. The system has a complex structure including a terminal, a server, and a user interface. The terminal is responsible for acquiring voice and transmitting it to the server. The server analyzes the received voice data, reduces noise by separating the sound source, and optimizes the voice based on the user's hearing profile.

[0037] Next, the server uses natural language processing technology to convert the optimized speech into text. This text data is used as information for the user to visually confirm. The server sends the optimized speech and text to the terminal, which then provides them to the user. The terminal plays the received optimized speech and simultaneously displays the converted text via a smartphone or dedicated device, thereby supporting the user's communication.

[0038] As a concrete example, consider a scenario where a user is in a noisy environment such as a cafe. In this system, the terminal acquires ambient sounds and sends them to a server. The server then processes the ambient noise using noise reduction technology, making only the user's voice clear. Furthermore, the server uses a language model to convert the conversation into text, which is then displayed to the user as visual information via the terminal. In this way, the user can understand the conversation in both audio and text formats. This entire process takes place in real time, and the user can adjust volume and sound quality settings through an intuitive and easy-to-use interface. This enables clear communication even in various environments.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The device uses its built-in microphone to acquire ambient audio data. This data includes the user's conversation and environmental noise. The acquired audio data is immediately placed in a state awaiting transmission to the server.

[0042] Step 2:

[0043] The terminal transmits the acquired voice data to the server via the network. During this process, the data is compressed and encrypted to ensure communication efficiency and security.

[0044] Step 3:

[0045] The server analyzes the received audio data. It applies speech separation technology to distinguish between human voices and noise, and suppresses unwanted sounds through a noise reduction process.

[0046] Step 4:

[0047] The server references the user's hearing profile and optimizes the audio data. This optimization includes processes that emphasize specific frequency bands and signal processing to make human voices easier to recognize.

[0048] Step 5:

[0049] The server converts optimized speech into text using natural language processing techniques. The generated text data is used as visual support for communication.

[0050] Step 6:

[0051] The server sends optimized voice and text data to the terminal. Here too, the necessary processing is performed to efficiently and securely transfer the data over the network.

[0052] Step 7:

[0053] The device plays the optimized audio data it receives, delivering it to the user as clear audio. Simultaneously, the device displays the text data on the user's smartphone or a dedicated device, enabling visual confirmation.

[0054] Step 8:

[0055] Users can change volume and sound quality settings using the smartphone app interface according to their individual needs regarding how they hear audio. The device notifies the server of these changes, which are then reflected in future audio optimizations.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] Current speech recognition technology has problems such as not being able to clarify speech in noisy environments and not being able to adequately optimize speech according to the individual hearing ability of users. In addition, there is a growing need for efficiency in data reception and transmission between devices, and for information provision that combines visual information.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes a device for acquiring acoustic signals, a device for identifying sound sources and suppressing unwanted components, and a device for optimizing acoustic signals based on the user's auditory information. This enables clear speech recognition even in noisy environments, and achieves personalized speech optimization and simultaneous provision of visual information.

[0061] An "acoustic signal" is electrical or vibrational information generated by sound or voice.

[0062] An "apparatus" is a mechanical or electronic means designed to perform a specific function.

[0063] "Information" refers to the content and meaningful data that are transmitted as knowledge or data.

[0064] "Analysis" is the process of examining information and data in detail to understand its structure and meaning.

[0065] A "sound source" is the place or object from which sound originates.

[0066] "Unwanted components" refer to parts of an acoustic signal that are unintended as noise or interference.

[0067] "Suppression" refers to means or actions that reduce or eliminate unwanted influences or sounds.

[0068] "Optimization" refers to adjusting conditions to maximize the performance of a system or process.

[0069] "Auditory information" refers to data related to how each individual user hears, and includes information that is unique to that person's hearing characteristics.

[0070] "Textual information" refers to a set of encoded symbols or characters used to visually represent sound.

[0071] This system is designed to allow users to enjoy clear voice communication. Its main components include a terminal, a server, and a user interface. The operation and coordination of each component enables accurate speech recognition and visual assistance even in noisy environments.

[0072] The terminal functions as a device for acquiring acoustic signals. Specifically, a smartphone or dedicated device is used to capture ambient sound. This acoustic signal is converted into a digital format and transmitted to a server via the network. The communication protocol used is selected to be secure in order to ensure data safety.

[0073] The server functions as a device that analyzes the received acoustic signal. First, techniques are applied to identify the sound source and suppress background noise. This is achieved through spectral subtraction and machine learning algorithms. Furthermore, digital signal processing is used to optimize the acoustic signal based on the user's unique auditory information. This optimization process makes the adjusted acoustic signal easier for the user to hear.

[0074] The optimized acoustic signal is converted into text information by a server using natural language processing technology. This process is made more accurate by using a speech recognition engine. This converted text information and the optimized acoustic signal are then sent back to the terminal.

[0075] The device plays back received audio signals while simultaneously displaying text information on its screen. This allows users to obtain information through both auditory and visual means. Furthermore, it provides functions to adjust volume and acoustic characteristics through the user interface, allowing users to customize their experience.

[0076] For example, if a user is in a noisy cafe, this system effectively reduces ambient noise and clarifies the user's voice. As a result, the user can understand what the other person is saying from both text and audio. An example of a prompt sentence for the generative AI model is, "How can I reduce noise in real time and convert speech to text?" The entire system is designed to operate efficiently in real time.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The device uses a built-in microphone to acquire ambient acoustic signals in real time. The input is raw sound from the environment, and the output is digital acoustic data. This conversion makes the acoustic data usable for transmission over a network. Specifically, analog audio is converted into digital data using an ADC (analog-to-digital converter).

[0080] Step 2:

[0081] The terminal transmits digital audio data to the server via the network. The input is the previously converted digital audio data, and the output is the audio data received by the server. The terminal transmits data while ensuring security using the SSL / TLS protocol.

[0082] Step 3:

[0083] The server analyzes the received acoustic data and applies a noise reduction algorithm. The input is the acoustic data received from the terminal, and the output is the audio data with reduced noise. Specifically, the server analyzes the frequency components using spectral subtraction and suppresses the noise components.

[0084] Step 4:

[0085] The server optimizes the noise-reduced audio data based on the user's hearing profile. The input is the noise-reduced audio data, and the output is the optimized audio data. The server applies DSP (Digital Signal Processing) parameters specific to each user to adjust the audio quality.

[0086] Step 5:

[0087] The server converts optimized audio data into text information using natural language processing technology. The input is optimized audio data, and the output is text data. Specifically, a speech recognition engine analyzes the audio and performs highly accurate text conversion.

[0088] Step 6:

[0089] The server sends optimized audio data and converted text data to the terminal. The input is the audio data and text data, and the output is this data sent to the terminal. The SSL / TLS protocol is used again for transmission to maintain data integrity and security.

[0090] Step 7:

[0091] The terminal plays the received audio data while displaying the converted text data on its screen. The input consists of audio and text data sent from the server, and the output is audiovisual information provided to the user. Specifically, it performs the function of playing audio through the terminal's speaker and displaying text on the screen.

[0092] Step 8:

[0093] Users adjust the volume and acoustic characteristics through the device's user interface. Input is user interaction through an easy-to-use interface, while output is the adjusted volume and sound quality. Users can change settings using the touchscreen or physical buttons.

[0094] (Application Example 1)

[0095] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0096] The problem that this invention aims to solve is to enable users to communicate smoothly through voice even in noisy environments. In particular, it aims to enable users to acquire clear voice and text information in environments where background noise exists, such as in homes or public places.

[0097] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0098] In this invention, the server includes means for acquiring acoustic information, means for separating sound sources and reducing noise, and means for reducing ambient noise and supporting clear interaction with the user. This makes it possible for the user to obtain clear audio and text information even in noisy environments.

[0099] "Acoustic information" refers to data that digitally captures sound waves, such as voices and ambient sounds, that travel through the air.

[0100] "Noise" refers to unwanted sound components that are mixed in with the target acoustic information, and it is a factor that affects speech recognition and sound quality.

[0101] A "user" is a person who uses this system to obtain clear audio and textual information.

[0102] "Hearing information" refers to characteristic information about how each user hears sounds, and is used to optimize speech.

[0103] "Textual information" refers to text data obtained by analyzing speech and performing natural language processing.

[0104] "Information network" refers to communication methods and network infrastructure for exchanging digital information.

[0105] A "connection device" refers to a device used by the user, such as a smartphone, tablet, or dedicated device.

[0106] To implement this invention, a user-operated connection device (e.g., a consumer robot) is required. The connection device is equipped with a microphone to acquire acoustic information. The server analyzes the acquired acoustic information and uses software to reduce noise. Specifically, it collects acoustic data using "pyaudio" and reduces noise using "noise_reduction_module". The server then converts the information into text information using the "speech_recognition" module and further optimizes the sound based on the user's hearing information using "voice_profile_optimizer".

[0107] The server has the functionality to convert optimized acoustic information back into speech using the "text_to_speech" module, while simultaneously displaying text information using "DisplayText". The acoustic and text information is transmitted to the connected device via the information network and provided to the user.

[0108] For example, if a user asks the robot "What's my schedule for today?" in a noisy home environment, the server will remove the noise from the voice and provide the schedule information to the user in both voice and text format.

[0109] An example of a prompt message would be: "Explain how a voice processing system that reduces noise can be applied to home robots. In particular, give specific examples of how it can be used to communicate clearly with users."

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The user acquires acoustic information through the microphone of a consumer robot. Raw acoustic data, including ambient sounds and the user's voice, is input. This input data is transmitted to the server via a connecting device.

[0113] Step 2:

[0114] The server analyzes the received audio data using "pyaudio" and reduces noise. The "noise_reduction_module" separates the sound source and removes unwanted noise. The output is clear audio data with reduced noise.

[0115] Step 3:

[0116] The server uses "voice_profile_optimizer" to optimize the audio data, which has reduced noise, based on the user's hearing information. This optimization process outputs sound quality tailored to each individual user.

[0117] Step 4:

[0118] The server converts the optimized acoustic data into text information using "speech_recognition". In this step, the speech is output as natural language text data, and user instructions and questions are captured as text information.

[0119] Step 5:

[0120] Textual information and optimized audio data are transmitted to the user's connected device via an information network. The output data is used by the robot to play back audio and display the textual information on a display.

[0121] Step 6:

[0122] The user's connected device plays the audio data using "text_to_speech" and displays the text information on the screen using "DisplayText". This allows the user to hear the response clearly in audio and simultaneously confirm it as text.

[0123] Step 7:

[0124] Ultimately, smooth voice communication is facilitated by providing the user with voice and text information generated by the server. System operation can be simulated and tuned using prompt examples generated by the AI ​​model.

[0125] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0126] This invention is a system for providing voice support to users, encompassing everything from acquiring and optimizing voice data to providing visual support. Furthermore, it incorporates an emotion engine that recognizes the user's emotional state and adaptively adjusts voice and text based on that information. This system consists of a terminal, a server, a user interface, and the emotion engine.

[0127] The device acquires audio on-site and sends the data to the server. Upon receiving the audio, the server reduces noise and optimizes the audio based on the user's hearing profile. The optimized audio data is converted to text using natural language processing, and then an emotion engine adjusts the text and audio according to the user's emotional state.

[0128] The emotion engine analyzes features such as voice intonation, speed, and facial expressions to infer the user's emotions. Based on this, the system determines a voice tone and speed that matches the user's current emotions and adjusts text messages as needed. This data is sent to the device and provided through the user interface.

[0129] As a concrete example, consider a situation where a user is nervous during a meeting. The device acquires their voice, and the server optimizes the audio with appropriate volume and noise level. The emotion engine detects the user's tension from the tempo and intensity of their voice and their facial expressions, and the server uses this information to adjust the output audio to a calmer tone. The converted text information is also written in a way that alleviates tension.

[0130] Through a smartphone app, users can receive voice and text messages that are adjusted in real time based on the results of emotion recognition. All of these processes occur in real time, allowing users to have a better auditory and communication experience.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The device uses its built-in microphone to acquire ambient sound. The acquired audio data includes the user's conversation and environmental noise. The device prepares the audio data for transmission to the server, and the data is prepared according to the transmission protocol.

[0134] Step 2:

[0135] The terminal compresses and encrypts the acquired voice data and sends it to the server over the network. This ensures communication efficiency and data security.

[0136] Step 3:

[0137] The server receives the transmitted audio data and first applies a noise reduction algorithm to remove ambient noise. In this process, specific frequency bands are selected to emphasize the human voice.

[0138] Step 4:

[0139] The server references the user's hearing profile and optimizes the noise-reduced audio data. By adjusting the volume and sound quality to the user's preferences, it generates audio that is easy to understand.

[0140] Step 5:

[0141] The server uses an emotion engine to analyze the user's emotional state from the characteristics, speed, and intonation of their voice. The emotion engine has the ability to predict the user's emotions in real time.

[0142] Step 6:

[0143] Based on the analysis results of the emotion engine, the server adjusts the tone and speed of the voice to optimize it. For example, if the user is nervous, the voice output will be adjusted to be calmer. Additionally, the converted text will be given expressions that reflect the emotion.

[0144] Step 7:

[0145] The server sends optimized and emotion-adapted voice and text data to the terminal. This data is provided to the user in the most optimal format.

[0146] Step 8:

[0147] The device plays the received audio data and outputs it in a format that is easy for the user to understand. At the same time, the device displays the converted text data on the user's smartphone or a dedicated display, providing visual support.

[0148] Step 9:

[0149] Through the provided interface, users can adjust volume, sound quality, and even the sensitivity of the emotion engine. This adjustment information is sent from the device to the server and continuously reflected in subsequent processes.

[0150] (Example 2)

[0151] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0152] Modern voice assistance systems often lack sufficient real-time emotion recognition and dynamic adjustment of speech and text information based on that recognition, making it difficult to provide a communication experience that aligns with the user's emotional state. Furthermore, they lack optimization of speech based on each user's auditory profile, failing to adequately address diverse user needs.

[0153] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0154] In this invention, the server includes a device for analyzing speech as data, a device for separating sound sources and reducing noise, a device for optimizing speech based on the user's auditory information, and a device for recognizing emotional states from text information and speech and adaptively adjusting speech and text information. This enables real-time adjustment of speech and text information according to the user's emotional state, thereby meeting diverse user needs.

[0155] A "speech acquisition device" is a device used to collect user speech in real time.

[0156] A "device for analyzing data" is a device that extracts specific information from audio and processes it as digital data.

[0157] A "noise reduction device" is a device that removes unwanted background noise from audio data and extracts clear audio.

[0158] A "device that optimizes speech based on auditory information" is a device that adjusts speech output based on the user's auditory characteristics.

[0159] A "device for converting to text information" is a device used to convert audio data into text data.

[0160] A "device for recognizing emotional states" is a device that analyzes and identifies a user's emotions from voice and text information.

[0161] A "device that adaptively adjusts speech and text information" is a device that dynamically changes speech and text information based on recognized emotions.

[0162] A "display device" is a device used to present the final, adjusted audio and text information to the user.

[0163] "Devices that transmit and receive data via a communication network" are devices used to exchange data over a network.

[0164] A "device for adjusting information characteristics" is a device used to customize the details of information based on the user's individual settings.

[0165] This invention is a system that optimizes voice support for users. It primarily utilizes a voice acquisition device, a server, and a user interface to achieve communication that takes user emotions into consideration.

[0166] The terminal first acquires the user's speech using a microphone or dedicated voice recording device. This voice data is then subjected to simple filtering on-site by initial processing software installed on the terminal, and then transmitted to the server via a secure communication protocol (e.g., SSL / TLS).

[0167] The server processes the received audio data using advanced noise reduction algorithms (e.g., spectral attenuation techniques). This process incorporates the user's auditory profile information to generate optimized audio. Subsequently, a natural language processing engine (e.g., speech recognition API) converts the audio into text. Furthermore, an emotion engine analyzes this data and determines the user's emotional state based on its content. The emotion engine considers the intonation and speed of the speech, as well as the meaning implied from the text information.

[0168] After the user's emotions are identified, the server adaptively adjusts the information. The tone and speed of the voice are adjusted to match the user's current emotional state, and the content of the text information is similarly modified. The adjusted information is then encrypted again and sent from the server to the terminal.

[0169] Users receive adjusted audio and text information through an interface displayed on their smartphones or computers. This interface is designed to visually present information in an easy-to-understand manner for the user.

[0170] A concrete example is a user who is feeling nervous during a meeting. In this case, the device acquires audio in real time, and the server detects the tension and adjusts the tone of voice to a calmer one. The text is also made to be calming for the user.

[0171] An example of a prompt message might be, "Recognize the user's emotions from the following audio data and generate audio and text information adjusted to an appropriate tone." This invention aims to provide user-centric voice support by integrating speech recognition and emotion adjustment.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] The device uses a microphone to capture the user's voice in real time. Raw audio signals are collected as input. This data undergoes initial filtering to reduce background noise. The output is filtered audio data.

[0175] Step 2:

[0176] The terminal encrypts the acquired audio data and sends it to the server via a highly secure communication protocol such as SSL / TLS. The input for this step is filtered audio data, and the output is securely packaged audio data.

[0177] Step 3:

[0178] The server applies advanced noise reduction algorithms to the received audio data. This process utilizes techniques such as source separation and spectral attenuation. The input is encrypted audio data, and the output is clear audio data with the noise removed.

[0179] Step 4:

[0180] The server optimizes the audio based on the user's auditory profile, which includes volume and frequency characteristics. For visualization purposes, the optimization process involves filter design and dynamic range control. The input is denoised audio data, and the output is optimized audio data.

[0181] Step 5:

[0182] The server converts optimized speech into text information using a natural language processing engine. Speech recognition APIs are used in this step. The input is optimized speech data, and the output is text information.

[0183] Step 6:

[0184] The server uses an emotion engine to recognize the user's emotional state from speech and text information. It analyzes features such as intonation, speed, volume, and content to infer emotions. The input is optimized speech data and text information, and the output is the user's emotional information.

[0185] Step 7:

[0186] The server adjusts the audio and text information according to the user's emotional state. The tone, speed, and content of the audio are appropriately modified. The input consists of optimized audio data, text information, and emotional information, while the output is adjusted audio and text.

[0187] Step 8:

[0188] The server sends the adjusted audio and text information back to the terminal. Again, the data is encrypted and transmitted via a secure communication protocol. The input is adjusted audio and text, and the output is securely packaged data.

[0189] Step 9:

[0190] The terminal displays received data on a user interface, and the user receives visual and audible feedback. Applications on the terminal allow the user to view the adjustment results in real time. Input is securely packaged data, and output is adjusted information presented to the user.

[0191] (Application Example 2)

[0192] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0193] In recent years, personal assistant devices that support communication within the home have become widespread. However, existing technologies cannot analyze the emotional state of a voice in real time and adjust the voice and text information appropriately based on that analysis. This presents a problem in that it is difficult to respond flexibly to the psychological state of individual users.

[0194] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0195] In this invention, the server includes means for acquiring sound, means for separating the sound source and reducing noise, and means for performing emotion analysis and adjusting the voice and text information based on the analysis results. This enables real-time adjustment of voice and text information based on the emotional state of the user within the home.

[0196] "Means for acquiring sound" refers to a device that converts sound from a physical environment into digital data and makes it usable in a system.

[0197] "Means of analyzing audio as data" refers to a function that structures acquired audio data and performs a process to evaluate its content and characteristics.

[0198] "Means for separating sound sources and reducing noise" refers to techniques that identify multiple voices or sound sources, extract them individually, and remove unwanted noise.

[0199] "Means of optimizing audio based on user auditory information" refers to a process that adjusts volume and frequency characteristics considering each user's auditory profile to provide an optimal listening experience.

[0200] "Optimized means of converting speech into text information" refers to speech recognition technology for converting speech signals into text format.

[0201] "Means for displaying optimized speech and converted text information" refers to technical means such as display devices that visually present processed speech and text to the user.

[0202] "Means of performing emotion analysis and adjusting audio and text information based on the analysis results" refers to a technology that analyzes the intonation, speed, and other characteristics of audio data to infer the user's emotional state, and dynamically changes the tone of the audio and text content accordingly.

[0203] "Means for transmitting and receiving voice and text data via a communication network" refers to communication devices and protocols for exchanging voice and text data with others over a network.

[0204] "Means that enable volume and quality adjustment through the user interface" refers to control panels or software functions that allow users to manually change the system's audio output settings.

[0205] The system that realizes this application is a voice assistance system that operates by integrating multiple means. First, the terminal is equipped with a high-sensitivity microphone to acquire voice and capture the user's voice in real time. The captured voice data is transmitted to a server using wireless communication technology.

[0206] The server performs noise reduction processing on the received audio data. This processing is done using, for example, the audio processing function of "Google Cloud Natural Language API". Next, the server generates text information from the audio data and converts it to text using natural language processing (NLP) techniques. Other API services such as "IBM Watson®" can also be used in this step.

[0207] The server then uses an emotion analysis engine to analyze the intonation and tempo of the voice and infer the user's emotions. Based on this analysis, it optimizes the voice tone and text content to provide a service tailored to the user's emotional state. In this process, it utilizes emotion analysis tools such as "Microsoft® Azure® Emotion API".

[0208] The processed information is sent back to the terminal, and the voice and text information is provided through the user interface. This allows the user to have a real-time, optimized communication experience. The terminal also accepts user input and provides a UI that allows for volume and sound quality adjustments.

[0209] As a concrete example, this system can read the emotional state of children getting ready in the morning and deliver encouraging messages in a gentle tone. For example, a prompt might be: "Imagine a scenario where a robot measures a child's emotions and encourages them before they go to school in the morning. Specifically, describe what kind of emotional analysis would be performed and what kind of engagement is possible." This allows the system to provide the optimal response based on the imagined scenario.

[0210] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0211] Step 1:

[0212] The device uses a high-sensitivity microphone to capture the user's voice in real time. This voice data is converted into a digital format. The input is ambient sound information, and the output is the digitized voice data.

[0213] Step 2:

[0214] The terminal transmits the acquired audio data to the server. Wireless communication technology is used for this process, and the data is compressed for efficient transmission. The input is digitized audio data, and the output is compressed audio data transmitted through a communication protocol.

[0215] Step 3:

[0216] The server performs noise reduction processing on the received audio data. This utilizes filtering techniques that remove background noise using audio processing software. The input is compressed audio data, and the output is clear audio data with reduced noise.

[0217] Step 4:

[0218] The server converts the noise-reduced audio data into text using natural language processing algorithms. By using services such as the "Google Cloud Speech-to-Text API," it converts speech into text information with high accuracy. The input is noise-reduced audio data, and the output is text information.

[0219] Step 5:

[0220] The server uses an emotion analysis engine to analyze the intonation and speed of the voice data and infer the user's emotions. This analysis is based on characteristics such as the volume and tempo of the voice. The input is clear voice data, and the output is data indicating the user's emotional state.

[0221] Step 6:

[0222] The server optimizes the voice tone and text information according to the user's needs based on the emotion analysis results. It also uses speech synthesis technology to generate a voice tone appropriate to the user's emotions as needed. The input consists of analyzed emotion data and original voice data / text information, while the output consists of the adjusted voice and text information.

[0223] Step 7:

[0224] The server transmits the adjusted audio and text information to the terminal, which is then provided to the user via the user interface. The audio output is played through the speaker, and the text information is displayed on the screen. The input is the adjusted audio and text information, and the output is the audio and text information presented to the user.

[0225] Step 8:

[0226] The user adjusts the volume and sound quality through the interface by operating the device. The device adjusts the audio output in real time in response to the user's input. The input is the user's operation commands, and the output is the adjusted audio output.

[0227] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0228] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0229] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0230] [Second Embodiment]

[0231] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0232] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0233] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0234] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0235] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0236] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0237] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0238] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0239] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0240] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0241] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0242] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0243] This invention provides a system for advanced voice assistance to users, aiming to acquire voice, reduce noise, and then optimize and deliver it. The system has a complex structure including a terminal, a server, and a user interface. The terminal is responsible for acquiring voice and transmitting it to the server. The server analyzes the received voice data, reduces noise by separating the sound source, and optimizes the voice based on the user's hearing profile.

[0244] Next, the server uses natural language processing technology to convert the optimized speech into text. This text data is used as information for the user to visually confirm. The server sends the optimized speech and text to the terminal, which then provides them to the user. The terminal plays the received optimized speech and simultaneously displays the converted text via a smartphone or dedicated device, thereby supporting the user's communication.

[0245] As a concrete example, consider a scenario where a user is in a noisy environment such as a cafe. In this system, the terminal acquires ambient sounds and sends them to a server. The server then processes the ambient noise using noise reduction technology, making only the user's voice clear. Furthermore, the server uses a language model to convert the conversation into text, which is then displayed to the user as visual information via the terminal. In this way, the user can understand the conversation in both audio and text formats. This entire process takes place in real time, and the user can adjust volume and sound quality settings through an intuitive and easy-to-use interface. This enables clear communication even in various environments.

[0246] The following describes the processing flow.

[0247] Step 1:

[0248] The device uses its built-in microphone to acquire ambient audio data. This data includes the user's conversation and environmental noise. The acquired audio data is immediately placed in a state awaiting transmission to the server.

[0249] Step 2:

[0250] The terminal transmits the acquired voice data to the server via the network. During this process, the data is compressed and encrypted to ensure communication efficiency and security.

[0251] Step 3:

[0252] The server analyzes the received audio data. It applies speech separation technology to distinguish between human voices and noise, and suppresses unwanted sounds through a noise reduction process.

[0253] Step 4:

[0254] The server references the user's hearing profile and optimizes the audio data. This optimization includes processes that emphasize specific frequency bands and signal processing to make human voices easier to recognize.

[0255] Step 5:

[0256] The server converts optimized speech into text using natural language processing techniques. The generated text data is used as visual support for communication.

[0257] Step 6:

[0258] The server sends optimized voice and text data to the terminal. Here too, the necessary processing is performed to efficiently and securely transfer the data over the network.

[0259] Step 7:

[0260] The device plays the optimized audio data it receives, delivering it to the user as clear audio. Simultaneously, the device displays the text data on the user's smartphone or a dedicated device, enabling visual confirmation.

[0261] Step 8:

[0262] Users can change volume and sound quality settings using the smartphone app interface according to their individual needs regarding how they hear audio. The device notifies the server of these changes, which are then reflected in future audio optimizations.

[0263] (Example 1)

[0264] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0265] Current speech recognition technology has problems such as not being able to clarify speech in noisy environments and not being able to adequately optimize speech according to the individual hearing ability of users. In addition, there is a growing need for efficiency in data reception and transmission between devices, and for information provision that combines visual information.

[0266] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0267] In this invention, the server includes a device for acquiring acoustic signals, a device for identifying sound sources and suppressing unwanted components, and a device for optimizing acoustic signals based on the user's auditory information. This enables clear speech recognition even in noisy environments, and achieves personalized speech optimization and simultaneous provision of visual information.

[0268] An "acoustic signal" is electrical or vibrational information generated by sound or voice.

[0269] An "apparatus" is a mechanical or electronic means designed to perform a specific function.

[0270] "Information" refers to the content and meaningful data that are transmitted as knowledge or data.

[0271] "Analysis" is the process of examining information and data in detail to understand its structure and meaning.

[0272] A "sound source" is the place or object from which sound originates.

[0273] "Unwanted components" refer to parts of an acoustic signal that are unintended as noise or interference.

[0274] "Suppression" refers to means or actions that reduce or eliminate unwanted influences or sounds.

[0275] "Optimization" refers to adjusting conditions to maximize the performance of a system or process.

[0276] "Auditory information" refers to data related to how each individual user hears, and includes information that is unique to that person's hearing characteristics.

[0277] "Textual information" refers to a set of encoded symbols or characters used to visually represent sound.

[0278] This system is designed to allow users to enjoy clear voice communication. Its main components include a terminal, a server, and a user interface. The operation and coordination of each component enables accurate speech recognition and visual assistance even in noisy environments.

[0279] The terminal functions as a device for acquiring acoustic signals. Specifically, a smartphone or dedicated device is used to capture ambient sound. This acoustic signal is converted into a digital format and transmitted to a server via the network. The communication protocol used is selected to be secure in order to ensure data safety.

[0280] The server functions as a device that analyzes the received acoustic signal. First, techniques are applied to identify the sound source and suppress background noise. This is achieved through spectral subtraction and machine learning algorithms. Furthermore, digital signal processing is used to optimize the acoustic signal based on the user's unique auditory information. This optimization process makes the adjusted acoustic signal easier for the user to hear.

[0281] The optimized acoustic signal is converted into character information by the server using natural language processing technology. By using a speech recognition engine, this process can be made more accurate. The converted character information and the optimized acoustic signal are sent back to the terminal.

[0282] The terminal plays the received acoustic signal while displaying the character information on the display. This enables the user to obtain information through both hearing and vision. Also, a function is provided to adjust the volume and acoustic characteristics through the user interface, making it possible to customize the user experience.

[0283] For example, when the user is in a noisy café, this system effectively reduces the surrounding noise and clarifies the user's voice. As a result, the user can understand the other person's words from both the text and the voice. An example of a prompt sentence for the generative AI model is "Please teach me how to reduce noise in real time and convert voice into text." The entire system is designed to operate efficiently in real time.

[0284] The flow of the specific process in Example 1 will be described using FIG. 11.

[0285] Step 1:

[0286] The terminal uses the built-in microphone to acquire the surrounding acoustic signal in real time. The input is raw voice from the environment, and the output is acoustic data in digital format. This conversion enables the acoustic data to be in a form that can be transmitted through the network. Specifically, analog voice is converted into digital data by an ADC (analog-to-digital conversion).

[0287] Step 2:

[0288] The terminal transmits digital audio data to the server via the network. The input is the previously converted digital audio data, and the output is the audio data received by the server. The terminal transmits data while ensuring security using the SSL / TLS protocol.

[0289] Step 3:

[0290] The server analyzes the received acoustic data and applies a noise reduction algorithm. The input is the acoustic data received from the terminal, and the output is the audio data with reduced noise. Specifically, the server analyzes the frequency components using spectral subtraction and suppresses the noise components.

[0291] Step 4:

[0292] The server optimizes the noise-reduced audio data based on the user's hearing profile. The input is the noise-reduced audio data, and the output is the optimized audio data. The server applies DSP (Digital Signal Processing) parameters specific to each user to adjust the audio quality.

[0293] Step 5:

[0294] The server converts optimized audio data into text information using natural language processing technology. The input is optimized audio data, and the output is text data. Specifically, a speech recognition engine analyzes the audio and performs highly accurate text conversion.

[0295] Step 6:

[0296] The server sends optimized audio data and converted text data to the terminal. The input is the audio data and text data, and the output is this data sent to the terminal. The SSL / TLS protocol is used again for transmission to maintain data integrity and security.

[0297] Step 7:

[0298] The terminal plays the received audio data while displaying the converted text data on its screen. The input consists of audio and text data sent from the server, and the output is audiovisual information provided to the user. Specifically, it performs the function of playing audio through the terminal's speaker and displaying text on the screen.

[0299] Step 8:

[0300] Users adjust the volume and acoustic characteristics through the device's user interface. Input is user interaction through an easy-to-use interface, while output is the adjusted volume and sound quality. Users can change settings using the touchscreen or physical buttons.

[0301] (Application Example 1)

[0302] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0303] The problem that this invention aims to solve is to enable users to communicate smoothly through voice even in noisy environments. In particular, it aims to enable users to acquire clear voice and text information in environments where background noise exists, such as in homes or public places.

[0304] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0305] In this invention, the server includes means for acquiring acoustic information, means for separating sound sources and reducing noise, and means for reducing ambient noise and supporting clear interaction with the user. This makes it possible for the user to obtain clear audio and text information even in noisy environments.

[0306] "Acoustic information" refers to data obtained by acquiring the waveforms of sounds transmitted through the air, such as voices and environmental sounds, in digital form.

[0307] "Noise" is an unnecessary sound component that is mixed into the target acoustic information and is a factor that affects speech recognition and sound quality.

[0308] "User" refers to a person who uses this system to obtain clear acoustic and character information.

[0309] "Hearing information" refers to characteristic information regarding how each individual user hears sounds and is used for optimizing voices.

[0310] "Character information" refers to text data obtained by analyzing voices and performing natural language processing.

[0311] "Information network" refers to communication means and network infrastructure for exchanging digital information.

[0312] "Connection device" refers to devices such as smartphones, tablets, or dedicated devices used by users.

[0313] To implement this invention, a connection device (e.g., a consumer robot) used by the user is required. A microphone is installed in the connection device to acquire acoustic information. The server analyzes the acquired acoustic information and uses software to reduce noise. Specifically, "pyaudio" is used to collect acoustic data, and "noise_reduction_module" is used to reduce noise. Then, the information is converted into character information using the "speech_recognition" module on the server, and the sound is optimized based on the hearing information of the user using "voice_profile_optimizer".

[0314] The server has the functionality to convert optimized acoustic information back into speech using the "text_to_speech" module, while simultaneously displaying text information using "DisplayText". The acoustic and text information is transmitted to the connected device via the information network and provided to the user.

[0315] For example, if a user asks the robot "What's my schedule for today?" in a noisy home environment, the server will remove the noise from the voice and provide the schedule information to the user in both voice and text format.

[0316] An example of a prompt message would be: "Explain how a voice processing system that reduces noise can be applied to home robots. In particular, give specific examples of how it can be used to communicate clearly with users."

[0317] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0318] Step 1:

[0319] The user acquires acoustic information through the microphone of a consumer robot. Raw acoustic data, including ambient sounds and the user's voice, is input. This input data is transmitted to the server via a connecting device.

[0320] Step 2:

[0321] The server analyzes the received audio data using "pyaudio" and reduces noise. The "noise_reduction_module" separates the sound source and removes unwanted noise. The output is clear audio data with reduced noise.

[0322] Step 3:

[0323] The server uses "voice_profile_optimizer" to optimize the audio data, which has reduced noise, based on the user's hearing information. This optimization process outputs sound quality tailored to each individual user.

[0324] Step 4:

[0325] The server converts the optimized acoustic data into text information using "speech_recognition". In this step, the speech is output as natural language text data, and user instructions and questions are captured as text information.

[0326] Step 5:

[0327] Textual information and optimized audio data are transmitted to the user's connected device via an information network. The output data is used by the robot to play back audio and display the textual information on a display.

[0328] Step 6:

[0329] The user's connected device plays the audio data using "text_to_speech" and displays the text information on the screen using "DisplayText". This allows the user to hear the response clearly in audio and simultaneously confirm it as text.

[0330] Step 7:

[0331] Ultimately, smooth voice communication is facilitated by providing the user with voice and text information generated by the server. System operation can be simulated and tuned using prompt examples generated by the AI ​​model.

[0332] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0333] This invention is a system for providing voice support to users, encompassing everything from acquiring and optimizing voice data to providing visual support. Furthermore, it incorporates an emotion engine that recognizes the user's emotional state and adaptively adjusts voice and text based on that information. This system consists of a terminal, a server, a user interface, and the emotion engine.

[0334] The device acquires audio on-site and sends the data to the server. Upon receiving the audio, the server reduces noise and optimizes the audio based on the user's hearing profile. The optimized audio data is converted to text using natural language processing, and then an emotion engine adjusts the text and audio according to the user's emotional state.

[0335] The emotion engine analyzes features such as voice intonation, speed, and facial expressions to infer the user's emotions. Based on this, the system determines a voice tone and speed that matches the user's current emotions and adjusts text messages as needed. This data is sent to the device and provided through the user interface.

[0336] As a concrete example, consider a situation where a user is nervous during a meeting. The device acquires their voice, and the server optimizes the audio with appropriate volume and noise level. The emotion engine detects the user's tension from the tempo and intensity of their voice and their facial expressions, and the server uses this information to adjust the output audio to a calmer tone. The converted text information is also written in a way that alleviates tension.

[0337] Through a smartphone app, users can receive voice and text messages that are adjusted in real time based on the results of emotion recognition. All of these processes occur in real time, allowing users to have a better auditory and communication experience.

[0338] The following describes the processing flow.

[0339] Step 1:

[0340] The device uses its built-in microphone to acquire ambient sound. The acquired audio data includes the user's conversation and environmental noise. The device prepares the audio data for transmission to the server, and the data is prepared according to the transmission protocol.

[0341] Step 2:

[0342] The terminal compresses and encrypts the acquired voice data and sends it to the server over the network. This ensures communication efficiency and data security.

[0343] Step 3:

[0344] The server receives the transmitted audio data and first applies a noise reduction algorithm to remove ambient noise. In this process, specific frequency bands are selected to emphasize the human voice.

[0345] Step 4:

[0346] The server references the user's hearing profile and optimizes the noise-reduced audio data. By adjusting the volume and sound quality to the user's preferences, it generates audio that is easy to understand.

[0347] Step 5:

[0348] The server uses an emotion engine to analyze the user's emotional state from the characteristics, speed, and intonation of their voice. The emotion engine has the ability to predict the user's emotions in real time.

[0349] Step 6:

[0350] Based on the analysis results of the emotion engine, the server adjusts the tone and speed of the voice to optimize it. For example, if the user is nervous, the voice output will be adjusted to be calmer. Additionally, the converted text will be given expressions that reflect the emotion.

[0351] Step 7:

[0352] The server sends optimized and emotion-adapted voice and text data to the terminal. This data is provided to the user in the most optimal format.

[0353] Step 8:

[0354] The device plays the received audio data and outputs it in a format that is easy for the user to understand. At the same time, the device displays the converted text data on the user's smartphone or a dedicated display, providing visual support.

[0355] Step 9:

[0356] Through the provided interface, users can adjust volume, sound quality, and even the sensitivity of the emotion engine. This adjustment information is sent from the device to the server and continuously reflected in subsequent processes.

[0357] (Example 2)

[0358] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0359] Modern voice assistance systems often lack sufficient real-time emotion recognition and dynamic adjustment of speech and text information based on that recognition, making it difficult to provide a communication experience that aligns with the user's emotional state. Furthermore, they lack optimization of speech based on each user's auditory profile, failing to adequately address diverse user needs.

[0360] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0361] In this invention, the server includes a device for analyzing speech as data, a device for separating sound sources and reducing noise, a device for optimizing speech based on the user's auditory information, and a device for recognizing emotional states from text information and speech and adaptively adjusting speech and text information. This enables real-time adjustment of speech and text information according to the user's emotional state, thereby meeting diverse user needs.

[0362] A "speech acquisition device" is a device used to collect user speech in real time.

[0363] A "device for analyzing data" is a device that extracts specific information from audio and processes it as digital data.

[0364] A "noise reduction device" is a device that removes unwanted background noise from audio data and extracts clear audio.

[0365] A "device that optimizes speech based on auditory information" is a device that adjusts speech output based on the user's auditory characteristics.

[0366] A "device for converting to text information" is a device used to convert audio data into text data.

[0367] A "device for recognizing emotional states" is a device that analyzes and identifies a user's emotions from voice and text information.

[0368] A "device that adaptively adjusts speech and text information" is a device that dynamically changes speech and text information based on recognized emotions.

[0369] A "display device" is a device used to present the final, adjusted audio and text information to the user.

[0370] "Devices that transmit and receive data via a communication network" are devices used to exchange data over a network.

[0371] A "device for adjusting information characteristics" is a device used to customize the details of information based on the user's individual settings.

[0372] This invention is a system that optimizes voice support for users. It primarily utilizes a voice acquisition device, a server, and a user interface to achieve communication that takes user emotions into consideration.

[0373] The terminal first acquires the user's speech using a microphone or dedicated voice recording device. This voice data is then subjected to simple filtering on-site by initial processing software installed on the terminal, and then transmitted to the server via a secure communication protocol (e.g., SSL / TLS).

[0374] The server processes the received audio data using advanced noise reduction algorithms (e.g., spectral attenuation techniques). This process incorporates the user's auditory profile information to generate optimized audio. Subsequently, a natural language processing engine (e.g., speech recognition API) converts the audio into text. Furthermore, an emotion engine analyzes this data and determines the user's emotional state based on its content. The emotion engine considers the intonation and speed of the speech, as well as the meaning implied from the text information.

[0375] After the user's emotions are identified, the server adaptively adjusts the information. The tone and speed of the voice are adjusted to match the user's current emotional state, and the content of the text information is similarly modified. The adjusted information is then encrypted again and sent from the server to the terminal.

[0376] Users receive adjusted audio and text information through an interface displayed on their smartphones or computers. This interface is designed to visually present information in an easy-to-understand manner for the user.

[0377] A concrete example is a user who is feeling nervous during a meeting. In this case, the device acquires audio in real time, and the server detects the tension and adjusts the tone of voice to a calmer one. The text is also made to be calming for the user.

[0378] An example of a prompt message might be, "Recognize the user's emotions from the following audio data and generate audio and text information adjusted to an appropriate tone." This invention aims to provide user-centric voice support by integrating speech recognition and emotion adjustment.

[0379] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0380] Step 1:

[0381] The device uses a microphone to capture the user's voice in real time. Raw audio signals are collected as input. This data undergoes initial filtering to reduce background noise. The output is filtered audio data.

[0382] Step 2:

[0383] The terminal encrypts the acquired audio data and sends it to the server via a highly secure communication protocol such as SSL / TLS. The input for this step is filtered audio data, and the output is securely packaged audio data.

[0384] Step 3:

[0385] The server applies advanced noise reduction algorithms to the received audio data. This process utilizes techniques such as source separation and spectral attenuation. The input is encrypted audio data, and the output is clear audio data with the noise removed.

[0386] Step 4:

[0387] The server optimizes the audio based on the user's auditory profile, which includes volume and frequency characteristics. For visualization purposes, the optimization process involves filter design and dynamic range control. The input is denoised audio data, and the output is optimized audio data.

[0388] Step 5:

[0389] The server converts optimized speech into text information using a natural language processing engine. Speech recognition APIs are used in this step. The input is optimized speech data, and the output is text information.

[0390] Step 6:

[0391] The server uses an emotion engine to recognize the user's emotional state from speech and text information. It analyzes features such as intonation, speed, volume, and content to infer emotions. The input is optimized speech data and text information, and the output is the user's emotional information.

[0392] Step 7:

[0393] The server adjusts the audio and text information according to the user's emotional state. The tone, speed, and content of the audio are appropriately modified. The input consists of optimized audio data, text information, and emotional information, while the output is adjusted audio and text.

[0394] Step 8:

[0395] The server sends the adjusted audio and text information back to the terminal. Again, the data is encrypted and transmitted via a secure communication protocol. The input is adjusted audio and text, and the output is securely packaged data.

[0396] Step 9:

[0397] The terminal displays received data on a user interface, and the user receives visual and audible feedback. Applications on the terminal allow the user to view the adjustment results in real time. Input is securely packaged data, and output is adjusted information presented to the user.

[0398] (Application Example 2)

[0399] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0400] In recent years, personal assistant devices that support communication within the home have become widespread. However, existing technologies cannot analyze the emotional state of a voice in real time and adjust the voice and text information appropriately based on that analysis. This presents a problem in that it is difficult to respond flexibly to the psychological state of individual users.

[0401] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0402] In this invention, the server includes means for acquiring sound, means for separating the sound source and reducing noise, and means for performing emotion analysis and adjusting the voice and text information based on the analysis results. This enables real-time adjustment of voice and text information based on the emotional state of the user within the home.

[0403] "Means for acquiring sound" refers to a device that converts sound from a physical environment into digital data and makes it usable in a system.

[0404] "Means of analyzing audio as data" refers to a function that structures acquired audio data and performs a process to evaluate its content and characteristics.

[0405] "Means for separating sound sources and reducing noise" refers to techniques that identify multiple voices or sound sources, extract them individually, and remove unwanted noise.

[0406] "Means of optimizing audio based on user auditory information" refers to a process that adjusts volume and frequency characteristics considering each user's auditory profile to provide an optimal listening experience.

[0407] "Optimized means of converting speech into text information" refers to speech recognition technology for converting speech signals into text format.

[0408] "Means for displaying optimized speech and converted text information" refers to technical means such as display devices that visually present processed speech and text to the user.

[0409] "Means of performing emotion analysis and adjusting audio and text information based on the analysis results" refers to a technology that analyzes the intonation, speed, and other characteristics of audio data to infer the user's emotional state, and dynamically changes the tone of the audio and text content accordingly.

[0410] "Means for transmitting and receiving voice and text data via a communication network" refers to communication devices and protocols for exchanging voice and text data with others over a network.

[0411] "Means that enable volume and quality adjustment through the user interface" refers to control panels or software functions that allow users to manually change the system's audio output settings.

[0412] The system that realizes this application is a voice assistance system that operates by integrating multiple means. First, the terminal is equipped with a high-sensitivity microphone to acquire voice and capture the user's voice in real time. The captured voice data is transmitted to a server using wireless communication technology.

[0413] The server performs noise reduction processing on the received audio data. This processing is done using, for example, the audio processing function of the "Google Cloud Natural Language API". Next, the server generates text information from the audio data and converts it into text using natural language processing (NLP) techniques. Other API services such as "IBM Watson" can also be used in this step.

[0414] The server then uses an emotion analysis engine to analyze the intonation and tempo of the voice and infer the user's emotions. Based on this analysis, it optimizes the voice tone and text content to provide a service tailored to the user's emotional state. In this process, it utilizes emotion analysis tools such as "Microsoft Azure Emotion API".

[0415] The processed information is sent back to the terminal, and the voice and text information is provided through the user interface. This allows the user to have a real-time, optimized communication experience. The terminal also accepts user input and provides a UI that allows for volume and sound quality adjustments.

[0416] As a concrete example, this system can read the emotional state of children getting ready in the morning and deliver encouraging messages in a gentle tone. For example, a prompt might be: "Imagine a scenario where a robot measures a child's emotions and encourages them before they go to school in the morning. Specifically, describe what kind of emotional analysis would be performed and what kind of engagement is possible." This allows the system to provide the optimal response based on the imagined scenario.

[0417] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0418] Step 1:

[0419] The device uses a high-sensitivity microphone to capture the user's voice in real time. This voice data is converted into a digital format. The input is ambient sound information, and the output is the digitized voice data.

[0420] Step 2:

[0421] The terminal transmits the acquired audio data to the server. Wireless communication technology is used for this process, and the data is compressed for efficient transmission. The input is digitized audio data, and the output is compressed audio data transmitted through a communication protocol.

[0422] Step 3:

[0423] The server performs noise reduction processing on the received audio data. This utilizes filtering techniques that remove background noise using audio processing software. The input is compressed audio data, and the output is clear audio data with reduced noise.

[0424] Step 4:

[0425] The server converts the noise-reduced audio data into text using natural language processing algorithms. By using services such as the "Google Cloud Speech-to-Text API," it converts speech into text information with high accuracy. The input is noise-reduced audio data, and the output is text information.

[0426] Step 5:

[0427] The server uses an emotion analysis engine to analyze the intonation and speed of the voice data and infer the user's emotions. This analysis is based on characteristics such as the volume and tempo of the voice. The input is clear voice data, and the output is data indicating the user's emotional state.

[0428] Step 6:

[0429] The server optimizes the voice tone and text information according to the user's needs based on the emotion analysis results. It also uses speech synthesis technology to generate a voice tone appropriate to the user's emotions as needed. The input consists of analyzed emotion data and original voice data / text information, while the output consists of the adjusted voice and text information.

[0430] Step 7:

[0431] The server transmits the adjusted audio and text information to the terminal, which is then provided to the user via the user interface. The audio output is played through the speaker, and the text information is displayed on the screen. The input is the adjusted audio and text information, and the output is the audio and text information presented to the user.

[0432] Step 8:

[0433] The user adjusts the volume and sound quality through the interface by operating the device. The device adjusts the audio output in real time in response to the user's input. The input is the user's operation commands, and the output is the adjusted audio output.

[0434] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0435] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0436] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0437] [Third Embodiment]

[0438] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0439] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0440] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0441] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0442] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0443] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0444] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0445] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0446] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0447] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0448] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0449] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0450] This invention provides a system for advanced voice assistance to users, aiming to acquire voice, reduce noise, and then optimize and deliver it. The system has a complex structure including a terminal, a server, and a user interface. The terminal is responsible for acquiring voice and transmitting it to the server. The server analyzes the received voice data, reduces noise by separating the sound source, and optimizes the voice based on the user's hearing profile.

[0451] Next, the server uses natural language processing technology to convert the optimized speech into text. This text data is used as information for the user to visually confirm. The server sends the optimized speech and text to the terminal, which then provides them to the user. The terminal plays the received optimized speech and simultaneously displays the converted text via a smartphone or dedicated device, thereby supporting the user's communication.

[0452] As a concrete example, consider a scenario where a user is in a noisy environment such as a cafe. In this system, the terminal acquires ambient sounds and sends them to a server. The server then processes the ambient noise using noise reduction technology, making only the user's voice clear. Furthermore, the server uses a language model to convert the conversation into text, which is then displayed to the user as visual information via the terminal. In this way, the user can understand the conversation in both audio and text formats. This entire process takes place in real time, and the user can adjust volume and sound quality settings through an intuitive and easy-to-use interface. This enables clear communication even in various environments.

[0453] The following describes the processing flow.

[0454] Step 1:

[0455] The device uses its built-in microphone to acquire ambient audio data. This data includes the user's conversation and environmental noise. The acquired audio data is immediately placed in a state awaiting transmission to the server.

[0456] Step 2:

[0457] The terminal transmits the acquired voice data to the server via the network. During this process, the data is compressed and encrypted to ensure communication efficiency and security.

[0458] Step 3:

[0459] The server analyzes the received audio data. It applies speech separation technology to distinguish between human voices and noise, and suppresses unwanted sounds through a noise reduction process.

[0460] Step 4:

[0461] The server references the user's hearing profile and optimizes the audio data. This optimization includes processes that emphasize specific frequency bands and signal processing to make human voices easier to recognize.

[0462] Step 5:

[0463] The server converts optimized speech into text using natural language processing techniques. The generated text data is used as visual support for communication.

[0464] Step 6:

[0465] The server sends optimized voice and text data to the terminal. Here too, the necessary processing is performed to efficiently and securely transfer the data over the network.

[0466] Step 7:

[0467] The device plays the optimized audio data it receives, delivering it to the user as clear audio. Simultaneously, the device displays the text data on the user's smartphone or a dedicated device, enabling visual confirmation.

[0468] Step 8:

[0469] Users can change volume and sound quality settings using the smartphone app interface according to their individual needs regarding how they hear audio. The device notifies the server of these changes, which are then reflected in future audio optimizations.

[0470] (Example 1)

[0471] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0472] Current speech recognition technology has problems such as not being able to clarify speech in noisy environments and not being able to adequately optimize speech according to the individual hearing ability of users. In addition, there is a growing need for efficiency in data reception and transmission between devices, and for information provision that combines visual information.

[0473] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0474] In this invention, the server includes a device for acquiring acoustic signals, a device for identifying sound sources and suppressing unwanted components, and a device for optimizing acoustic signals based on the user's auditory information. This enables clear speech recognition even in noisy environments, and achieves personalized speech optimization and simultaneous provision of visual information.

[0475] An "acoustic signal" is electrical or vibrational information generated by sound or voice.

[0476] An "apparatus" is a mechanical or electronic means designed to perform a specific function.

[0477] "Information" refers to the content and meaningful data that are transmitted as knowledge or data.

[0478] "Analysis" is the process of examining information and data in detail to understand its structure and meaning.

[0479] A "sound source" is the place or object from which sound originates.

[0480] "Unwanted components" refer to parts of an acoustic signal that are unintended as noise or interference.

[0481] "Suppression" refers to means or actions that reduce or eliminate unwanted influences or sounds.

[0482] "Optimization" refers to adjusting conditions to maximize the performance of a system or process.

[0483] "Auditory information" refers to data related to how each individual user hears, and includes information that is unique to that person's hearing characteristics.

[0484] "Textual information" refers to a set of encoded symbols or characters used to visually represent sound.

[0485] This system is designed to allow users to enjoy clear voice communication. Its main components include a terminal, a server, and a user interface. The operation and coordination of each component enables accurate speech recognition and visual assistance even in noisy environments.

[0486] The terminal functions as a device for acquiring acoustic signals. Specifically, a smartphone or dedicated device is used to capture ambient sound. This acoustic signal is converted into a digital format and transmitted to a server via the network. The communication protocol used is selected to be secure in order to ensure data safety.

[0487] The server functions as a device that analyzes the received acoustic signal. First, techniques are applied to identify the sound source and suppress background noise. This is achieved through spectral subtraction and machine learning algorithms. Furthermore, digital signal processing is used to optimize the acoustic signal based on the user's unique auditory information. This optimization process makes the adjusted acoustic signal easier for the user to hear.

[0488] The optimized acoustic signal is converted into text information by a server using natural language processing technology. This process is made more accurate by using a speech recognition engine. This converted text information and the optimized acoustic signal are then sent back to the terminal.

[0489] The device plays back received audio signals while simultaneously displaying text information on its screen. This allows users to obtain information through both auditory and visual means. Furthermore, it provides functions to adjust volume and acoustic characteristics through the user interface, allowing users to customize their experience.

[0490] For example, if a user is in a noisy cafe, this system effectively reduces ambient noise and clarifies the user's voice. As a result, the user can understand what the other person is saying from both text and audio. An example of a prompt sentence for the generative AI model is, "How can I reduce noise in real time and convert speech to text?" The entire system is designed to operate efficiently in real time.

[0491] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0492] Step 1:

[0493] The device uses a built-in microphone to acquire ambient acoustic signals in real time. The input is raw sound from the environment, and the output is digital acoustic data. This conversion makes the acoustic data usable for transmission over a network. Specifically, analog audio is converted into digital data using an ADC (analog-to-digital converter).

[0494] Step 2:

[0495] The terminal transmits digital audio data to the server via the network. The input is the previously converted digital audio data, and the output is the audio data received by the server. The terminal transmits data while ensuring security using the SSL / TLS protocol.

[0496] Step 3:

[0497] The server analyzes the received acoustic data and applies a noise reduction algorithm. The input is the acoustic data received from the terminal, and the output is the audio data with reduced noise. Specifically, the server analyzes the frequency components using spectral subtraction and suppresses the noise components.

[0498] Step 4:

[0499] The server optimizes the noise-reduced audio data based on the user's hearing profile. The input is the noise-reduced audio data, and the output is the optimized audio data. The server applies DSP (Digital Signal Processing) parameters specific to each user to adjust the audio quality.

[0500] Step 5:

[0501] The server converts optimized audio data into text information using natural language processing technology. The input is optimized audio data, and the output is text data. Specifically, a speech recognition engine analyzes the audio and performs highly accurate text conversion.

[0502] Step 6:

[0503] The server sends optimized audio data and converted text data to the terminal. The input is the audio data and text data, and the output is this data sent to the terminal. The SSL / TLS protocol is used again for transmission to maintain data integrity and security.

[0504] Step 7:

[0505] The terminal plays the received audio data while displaying the converted text data on its screen. The input consists of audio and text data sent from the server, and the output is audiovisual information provided to the user. Specifically, it performs the function of playing audio through the terminal's speaker and displaying text on the screen.

[0506] Step 8:

[0507] Users adjust the volume and acoustic characteristics through the device's user interface. Input is user interaction through an easy-to-use interface, while output is the adjusted volume and sound quality. Users can change settings using the touchscreen or physical buttons.

[0508] (Application Example 1)

[0509] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0510] The problem that this invention aims to solve is to enable users to communicate smoothly through voice even in noisy environments. In particular, it aims to enable users to acquire clear voice and text information in environments where background noise exists, such as in homes or public places.

[0511] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0512] In this invention, the server includes means for acquiring acoustic information, means for separating sound sources and reducing noise, and means for reducing ambient noise and supporting clear interaction with the user. This makes it possible for the user to obtain clear audio and text information even in noisy environments.

[0513] "Acoustic information" refers to data that digitally captures sound waves, such as voices and ambient sounds, that travel through the air.

[0514] "Noise" refers to unwanted sound components that are mixed in with the target acoustic information, and it is a factor that affects speech recognition and sound quality.

[0515] A "user" is a person who uses this system to obtain clear audio and textual information.

[0516] "Hearing information" refers to characteristic information about how each user hears sounds, and is used to optimize speech.

[0517] "Textual information" refers to text data obtained by analyzing speech and performing natural language processing.

[0518] "Information network" refers to communication methods and network infrastructure for exchanging digital information.

[0519] A "connection device" refers to a device used by the user, such as a smartphone, tablet, or dedicated device.

[0520] To implement this invention, a user-operated connection device (e.g., a consumer robot) is required. The connection device is equipped with a microphone to acquire acoustic information. The server analyzes the acquired acoustic information and uses software to reduce noise. Specifically, it collects acoustic data using "pyaudio" and reduces noise using "noise_reduction_module". The server then converts the information into text information using the "speech_recognition" module and further optimizes the sound based on the user's hearing information using "voice_profile_optimizer".

[0521] The server has the functionality to convert optimized acoustic information back into speech using the "text_to_speech" module, while simultaneously displaying text information using "DisplayText". The acoustic and text information is transmitted to the connected device via the information network and provided to the user.

[0522] For example, if a user asks the robot "What's my schedule for today?" in a noisy home environment, the server will remove the noise from the voice and provide the schedule information to the user in both voice and text format.

[0523] An example of a prompt message would be: "Explain how a voice processing system that reduces noise can be applied to home robots. In particular, give specific examples of how it can be used to communicate clearly with users."

[0524] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0525] Step 1:

[0526] The user acquires acoustic information through the microphone of a consumer robot. Raw acoustic data, including ambient sounds and the user's voice, is input. This input data is transmitted to the server via a connecting device.

[0527] Step 2:

[0528] The server analyzes the received audio data using "pyaudio" and reduces noise. The "noise_reduction_module" separates the sound source and removes unwanted noise. The output is clear audio data with reduced noise.

[0529] Step 3:

[0530] The server uses "voice_profile_optimizer" to optimize the audio data, which has reduced noise, based on the user's hearing information. This optimization process outputs sound quality tailored to each individual user.

[0531] Step 4:

[0532] The server converts the optimized acoustic data into text information using "speech_recognition". In this step, the speech is output as natural language text data, and user instructions and questions are captured as text information.

[0533] Step 5:

[0534] Textual information and optimized audio data are transmitted to the user's connected device via an information network. The output data is used by the robot to play back audio and display the textual information on a display.

[0535] Step 6:

[0536] The user's connected device plays the audio data using "text_to_speech" and displays the text information on the screen using "DisplayText". This allows the user to hear the response clearly in audio and simultaneously confirm it as text.

[0537] Step 7:

[0538] Ultimately, smooth voice communication is facilitated by providing the user with voice and text information generated by the server. System operation can be simulated and tuned using prompt examples generated by the AI ​​model.

[0539] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0540] This invention is a system for providing voice support to users, encompassing everything from acquiring and optimizing voice data to providing visual support. Furthermore, it incorporates an emotion engine that recognizes the user's emotional state and adaptively adjusts voice and text based on that information. This system consists of a terminal, a server, a user interface, and the emotion engine.

[0541] The device acquires audio on-site and sends the data to the server. Upon receiving the audio, the server reduces noise and optimizes the audio based on the user's hearing profile. The optimized audio data is converted to text using natural language processing, and then an emotion engine adjusts the text and audio according to the user's emotional state.

[0542] The emotion engine analyzes features such as voice intonation, speed, and facial expressions to infer the user's emotions. Based on this, the system determines a voice tone and speed that matches the user's current emotions and adjusts text messages as needed. This data is sent to the device and provided through the user interface.

[0543] As a concrete example, consider a situation where a user is nervous during a meeting. The device acquires their voice, and the server optimizes the audio with appropriate volume and noise level. The emotion engine detects the user's tension from the tempo and intensity of their voice and their facial expressions, and the server uses this information to adjust the output audio to a calmer tone. The converted text information is also written in a way that alleviates tension.

[0544] Through a smartphone app, users can receive voice and text messages that are adjusted in real time based on the results of emotion recognition. All of these processes occur in real time, allowing users to have a better auditory and communication experience.

[0545] The following describes the processing flow.

[0546] Step 1:

[0547] The device uses its built-in microphone to acquire ambient sound. The acquired audio data includes the user's conversation and environmental noise. The device prepares the audio data for transmission to the server, and the data is prepared according to the transmission protocol.

[0548] Step 2:

[0549] The terminal compresses and encrypts the acquired voice data and sends it to the server over the network. This ensures communication efficiency and data security.

[0550] Step 3:

[0551] The server receives the transmitted audio data and first applies a noise reduction algorithm to remove ambient noise. In this process, specific frequency bands are selected to emphasize the human voice.

[0552] Step 4:

[0553] The server references the user's hearing profile and optimizes the noise-reduced audio data. By adjusting the volume and sound quality to the user's preferences, it generates audio that is easy to understand.

[0554] Step 5:

[0555] The server uses an emotion engine to analyze the user's emotional state from the characteristics, speed, and intonation of their voice. The emotion engine has the ability to predict the user's emotions in real time.

[0556] Step 6:

[0557] Based on the analysis results of the emotion engine, the server adjusts the tone and speed of the voice to optimize it. For example, if the user is nervous, the voice output will be adjusted to be calmer. Additionally, the converted text will be given expressions that reflect the emotion.

[0558] Step 7:

[0559] The server sends optimized and emotion-adapted voice and text data to the terminal. This data is provided to the user in the most optimal format.

[0560] Step 8:

[0561] The device plays the received audio data and outputs it in a format that is easy for the user to understand. At the same time, the device displays the converted text data on the user's smartphone or a dedicated display, providing visual support.

[0562] Step 9:

[0563] Through the provided interface, users can adjust volume, sound quality, and even the sensitivity of the emotion engine. This adjustment information is sent from the device to the server and continuously reflected in subsequent processes.

[0564] (Example 2)

[0565] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0566] Modern voice assistance systems often lack sufficient real-time emotion recognition and dynamic adjustment of speech and text information based on that recognition, making it difficult to provide a communication experience that aligns with the user's emotional state. Furthermore, they lack optimization of speech based on each user's auditory profile, failing to adequately address diverse user needs.

[0567] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0568] In this invention, the server includes a device for analyzing speech as data, a device for separating sound sources and reducing noise, a device for optimizing speech based on the user's auditory information, and a device for recognizing emotional states from text information and speech and adaptively adjusting speech and text information. This enables real-time adjustment of speech and text information according to the user's emotional state, thereby meeting diverse user needs.

[0569] A "speech acquisition device" is a device used to collect user speech in real time.

[0570] A "device for analyzing data" is a device that extracts specific information from audio and processes it as digital data.

[0571] A "noise reduction device" is a device that removes unwanted background noise from audio data and extracts clear audio.

[0572] A "device that optimizes speech based on auditory information" is a device that adjusts speech output based on the user's auditory characteristics.

[0573] A "device for converting to text information" is a device used to convert audio data into text data.

[0574] A "device for recognizing emotional states" is a device that analyzes and identifies a user's emotions from voice and text information.

[0575] A "device that adaptively adjusts speech and text information" is a device that dynamically changes speech and text information based on recognized emotions.

[0576] A "display device" is a device used to present the final, adjusted audio and text information to the user.

[0577] "Devices that transmit and receive data via a communication network" are devices used to exchange data over a network.

[0578] A "device for adjusting information characteristics" is a device used to customize the details of information based on the user's individual settings.

[0579] This invention is a system that optimizes voice support for users. It primarily utilizes a voice acquisition device, a server, and a user interface to achieve communication that takes user emotions into consideration.

[0580] The terminal first acquires the user's speech using a microphone or dedicated voice recording device. This voice data is then subjected to simple filtering on-site by initial processing software installed on the terminal, and then transmitted to the server via a secure communication protocol (e.g., SSL / TLS).

[0581] The server processes the received audio data using advanced noise reduction algorithms (e.g., spectral attenuation techniques). This process incorporates the user's auditory profile information to generate optimized audio. Subsequently, a natural language processing engine (e.g., speech recognition API) converts the audio into text. Furthermore, an emotion engine analyzes this data and determines the user's emotional state based on its content. The emotion engine considers the intonation and speed of the speech, as well as the meaning implied from the text information.

[0582] After the user's emotions are identified, the server adaptively adjusts the information. The tone and speed of the voice are adjusted to match the user's current emotional state, and the content of the text information is similarly modified. The adjusted information is then encrypted again and sent from the server to the terminal.

[0583] Users receive adjusted audio and text information through an interface displayed on their smartphones or computers. This interface is designed to visually present information in an easy-to-understand manner for the user.

[0584] A concrete example is a user who is feeling nervous during a meeting. In this case, the device acquires audio in real time, and the server detects the tension and adjusts the tone of voice to a calmer one. The text is also made to be calming for the user.

[0585] An example of a prompt message might be, "Recognize the user's emotions from the following audio data and generate audio and text information adjusted to an appropriate tone." This invention aims to provide user-centric voice support by integrating speech recognition and emotion adjustment.

[0586] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0587] Step 1:

[0588] The device uses a microphone to capture the user's voice in real time. Raw audio signals are collected as input. This data undergoes initial filtering to reduce background noise. The output is filtered audio data.

[0589] Step 2:

[0590] The terminal encrypts the acquired audio data and sends it to the server via a highly secure communication protocol such as SSL / TLS. The input for this step is filtered audio data, and the output is securely packaged audio data.

[0591] Step 3:

[0592] The server applies advanced noise reduction algorithms to the received audio data. This process utilizes techniques such as source separation and spectral attenuation. The input is encrypted audio data, and the output is clear audio data with the noise removed.

[0593] Step 4:

[0594] The server optimizes the audio based on the user's auditory profile, which includes volume and frequency characteristics. For visualization purposes, the optimization process involves filter design and dynamic range control. The input is denoised audio data, and the output is optimized audio data.

[0595] Step 5:

[0596] The server converts optimized speech into text information using a natural language processing engine. Speech recognition APIs are used in this step. The input is optimized speech data, and the output is text information.

[0597] Step 6:

[0598] The server uses an emotion engine to recognize the user's emotional state from speech and text information. It analyzes features such as intonation, speed, volume, and content to infer emotions. The input is optimized speech data and text information, and the output is the user's emotional information.

[0599] Step 7:

[0600] The server adjusts the audio and text information according to the user's emotional state. The tone, speed, and content of the audio are appropriately modified. The input consists of optimized audio data, text information, and emotional information, while the output is adjusted audio and text.

[0601] Step 8:

[0602] The server sends the adjusted audio and text information back to the terminal. Again, the data is encrypted and transmitted via a secure communication protocol. The input is adjusted audio and text, and the output is securely packaged data.

[0603] Step 9:

[0604] The terminal displays received data on a user interface, and the user receives visual and audible feedback. Applications on the terminal allow the user to view the adjustment results in real time. Input is securely packaged data, and output is adjusted information presented to the user.

[0605] (Application Example 2)

[0606] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0607] In recent years, personal assistant devices that support communication within the home have become widespread. However, existing technologies cannot analyze the emotional state of a voice in real time and adjust the voice and text information appropriately based on that analysis. This presents a problem in that it is difficult to respond flexibly to the psychological state of individual users.

[0608] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0609] In this invention, the server includes means for acquiring sound, means for separating the sound source and reducing noise, and means for performing emotion analysis and adjusting the voice and text information based on the analysis results. This enables real-time adjustment of voice and text information based on the emotional state of the user within the home.

[0610] "Means for acquiring sound" refers to a device that converts sound from a physical environment into digital data and makes it usable in a system.

[0611] "Means of analyzing audio as data" refers to a function that structures acquired audio data and performs a process to evaluate its content and characteristics.

[0612] "Means for separating sound sources and reducing noise" refers to techniques that identify multiple voices or sound sources, extract them individually, and remove unwanted noise.

[0613] "Means of optimizing audio based on user auditory information" refers to a process that adjusts volume and frequency characteristics considering each user's auditory profile to provide an optimal listening experience.

[0614] "Optimized means of converting speech into text information" refers to speech recognition technology for converting speech signals into text format.

[0615] "Means for displaying optimized speech and converted text information" refers to technical means such as display devices that visually present processed speech and text to the user.

[0616] "Means of performing emotion analysis and adjusting audio and text information based on the analysis results" refers to a technology that analyzes the intonation, speed, and other characteristics of audio data to infer the user's emotional state, and dynamically changes the tone of the audio and text content accordingly.

[0617] "Means for transmitting and receiving voice and text data via a communication network" refers to communication devices and protocols for exchanging voice and text data with others over a network.

[0618] "Means that enable volume and quality adjustment through the user interface" refers to control panels or software functions that allow users to manually change the system's audio output settings.

[0619] The system that realizes this application is a voice assistance system that operates by integrating multiple means. First, the terminal is equipped with a high-sensitivity microphone to acquire voice and capture the user's voice in real time. The captured voice data is transmitted to a server using wireless communication technology.

[0620] The server performs noise reduction processing on the received audio data. This processing is done using, for example, the audio processing function of the "Google Cloud Natural Language API". Next, the server generates text information from the audio data and converts it into text using natural language processing (NLP) techniques. Other API services such as "IBM Watson" can also be used in this step.

[0621] The server then uses an emotion analysis engine to analyze the intonation and tempo of the voice and infer the user's emotions. Based on this analysis, it optimizes the voice tone and text content to provide a service tailored to the user's emotional state. In this process, it utilizes emotion analysis tools such as "Microsoft Azure Emotion API".

[0622] The processed information is sent back to the terminal, and the voice and text information is provided through the user interface. This allows the user to have a real-time, optimized communication experience. The terminal also accepts user input and provides a UI that allows for volume and sound quality adjustments.

[0623] As a concrete example, this system can read the emotional state of children getting ready in the morning and deliver encouraging messages in a gentle tone. For example, a prompt might be: "Imagine a scenario where a robot measures a child's emotions and encourages them before they go to school in the morning. Specifically, describe what kind of emotional analysis would be performed and what kind of engagement is possible." This allows the system to provide the optimal response based on the imagined scenario.

[0624] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0625] Step 1:

[0626] The device uses a high-sensitivity microphone to capture the user's voice in real time. This voice data is converted into a digital format. The input is ambient sound information, and the output is the digitized voice data.

[0627] Step 2:

[0628] The terminal transmits the acquired audio data to the server. Wireless communication technology is used for this process, and the data is compressed for efficient transmission. The input is digitized audio data, and the output is compressed audio data transmitted through a communication protocol.

[0629] Step 3:

[0630] The server performs noise reduction processing on the received audio data. This utilizes filtering techniques that remove background noise using audio processing software. The input is compressed audio data, and the output is clear audio data with reduced noise.

[0631] Step 4:

[0632] The server converts the noise-reduced audio data into text using natural language processing algorithms. By using services such as the "Google Cloud Speech-to-Text API," it converts speech into text information with high accuracy. The input is noise-reduced audio data, and the output is text information.

[0633] Step 5:

[0634] The server uses an emotion analysis engine to analyze the intonation and speed of the voice data and infer the user's emotions. This analysis is based on characteristics such as the volume and tempo of the voice. The input is clear voice data, and the output is data indicating the user's emotional state.

[0635] Step 6:

[0636] The server optimizes the voice tone and text information according to the user's needs based on the emotion analysis results. It also uses speech synthesis technology to generate a voice tone appropriate to the user's emotions as needed. The input consists of analyzed emotion data and original voice data / text information, while the output consists of the adjusted voice and text information.

[0637] Step 7:

[0638] The server transmits the adjusted audio and text information to the terminal, which is then provided to the user via the user interface. The audio output is played through the speaker, and the text information is displayed on the screen. The input is the adjusted audio and text information, and the output is the audio and text information presented to the user.

[0639] Step 8:

[0640] The user adjusts the volume and sound quality through the interface by operating the device. The device adjusts the audio output in real time in response to the user's input. The input is the user's operation commands, and the output is the adjusted audio output.

[0641] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0642] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0643] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0644] [Fourth Embodiment]

[0645] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0646] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0647] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0648] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0649] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0650] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0651] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0652] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0653] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0654] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0655] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0656] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0657] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0658] This invention provides a system for advanced voice assistance to users, aiming to acquire voice, reduce noise, and then optimize and deliver it. The system has a complex structure including a terminal, a server, and a user interface. The terminal is responsible for acquiring voice and transmitting it to the server. The server analyzes the received voice data, reduces noise by separating the sound source, and optimizes the voice based on the user's hearing profile.

[0659] Next, the server uses natural language processing technology to convert the optimized speech into text. This text data is used as information for the user to visually confirm. The server sends the optimized speech and text to the terminal, which then provides them to the user. The terminal plays the received optimized speech and simultaneously displays the converted text via a smartphone or dedicated device, thereby supporting the user's communication.

[0660] As a concrete example, consider a scenario where a user is in a noisy environment such as a cafe. In this system, the terminal acquires ambient sounds and sends them to a server. The server then processes the ambient noise using noise reduction technology, making only the user's voice clear. Furthermore, the server uses a language model to convert the conversation into text, which is then displayed to the user as visual information via the terminal. In this way, the user can understand the conversation in both audio and text formats. This entire process takes place in real time, and the user can adjust volume and sound quality settings through an intuitive and easy-to-use interface. This enables clear communication even in various environments.

[0661] The following describes the processing flow.

[0662] Step 1:

[0663] The device uses its built-in microphone to acquire ambient audio data. This data includes the user's conversation and environmental noise. The acquired audio data is immediately placed in a state awaiting transmission to the server.

[0664] Step 2:

[0665] The terminal transmits the acquired voice data to the server via the network. During this process, the data is compressed and encrypted to ensure communication efficiency and security.

[0666] Step 3:

[0667] The server analyzes the received audio data. It applies speech separation technology to distinguish between human voices and noise, and suppresses unwanted sounds through a noise reduction process.

[0668] Step 4:

[0669] The server references the user's hearing profile and optimizes the audio data. This optimization includes processes that emphasize specific frequency bands and signal processing to make human voices easier to recognize.

[0670] Step 5:

[0671] The server converts optimized speech into text using natural language processing techniques. The generated text data is used as visual support for communication.

[0672] Step 6:

[0673] The server sends optimized voice and text data to the terminal. Here too, the necessary processing is performed to efficiently and securely transfer the data over the network.

[0674] Step 7:

[0675] The device plays the optimized audio data it receives, delivering it to the user as clear audio. Simultaneously, the device displays the text data on the user's smartphone or a dedicated device, enabling visual confirmation.

[0676] Step 8:

[0677] Users can change volume and sound quality settings using the smartphone app interface according to their individual needs regarding how they hear audio. The device notifies the server of these changes, which are then reflected in future audio optimizations.

[0678] (Example 1)

[0679] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0680] Current speech recognition technology has problems such as not being able to clarify speech in noisy environments and not being able to adequately optimize speech according to the individual hearing ability of users. In addition, there is a growing need for efficiency in data reception and transmission between devices, and for information provision that combines visual information.

[0681] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0682] In this invention, the server includes a device for acquiring acoustic signals, a device for identifying sound sources and suppressing unwanted components, and a device for optimizing acoustic signals based on the user's auditory information. This enables clear speech recognition even in noisy environments, and achieves personalized speech optimization and simultaneous provision of visual information.

[0683] An "acoustic signal" is electrical or vibrational information generated by sound or voice.

[0684] An "apparatus" is a mechanical or electronic means designed to perform a specific function.

[0685] "Information" refers to the content and meaningful data that are transmitted as knowledge or data.

[0686] "Analysis" is the process of examining information and data in detail to understand its structure and meaning.

[0687] A "sound source" is the place or object from which sound originates.

[0688] "Unwanted components" refer to parts of an acoustic signal that are unintended as noise or interference.

[0689] "Suppression" refers to means or actions that reduce or eliminate unwanted influences or sounds.

[0690] "Optimization" refers to adjusting conditions to maximize the performance of a system or process.

[0691] "Auditory information" refers to data related to how each individual user hears, and includes information that is unique to that person's hearing characteristics.

[0692] "Textual information" refers to a set of encoded symbols or characters used to visually represent sound.

[0693] This system is designed to allow users to enjoy clear voice communication. Its main components include a terminal, a server, and a user interface. The operation and coordination of each component enables accurate speech recognition and visual assistance even in noisy environments.

[0694] The terminal functions as a device for acquiring acoustic signals. Specifically, a smartphone or dedicated device is used to capture ambient sound. This acoustic signal is converted into a digital format and transmitted to a server via the network. The communication protocol used is selected to be secure in order to ensure data safety.

[0695] The server functions as a device that analyzes the received acoustic signal. First, techniques are applied to identify the sound source and suppress background noise. This is achieved through spectral subtraction and machine learning algorithms. Furthermore, digital signal processing is used to optimize the acoustic signal based on the user's unique auditory information. This optimization process makes the adjusted acoustic signal easier for the user to hear.

[0696] The optimized acoustic signal is converted into text information by a server using natural language processing technology. This process is made more accurate by using a speech recognition engine. This converted text information and the optimized acoustic signal are then sent back to the terminal.

[0697] The device plays back received audio signals while simultaneously displaying text information on its screen. This allows users to obtain information through both auditory and visual means. Furthermore, it provides functions to adjust volume and acoustic characteristics through the user interface, allowing users to customize their experience.

[0698] For example, if a user is in a noisy cafe, this system effectively reduces ambient noise and clarifies the user's voice. As a result, the user can understand what the other person is saying from both text and audio. An example of a prompt sentence for the generative AI model is, "How can I reduce noise in real time and convert speech to text?" The entire system is designed to operate efficiently in real time.

[0699] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0700] Step 1:

[0701] The device uses a built-in microphone to acquire ambient acoustic signals in real time. The input is raw sound from the environment, and the output is digital acoustic data. This conversion makes the acoustic data usable for transmission over a network. Specifically, analog audio is converted into digital data using an ADC (analog-to-digital converter).

[0702] Step 2:

[0703] The terminal transmits digital audio data to the server via the network. The input is the previously converted digital audio data, and the output is the audio data received by the server. The terminal transmits data while ensuring security using the SSL / TLS protocol.

[0704] Step 3:

[0705] The server analyzes the received acoustic data and applies a noise reduction algorithm. The input is the acoustic data received from the terminal, and the output is the audio data with reduced noise. Specifically, the server analyzes the frequency components using spectral subtraction and suppresses the noise components.

[0706] Step 4:

[0707] The server optimizes the noise-reduced audio data based on the user's hearing profile. The input is the noise-reduced audio data, and the output is the optimized audio data. The server applies DSP (Digital Signal Processing) parameters specific to each user to adjust the audio quality.

[0708] Step 5:

[0709] The server converts optimized audio data into text information using natural language processing technology. The input is optimized audio data, and the output is text data. Specifically, a speech recognition engine analyzes the audio and performs highly accurate text conversion.

[0710] Step 6:

[0711] The server sends optimized audio data and converted text data to the terminal. The input is the audio data and text data, and the output is this data sent to the terminal. The SSL / TLS protocol is used again for transmission to maintain data integrity and security.

[0712] Step 7:

[0713] The terminal plays the received audio data while displaying the converted text data on its screen. The input consists of audio and text data sent from the server, and the output is audiovisual information provided to the user. Specifically, it performs the function of playing audio through the terminal's speaker and displaying text on the screen.

[0714] Step 8:

[0715] Users adjust the volume and acoustic characteristics through the device's user interface. Input is user interaction through an easy-to-use interface, while output is the adjusted volume and sound quality. Users can change settings using the touchscreen or physical buttons.

[0716] (Application Example 1)

[0717] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0718] The problem that this invention aims to solve is to enable users to communicate smoothly through voice even in noisy environments. In particular, it aims to enable users to acquire clear voice and text information in environments where background noise exists, such as in homes or public places.

[0719] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0720] In this invention, the server includes means for acquiring acoustic information, means for separating sound sources and reducing noise, and means for reducing ambient noise and supporting clear interaction with the user. This makes it possible for the user to obtain clear audio and text information even in noisy environments.

[0721] "Acoustic information" refers to data that digitally captures sound waves, such as voices and ambient sounds, that travel through the air.

[0722] "Noise" refers to unwanted sound components that are mixed in with the target acoustic information, and it is a factor that affects speech recognition and sound quality.

[0723] A "user" is a person who uses this system to obtain clear audio and textual information.

[0724] "Hearing information" refers to characteristic information about how each user hears sounds, and is used to optimize speech.

[0725] "Textual information" refers to text data obtained by analyzing speech and performing natural language processing.

[0726] "Information network" refers to communication methods and network infrastructure for exchanging digital information.

[0727] A "connection device" refers to a device used by the user, such as a smartphone, tablet, or dedicated device.

[0728] To implement this invention, a user-operated connection device (e.g., a consumer robot) is required. The connection device is equipped with a microphone to acquire acoustic information. The server analyzes the acquired acoustic information and uses software to reduce noise. Specifically, it collects acoustic data using "pyaudio" and reduces noise using "noise_reduction_module". The server then converts the information into text information using the "speech_recognition" module and further optimizes the sound based on the user's hearing information using "voice_profile_optimizer".

[0729] The server has the functionality to convert optimized acoustic information back into speech using the "text_to_speech" module, while simultaneously displaying text information using "DisplayText". The acoustic and text information is transmitted to the connected device via the information network and provided to the user.

[0730] For example, if a user asks the robot "What's my schedule for today?" in a noisy home environment, the server will remove the noise from the voice and provide the schedule information to the user in both voice and text format.

[0731] An example of a prompt message would be: "Explain how a voice processing system that reduces noise can be applied to home robots. In particular, give specific examples of how it can be used to communicate clearly with users."

[0732] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0733] Step 1:

[0734] The user acquires acoustic information through the microphone of a consumer robot. Raw acoustic data, including ambient sounds and the user's voice, is input. This input data is transmitted to the server via a connecting device.

[0735] Step 2:

[0736] The server analyzes the received audio data using "pyaudio" and reduces noise. The "noise_reduction_module" separates the sound source and removes unwanted noise. The output is clear audio data with reduced noise.

[0737] Step 3:

[0738] The server uses "voice_profile_optimizer" to optimize the audio data, which has reduced noise, based on the user's hearing information. This optimization process outputs sound quality tailored to each individual user.

[0739] Step 4:

[0740] The server converts the optimized acoustic data into text information using "speech_recognition". In this step, the speech is output as natural language text data, and user instructions and questions are captured as text information.

[0741] Step 5:

[0742] Textual information and optimized audio data are transmitted to the user's connected device via an information network. The output data is used by the robot to play back audio and display the textual information on a display.

[0743] Step 6:

[0744] The user's connected device plays the audio data using "text_to_speech" and displays the text information on the screen using "DisplayText". This allows the user to hear the response clearly in audio and simultaneously confirm it as text.

[0745] Step 7:

[0746] Ultimately, smooth voice communication is facilitated by providing the user with voice and text information generated by the server. System operation can be simulated and tuned using prompt examples generated by the AI ​​model.

[0747] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0748] This invention is a system for providing voice support to users, encompassing everything from acquiring and optimizing voice data to providing visual support. Furthermore, it incorporates an emotion engine that recognizes the user's emotional state and adaptively adjusts voice and text based on that information. This system consists of a terminal, a server, a user interface, and the emotion engine.

[0749] The device acquires audio on-site and sends the data to the server. Upon receiving the audio, the server reduces noise and optimizes the audio based on the user's hearing profile. The optimized audio data is converted to text using natural language processing, and then an emotion engine adjusts the text and audio according to the user's emotional state.

[0750] The emotion engine analyzes features such as voice intonation, speed, and facial expressions to infer the user's emotions. Based on this, the system determines a voice tone and speed that matches the user's current emotions and adjusts text messages as needed. This data is sent to the device and provided through the user interface.

[0751] As a concrete example, consider a situation where a user is nervous during a meeting. The device acquires their voice, and the server optimizes the audio with appropriate volume and noise level. The emotion engine detects the user's tension from the tempo and intensity of their voice and their facial expressions, and the server uses this information to adjust the output audio to a calmer tone. The converted text information is also written in a way that alleviates tension.

[0752] Through a smartphone app, users can receive voice and text messages that are adjusted in real time based on the results of emotion recognition. All of these processes occur in real time, allowing users to have a better auditory and communication experience.

[0753] The following describes the processing flow.

[0754] Step 1:

[0755] The device uses its built-in microphone to acquire ambient sound. The acquired audio data includes the user's conversation and environmental noise. The device prepares the audio data for transmission to the server, and the data is prepared according to the transmission protocol.

[0756] Step 2:

[0757] The terminal compresses and encrypts the acquired voice data and sends it to the server over the network. This ensures communication efficiency and data security.

[0758] Step 3:

[0759] The server receives the transmitted audio data and first applies a noise reduction algorithm to remove ambient noise. In this process, specific frequency bands are selected to emphasize the human voice.

[0760] Step 4:

[0761] The server references the user's hearing profile and optimizes the noise-reduced audio data. By adjusting the volume and sound quality to the user's preferences, it generates audio that is easy to understand.

[0762] Step 5:

[0763] The server uses an emotion engine to analyze the user's emotional state from the characteristics, speed, and intonation of their voice. The emotion engine has the ability to predict the user's emotions in real time.

[0764] Step 6:

[0765] Based on the analysis results of the emotion engine, the server adjusts the tone and speed of the voice to optimize it. For example, if the user is nervous, the voice output will be adjusted to be calmer. Additionally, the converted text will be given expressions that reflect the emotion.

[0766] Step 7:

[0767] The server sends optimized and emotion-adapted voice and text data to the terminal. This data is provided to the user in the most optimal format.

[0768] Step 8:

[0769] The device plays the received audio data and outputs it in a format that is easy for the user to understand. At the same time, the device displays the converted text data on the user's smartphone or a dedicated display, providing visual support.

[0770] Step 9:

[0771] Through the provided interface, users can adjust volume, sound quality, and even the sensitivity of the emotion engine. This adjustment information is sent from the device to the server and continuously reflected in subsequent processes.

[0772] (Example 2)

[0773] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0774] Modern voice assistance systems often lack sufficient real-time emotion recognition and dynamic adjustment of speech and text information based on that recognition, making it difficult to provide a communication experience that aligns with the user's emotional state. Furthermore, they lack optimization of speech based on each user's auditory profile, failing to adequately address diverse user needs.

[0775] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0776] In this invention, the server includes a device for analyzing speech as data, a device for separating sound sources and reducing noise, a device for optimizing speech based on the user's auditory information, and a device for recognizing emotional states from text information and speech and adaptively adjusting speech and text information. This enables real-time adjustment of speech and text information according to the user's emotional state, thereby meeting diverse user needs.

[0777] A "speech acquisition device" is a device used to collect user speech in real time.

[0778] A "device for analyzing data" is a device that extracts specific information from audio and processes it as digital data.

[0779] A "noise reduction device" is a device that removes unwanted background noise from audio data and extracts clear audio.

[0780] A "device that optimizes speech based on auditory information" is a device that adjusts speech output based on the user's auditory characteristics.

[0781] A "device for converting to text information" is a device used to convert audio data into text data.

[0782] A "device for recognizing emotional states" is a device that analyzes and identifies a user's emotions from voice and text information.

[0783] A "device that adaptively adjusts speech and text information" is a device that dynamically changes speech and text information based on recognized emotions.

[0784] A "display device" is a device used to present the final, adjusted audio and text information to the user.

[0785] "Devices that transmit and receive data via a communication network" are devices used to exchange data over a network.

[0786] A "device for adjusting information characteristics" is a device used to customize the details of information based on the user's individual settings.

[0787] This invention is a system that optimizes voice support for users. It primarily utilizes a voice acquisition device, a server, and a user interface to achieve communication that takes user emotions into consideration.

[0788] The terminal first acquires the user's speech using a microphone or dedicated voice recording device. This voice data is then subjected to simple filtering on-site by initial processing software installed on the terminal, and then transmitted to the server via a secure communication protocol (e.g., SSL / TLS).

[0789] The server processes the received audio data using advanced noise reduction algorithms (e.g., spectral attenuation techniques). This process incorporates the user's auditory profile information to generate optimized audio. Subsequently, a natural language processing engine (e.g., speech recognition API) converts the audio into text. Furthermore, an emotion engine analyzes this data and determines the user's emotional state based on its content. The emotion engine considers the intonation and speed of the speech, as well as the meaning implied from the text information.

[0790] After the user's emotions are identified, the server adaptively adjusts the information. The tone and speed of the voice are adjusted to match the user's current emotional state, and the content of the text information is similarly modified. The adjusted information is then encrypted again and sent from the server to the terminal.

[0791] Users receive adjusted audio and text information through an interface displayed on their smartphones or computers. This interface is designed to visually present information in an easy-to-understand manner for the user.

[0792] A concrete example is a user who is feeling nervous during a meeting. In this case, the device acquires audio in real time, and the server detects the tension and adjusts the tone of voice to a calmer one. The text is also made to be calming for the user.

[0793] An example of a prompt message might be, "Recognize the user's emotions from the following audio data and generate audio and text information adjusted to an appropriate tone." This invention aims to provide user-centric voice support by integrating speech recognition and emotion adjustment.

[0794] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0795] Step 1:

[0796] The device uses a microphone to capture the user's voice in real time. Raw audio signals are collected as input. This data undergoes initial filtering to reduce background noise. The output is filtered audio data.

[0797] Step 2:

[0798] The terminal encrypts the acquired audio data and sends it to the server via a highly secure communication protocol such as SSL / TLS. The input for this step is filtered audio data, and the output is securely packaged audio data.

[0799] Step 3:

[0800] The server applies advanced noise reduction algorithms to the received audio data. This process utilizes techniques such as source separation and spectral attenuation. The input is encrypted audio data, and the output is clear audio data with the noise removed.

[0801] Step 4:

[0802] The server optimizes the audio based on the user's auditory profile, which includes volume and frequency characteristics. For visualization purposes, the optimization process involves filter design and dynamic range control. The input is denoised audio data, and the output is optimized audio data.

[0803] Step 5:

[0804] The server converts optimized speech into text information using a natural language processing engine. Speech recognition APIs are used in this step. The input is optimized speech data, and the output is text information.

[0805] Step 6:

[0806] The server uses an emotion engine to recognize the user's emotional state from speech and text information. It analyzes features such as intonation, speed, volume, and content to infer emotions. The input is optimized speech data and text information, and the output is the user's emotional information.

[0807] Step 7:

[0808] The server adjusts the audio and text information according to the user's emotional state. The tone, speed, and content of the audio are appropriately modified. The input consists of optimized audio data, text information, and emotional information, while the output is adjusted audio and text.

[0809] Step 8:

[0810] The server sends the adjusted audio and text information back to the terminal. Again, the data is encrypted and transmitted via a secure communication protocol. The input is adjusted audio and text, and the output is securely packaged data.

[0811] Step 9:

[0812] The terminal displays received data on a user interface, and the user receives visual and audible feedback. Applications on the terminal allow the user to view the adjustment results in real time. Input is securely packaged data, and output is adjusted information presented to the user.

[0813] (Application Example 2)

[0814] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0815] In recent years, personal assistant devices that support communication within the home have become widespread. However, existing technologies cannot analyze the emotional state of a voice in real time and adjust the voice and text information appropriately based on that analysis. This presents a problem in that it is difficult to respond flexibly to the psychological state of individual users.

[0816] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0817] In this invention, the server includes means for acquiring sound, means for separating the sound source and reducing noise, and means for performing emotion analysis and adjusting the voice and text information based on the analysis results. This enables real-time adjustment of voice and text information based on the emotional state of the user within the home.

[0818] "Means for acquiring sound" refers to a device that converts sound from a physical environment into digital data and makes it usable in a system.

[0819] "Means of analyzing audio as data" refers to a function that structures acquired audio data and performs a process to evaluate its content and characteristics.

[0820] "Means for separating sound sources and reducing noise" refers to techniques that identify multiple voices or sound sources, extract them individually, and remove unwanted noise.

[0821] "Means of optimizing audio based on user auditory information" refers to a process that adjusts volume and frequency characteristics considering each user's auditory profile to provide an optimal listening experience.

[0822] "Optimized means of converting speech into text information" refers to speech recognition technology for converting speech signals into text format.

[0823] "Means for displaying optimized speech and converted text information" refers to technical means such as display devices that visually present processed speech and text to the user.

[0824] "Means of performing emotion analysis and adjusting audio and text information based on the analysis results" refers to a technology that analyzes the intonation, speed, and other characteristics of audio data to infer the user's emotional state, and dynamically changes the tone of the audio and text content accordingly.

[0825] "Means for transmitting and receiving voice and text data via a communication network" refers to communication devices and protocols for exchanging voice and text data with others over a network.

[0826] "Means that enable volume and quality adjustment through the user interface" refers to control panels or software functions that allow users to manually change the system's audio output settings.

[0827] The system that realizes this application is a voice assistance system that operates by integrating multiple means. First, the terminal is equipped with a high-sensitivity microphone to acquire voice and capture the user's voice in real time. The captured voice data is transmitted to a server using wireless communication technology.

[0828] The server performs noise reduction processing on the received audio data. This processing is done using, for example, the audio processing function of the "Google Cloud Natural Language API". Next, the server generates text information from the audio data and converts it into text using natural language processing (NLP) techniques. Other API services such as "IBM Watson" can also be used in this step.

[0829] The server then uses an emotion analysis engine to analyze the intonation and tempo of the voice and infer the user's emotions. Based on this analysis, it optimizes the voice tone and text content to provide a service tailored to the user's emotional state. In this process, it utilizes emotion analysis tools such as "Microsoft Azure Emotion API".

[0830] The processed information is sent back to the terminal, and the voice and text information is provided through the user interface. This allows the user to have a real-time, optimized communication experience. The terminal also accepts user input and provides a UI that allows for volume and sound quality adjustments.

[0831] As a concrete example, this system can read the emotional state of children getting ready in the morning and deliver encouraging messages in a gentle tone. For example, a prompt might be: "Imagine a scenario where a robot measures a child's emotions and encourages them before they go to school in the morning. Specifically, describe what kind of emotional analysis would be performed and what kind of engagement is possible." This allows the system to provide the optimal response based on the imagined scenario.

[0832] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0833] Step 1:

[0834] The device uses a high-sensitivity microphone to capture the user's voice in real time. This voice data is converted into a digital format. The input is ambient sound information, and the output is the digitized voice data.

[0835] Step 2:

[0836] The terminal transmits the acquired audio data to the server. Wireless communication technology is used for this process, and the data is compressed for efficient transmission. The input is digitized audio data, and the output is compressed audio data transmitted through a communication protocol.

[0837] Step 3:

[0838] The server performs noise reduction processing on the received audio data. This utilizes filtering techniques that remove background noise using audio processing software. The input is compressed audio data, and the output is clear audio data with reduced noise.

[0839] Step 4:

[0840] The server converts the noise-reduced audio data into text using natural language processing algorithms. By using services such as the "Google Cloud Speech-to-Text API," it converts speech into text information with high accuracy. The input is noise-reduced audio data, and the output is text information.

[0841] Step 5:

[0842] The server uses an emotion analysis engine to analyze the intonation and speed of the voice data and infer the user's emotions. This analysis is based on characteristics such as the volume and tempo of the voice. The input is clear voice data, and the output is data indicating the user's emotional state.

[0843] Step 6:

[0844] The server optimizes the voice tone and text information according to the user's needs based on the emotion analysis results. It also uses speech synthesis technology to generate a voice tone appropriate to the user's emotions as needed. The input consists of analyzed emotion data and original voice data / text information, while the output consists of the adjusted voice and text information.

[0845] Step 7:

[0846] The server transmits the adjusted audio and text information to the terminal, which is then provided to the user via the user interface. The audio output is played through the speaker, and the text information is displayed on the screen. The input is the adjusted audio and text information, and the output is the audio and text information presented to the user.

[0847] Step 8:

[0848] The user adjusts the volume and sound quality through the interface by operating the device. The device adjusts the audio output in real time in response to the user's input. The input is the user's operation commands, and the output is the adjusted audio output.

[0849] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0850] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0851] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0852] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0853] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0854] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0855] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0856] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0857] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0858] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0859] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0860] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0861] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0862] 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.

[0863] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0864] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0865] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0866] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0867] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0868] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0869] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0870] The following is further disclosed regarding the embodiments described above.

[0871] (Claim 1)

[0872] Means of acquiring sound,

[0873] A means for analyzing the audio as data,

[0874] A means of separating sound sources and reducing noise,

[0875] A means for optimizing speech based on the user's hearing information,

[0876] A means of converting optimized speech to text,

[0877] A means of displaying optimized speech and converted text,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, further comprising means for transmitting and receiving voice and text data over a network.

[0881] (Claim 3)

[0882] The system according to claim 1, further comprising means for enabling adjustment of volume and sound quality through a user interface.

[0883] "Example 1"

[0884] (Claim 1)

[0885] A device for acquiring acoustic signals,

[0886] A device for analyzing the acoustic signal as information,

[0887] A device that identifies sound sources and suppresses unwanted components,

[0888] A device that optimizes acoustic signals based on the user's auditory information,

[0889] A device that converts optimized acoustic signals into text information,

[0890] A device that displays optimized acoustic signals and converted character information,

[0891] A system that includes this.

[0892] (Claim 2)

[0893] The system according to claim 1, further comprising a device for transmitting and receiving acoustic signals and character data over an information network.

[0894] (Claim 3)

[0895] The system according to claim 1, further comprising a device that enables adjustment of volume and acoustic characteristics through a user interface.

[0896] "Application Example 1"

[0897] (Claim 1)

[0898] Means for acquiring acoustic information,

[0899] A means for analyzing the acoustic information as information,

[0900] A means of separating sound sources and reducing noise,

[0901] A means for optimizing sound based on the user's hearing information,

[0902] A means of converting optimized sound into text information,

[0903] A means for displaying optimized sound and converted text information,

[0904] A means to reduce ambient noise and support clear communication with the user,

[0905] A system that includes this.

[0906] (Claim 2)

[0907] The system according to claim 1, further comprising means for transmitting and receiving acoustic and textual data via an information network.

[0908] (Claim 3)

[0909] The system according to claim 1, further comprising means for enabling adjustment of volume and sound quality through a user's connection device.

[0910] "Example 2 of combining an emotion engine"

[0911] (Claim 1)

[0912] A device for acquiring sound,

[0913] A device for analyzing the audio as data,

[0914] A device that separates sound sources and reduces noise,

[0915] A device that optimizes speech based on the user's auditory information,

[0916] A device that converts optimized speech into text information,

[0917] A device that recognizes emotional states from textual and auditory information and adaptively adjusts the auditory and textual information,

[0918] A device that displays optimized speech and adjusted text information,

[0919] A system that includes this.

[0920] (Claim 2)

[0921] The system according to claim 1, further comprising a device for transmitting and receiving voice and text data over a communication network.

[0922] (Claim 3)

[0923] The system according to claim 1, further comprising a device that enables the adjustment of information characteristics through a user interface.

[0924] "Application example 2 when combining with an emotional engine"

[0925] (Claim 1)

[0926] Means of acquiring sound,

[0927] A means for analyzing the audio as data,

[0928] A means of separating sound sources and reducing noise,

[0929] A means for optimizing speech based on the user's auditory information,

[0930] A means of converting optimized speech into text information,

[0931] A means for displaying optimized speech and converted text information,

[0932] A means of performing emotion analysis and adjusting audio and text information based on the analysis results,

[0933] A system that includes this.

[0934] (Claim 2)

[0935] The system according to claim 1, further comprising means for transmitting and receiving voice and text data over a communication network.

[0936] (Claim 3)

[0937] The system according to claim 1, further comprising means for enabling adjustment of volume and quality through a user interface. [Explanation of Symbols]

[0938] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of acquiring sound, A means for analyzing the audio as data, A means of separating sound sources and reducing noise, A means for optimizing speech based on the user's hearing information, A means of converting optimized speech to text, A means of displaying optimized speech and converted text, A system that includes this.

2. The system according to claim 1, further comprising means for transmitting and receiving voice and text data over a network.

3. The system according to claim 1, further comprising means for enabling adjustment of volume and sound quality through a user interface.

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