system

Abstract

We provide the system. [Solution] A means of converting audio data into text data, A means for analyzing the text data and extracting important information, Means for converting text back into speech or generating related images based on the extracted information, Means for providing the generated audio or image to the user, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the evolution of information technology, it has become necessary to access information in various media formats. However, for people with visual or auditory impairments, accessing information is still often difficult, and in particular, there is a lack of means for integrally utilizing voice, text, and image formats. Also, in creative content production and educational settings, it is not easy to convert between different media formats, and devices for deepening user understanding are required. Technologies for efficiently solving these problems are demanded.

Means for Solving the Problems

[0005] This invention provides a system that improves information accessibility by comprising means for converting audio data into text data, means for analyzing the text data to extract important information, and means for converting the text back into audio or generating related images based on the extracted information. This makes it easier for people with visual or hearing impairments to access information, and also improves the efficiency of information transmission in educational and business settings. Furthermore, background noise is removed and the audio is clarified during the conversion of audio data, and natural language processing technology is used for the analysis of text data, enabling highly accurate information processing.

[0006] "Audio data" refers to digital or analog data that represents information in the form of sound.

[0007] "Text data" refers to digital data that uses characters and symbols to represent information.

[0008] "Conversion" refers to the process of replacing data in one format with another.

[0009] "Analysis" is the process of evaluation and interpretation performed on data in order to understand its content and identify important information.

[0010] "Information extraction" refers to taking out important or useful data from existing data.

[0011] "Natural language processing technology" is a technology that enables computers to understand and generate human language.

[0012] "Re-converting to speech" is the process of generating the original speech from text data.

[0013] "Generating related images" means creating image data that visually represents the content of specific text or audio.

[0014] "Removing background noise" is a process of deleting irrelevant sounds other than the main voice from voice data.

[0015] "Clarifying voice" means obtaining clear and easy-to-hear voice by processing voice data.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Embodiments for Carrying out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

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

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0031] 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.

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

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention is a system aimed at making information more accessible to a diverse range of users, including those with visual and hearing impairments. This system has the functionality to convert audio data into text data, analyze that text data to extract important information, and, if necessary, convert the text back into audio or generate related images. Specific implementations are described below.

[0038] First, the user inputs voice through their device. The voice is typically recorded via a microphone and sent directly to the server as digital data. The server then uses a speech recognition algorithm to convert the received voice data into text. This conversion process utilizes noise filtering technology to improve voice clarity, thereby increasing the accuracy of the converted text.

[0039] Next, the server applies natural language processing techniques to the converted text data to analyze its content. Specifically, it performs contextual understanding and extracts key phrases. Through this process, the system can identify important parts of the information contained and highlight information that is useful to the user.

[0040] Furthermore, based on the analysis results, the server selects the re-output format to suit the user's needs. This includes methods such as converting the extracted text information back into speech, as well as generating appropriate images from the text for visualization. The generated audio and image data are sent to the user's device for display or playback.

[0041] For example, when this system is applied to recording meetings, participants' voices are captured in real time and immediately recorded as text. This transcribed information is provided to participants in a way that highlights the important points and action items of the meeting. Participants with hearing impairments can follow the progress of the meeting by reading the text displayed in real time. In educational settings, it is also possible to use text and images generated from audio as part of multimedia teaching materials to support learners' understanding.

[0042] Thus, this system, which processes audio, text, and images in an integrated manner, will improve information accessibility in a variety of situations.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user inputs voice using the device. The voice is captured through the device's microphone and temporarily stored as digital data.

[0046] Step 2:

[0047] The terminal sends the captured audio data to the server. During this process, the data is converted to an appropriate format and transmitted via the communication line.

[0048] Step 3:

[0049] The server passes the audio data received from the terminal to speech recognition software, which converts the audio into text data. This conversion process applies a noise reduction filter to improve the clarity of the speech.

[0050] Step 4:

[0051] The server passes the obtained text data to a natural language processing algorithm for content analysis. This analysis understands the context from the text and extracts important information and key phrases.

[0052] Step 5:

[0053] Based on the analysis results, the server selects the appropriate output format. Specifically, this includes means of converting text back into speech and generating related images.

[0054] Step 6:

[0055] The generated audio or image data is sent from the server to the terminal. A secure communication protocol is used for transmission to ensure accurate data delivery.

[0056] Step 7:

[0057] The device provides the user with the received data. Audio data is played through the speaker, and image data and text data are displayed on the screen.

[0058] (Example 1)

[0059] 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."

[0060] Conventional information access systems have made it difficult for diverse users, including those with visual and hearing impairments, to efficiently acquire and utilize information. In particular, there were problems with the quality of information degrading due to noise contamination during the conversion of audio information and insufficient identification of important information during text analysis. Furthermore, it was difficult to output the acquired information in a format that met the individual needs of users, posing accessibility challenges.

[0061] 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.

[0062] In this invention, the server includes means for converting audio information into symbolic information, means for analyzing the symbolic information to identify important information, means for converting the symbols back into audio or generating related visual information based on the identified information, and means for selecting an output format according to the user's request based on the integrated information. This makes it possible to process diverse information centrally and provide information in the optimal format to meet the needs of each user while maintaining the quality of the information.

[0063] "Audio information" refers to data acquired as sound waves, such as human voices.

[0064] "Symbolic information" refers to digital data converted from audio information, and is usually represented in text format.

[0065] "Analysis" refers to the process of extracting useful information from data, and includes understanding the context of the data and identifying important information.

[0066] "Visual information" refers to information that has been converted into a visually understandable form, and is provided as data such as images and diagrams.

[0067] "Filtering" is the process of removing unwanted elements from data, and in this context, it refers to the operation of removing noise from an audio signal.

[0068] "Language analysis technology" refers to techniques used to analyze text and understand its grammar and meaning, and includes natural language processing technology.

[0069] "Integrated information" refers to data from different formats that have been combined and compiled into a single, comprehensive set of information.

[0070] "User" refers to an individual who uses this system to obtain or manipulate information.

[0071] "Output format" refers to the final form in which information is presented, and includes formats such as audio, text, and images.

[0072] This invention provides a system that makes information more accessible to a diverse range of users, including those with visual and hearing impairments. This system converts audio information into symbolic information, analyzes that symbolic information to extract important information, and can also convert it back to audio or generate related visual information as needed.

[0073] The user inputs voice through the device. The voice input is transmitted via a microphone, converted to digital format, and then sent to the server. During this process, the device captures the voice in real time, minimizing latency through edge computing technology. The server applies a speech recognition algorithm to the received voice information, converting it into symbolic information. A common speech recognition engine can be used here.

[0074] The server analyzes the acquired symbolic information using language analysis techniques. This analysis process utilizes pre-trained natural language processing techniques to understand the text context and extract key phrases. For example, it can highlight information important to the user and provide a re-output format tailored to the user's intent.

[0075] The server uses the information generated based on the analysis to produce audio and visual information. A general-purpose speech synthesis engine can be used for audio generation, and image generation technology can be utilized for visual information generation. Users receive the generated results through their devices and can view the information, either played back as audio or displayed on the screen.

[0076] As a concrete example, this system can be used to transcribe audio information from a meeting into text and provide it to meeting participants in a format that highlights the key points. In this scenario, participants with hearing impairments can read the text information in real time and follow the progress of the meeting.

[0077] Examples of prompts include, "Transcribe the meeting audio into text and highlight the key points," and "Generate relevant images from the audio data to provide visual supplementation."

[0078] In this way, this system integrates audio, symbolic, and visual information to provide information tailored to the individual needs of users.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The user inputs voice using a terminal. Voice input is typically performed through a microphone, and the analog voice signal is converted into digital data. The terminal sends this digital voice data to the server. At this stage, the input is the user's voice, and the output is the digital voice data sent to the server.

[0082] Step 2:

[0083] The server applies a speech recognition algorithm to the received digital audio data. Noise filtering techniques are used to remove background noise and improve speech clarity. Specifically, noise components are removed from the audio data to generate highly accurate text data. At this stage, the input is digital audio data, and the output is the converted text data.

[0084] Step 3:

[0085] The server uses language analysis techniques to parse the converted text data. This process involves understanding the context of the text and extracting key phrases. Specifically, it uses natural language processing techniques to analyze the syntax within the text and highlight important information. The input at this stage is the text data, and the output is a list of important information or the corrected text data.

[0086] Step 4:

[0087] The server determines the output format that best suits the user's needs based on the analysis results. Using a generative AI model, it converts the data back into audio or generates images as visual complements. At this stage, the input is the analysis results, and the output is the generated audio or image data.

[0088] Step 5:

[0089] The server sends the generated audio and image data to the user's device. The device receives this data and provides it to the user in a format suitable for visual or auditory use. Specifically, it may play audio through a speaker or display images on a screen. At this stage, the input is the generated content, and the output is usable information for the user's experience.

[0090] (Application Example 1)

[0091] 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."

[0092] In factories, rapid and accurate information transmission and understanding are essential. However, traditional methods such as voice instructions and written documents have made it difficult to adequately convey information to workers with visual and hearing impairments. Furthermore, background noise in voice instructions can sometimes obscure the content. Therefore, there is a need for a system that allows workers to efficiently receive information and perform tasks safely and reliably.

[0093] 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.

[0094] In this invention, the server includes means for converting audio data into text data, means for analyzing the text data to extract important information, means for converting the text back into audio or generating related images based on the extracted information, means for visually presenting the extracted information using a visual display device, and means for analyzing audio instructions entered by the user and selecting appropriate process information. This enables factory workers to efficiently receive work instructions and safely perform their work without visual or auditory limitations.

[0095] "Audio data" refers to information obtained by digitizing audio signals, and it represents various sounds, including human voices.

[0096] "Text data" refers to information composed of strings of characters, and is a data format used to describe sentences, commands, and other similar content.

[0097] A "visual display device" is a device, such as a display or screen, that displays text or images to appeal to human vision.

[0098] "Speech recognition" is a technology that analyzes speech data and converts it into text data.

[0099] "Natural language processing technology" refers to the technology that enables computers to understand, interpret, and generate human language, and is used for analyzing text data and understanding context.

[0100] "Noise filtering" is a technique that removes unwanted background noise and other sounds from audio data to improve speech clarity.

[0101] "Speech re-conversion" is the process of generating speech based on text data and then providing it as speech again.

[0102] "Information extraction" refers to the process of taking out necessary or important parts of data.

[0103] "Process information" refers to information that includes the procedures and instructions necessary to perform a specific task or operation.

[0104] This invention is a system for streamlining information exchange among workers in a factory. This system begins with speech recognition using voice data, then utilizes natural language processing technology to extract important information as text data, and finally provides this information through a visual display device.

[0105] First, the operator inputs voice commands into the system via an input device. The server converts this voice data into a digital signal and uses noise filtering technology to improve the clarity of the voice. Next, speech recognition software converts the voice into text data.

[0106] The server applies natural language processing techniques to the obtained text data to perform contextual understanding and precisely analyze important information and process details. Furthermore, based on the analyzed information, speech re-conversion is performed, or related images are generated, and the information is presented on a visual display device. As a result, the information displayed on the visual display device becomes an important factor for workers to make decisions that enable them to perform their tasks efficiently.

[0107] For example, if a worker wants to know the maintenance procedure for a conveyor belt, they can instruct the smart glasses by saying, "Tell me the procedure for the conveyor belt." The server analyzes this instruction and displays the relevant procedure information on the screen, and can play it back as audio if necessary. This allows workers to receive information through both sight and sound, and acquire the knowledge necessary for the job from multiple perspectives.

[0108] As an example of a prompt, we will use the instruction, "Please describe the inspection procedure for the conveyor belt. Please keep each step simple and highlight the points to pay attention to." This prompt will contribute to the generation of specific and concise work procedures through the generative AI model.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The user inputs voice commands through a terminal. The terminal converts this voice data into a digital signal and sends it to the server. The input is the user's voice, and the output is digital voice data.

[0112] Step 2:

[0113] The server performs noise filtering on the received digital audio data to improve speech clarity. The input is digital audio data, and the output is clear audio data with the noise removed.

[0114] Step 3:

[0115] The server processes clear audio data using a speech recognition algorithm to convert it into text data. The input is noise-removed audio data, and the output is the corresponding text data.

[0116] Step 4:

[0117] The server analyzes the generated text data using natural language processing techniques to understand the context and extract important information. The input is text data, and the output is the extracted important information.

[0118] Step 5:

[0119] The server performs speech re-transmission or generates images for visual display based on the extracted key information. In this step, a generative AI model may be used to create relevant image materials. The input is the extracted information, and the output is synthesized speech or generated image data.

[0120] Step 6:

[0121] The terminal receives synthesized speech or image data transmitted from the server and provides it to the user through a visual display device. The input is speech or image data, and the output is the presentation of information to the user.

[0122] Step 7:

[0123] The user receives the information and, if necessary, issues another voice command, starting a cycle of inputting data into the server again. This is part of the user's actions to ensure the smooth running of the entire process.

[0124] 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.

[0125] This invention is a system that not only converts audio data into text data, analyzes the text to extract important information, but also recognizes the user's emotions contained in the audio data and customizes the information output based on that. By incorporating an emotion engine, this system aims to enrich the user experience by enabling the provision of content that responds to the user's emotions.

[0126] When a user inputs voice into a device, the device captures the voice data and sends it to a server. The server not only converts the voice to text but also uses an emotion engine to extract emotional characteristics from the voice. Emotion recognition technology, used in conjunction with speech recognition algorithms, recognizes emotional states based on voice tone and speaking style.

[0127] Text data is analyzed through natural language processing to extract important information. During this process, sentiment data obtained from the sentiment engine is used to deepen contextual understanding in the text analysis. This allows for output that considers the user's emotions, rather than simply extracting information.

[0128] Furthermore, emotional information is taken into consideration when converting the extracted information back into speech or generating related images. The server delivers the generated speech or images in an appropriate tone and style according to the user's emotional state.

[0129] For example, in educational settings, if a learner expresses feelings of dissatisfaction or confusion, the system can provide supplementary explanations in a more approachable tone. In business settings, if a user is perceived as stressed, the system can intervene in a way that is appropriate to their emotions, such as suggesting relaxing content.

[0130] In this way, by integrating an emotion engine, the system goes beyond simple information conversion and enables the delivery of dynamic content that responds to the user's emotions.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The user uses a device to input voice. The device records this voice as digital data and prepares to send it to the server.

[0134] Step 2:

[0135] The terminal sends the recorded audio data to the server. It is important to use a communication protocol to securely transmit the data.

[0136] Step 3:

[0137] The server processes the received audio data through a speech recognition engine, converting it into text data. During this process, a noise filter is applied to improve the clarity of the speech.

[0138] Step 4:

[0139] The server passes the audio data to the emotion engine, which analyzes the emotional characteristics contained in the audio. It identifies emotions based on factors such as tone, speed, and volume of the voice.

[0140] Step 5:

[0141] The server analyzes the generated text data using natural language processing algorithms. It understands the context and extracts important information and key phrases.

[0142] Step 6:

[0143] The server takes into account the results from the emotion engine and determines the output format based on the extracted information. When converting text to speech, it applies an emotion-appropriate tone. When generating images, it considers emotion-based design.

[0144] Step 7:

[0145] The server generates information that corresponds to the user's emotions and sends it to the terminal. The transmitted data includes content that reflects those emotions.

[0146] Step 8:

[0147] The device provides the user with audio, text, or image data received from the server. This ensures that the information displayed is sensitive to the user's unconscious emotions.

[0148] (Example 2)

[0149] 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".

[0150] In the process of converting audio data into text data and analyzing the data to extract important information, conventional systems have struggled to generate and provide content that takes into account the user's emotional state. Furthermore, as the importance of providing content that responds to emotions increases, there is a need for a mechanism that provides dynamic and personalized output to enrich the user experience. In addition, it is necessary to ensure that users receive the information provided in the most optimal way based on their emotions.

[0151] 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.

[0152] In this invention, the server includes means for converting audio data into text data, means for analyzing the text data and audio data and identifying emotions based on the tone and speed of the voice, and means for dynamically generating content using a generative model from information extracted based on the emotion identification data. This makes it possible to provide information customized with a tone and style that corresponds to the user's emotions.

[0153] "Audio data" refers to information that represents sound in digital format, and involves the electronic recording and processing of acoustic signals.

[0154] "Text data" refers to strings of information written in natural language, which are character information that is recorded and processed electronically.

[0155] "Input device" refers to a device used to capture audio or other sounds, and includes recording devices and microphones.

[0156] A "communication device" is a device used to send and receive data, and it has a network interface and protocol conversion function.

[0157] A "central processing unit" is a key component of a computer system that receives, processes, and analyzes data.

[0158] "Means of conversion" refers to technical processes or devices used to change data from one format to another.

[0159] "Means of analysis" refers to techniques for analyzing data and extracting specific information or features.

[0160] "Emotion recognition" is a technology that determines a user's emotional state from voice and text data, taking into account factors such as tone and speed.

[0161] A "generative model" refers to an algorithm that automatically generates new content and information using machine learning or artificial intelligence.

[0162] "Methods for dynamically generating content" refer to technologies that adaptively generate new information in response to the user's emotions and circumstances.

[0163] A "server" refers to a computer system that processes and provides data in response to client requests.

[0164] This invention is a system that provides content that takes into account the user's emotions based on voice input. Specific embodiments for realizing this system are described below.

[0165] The user first inputs their voice using the terminal's input device. At this stage, the input device, including a microphone, is used to capture the voice with high accuracy. The captured voice data is immediately converted to a digital format and then transmitted to the central processing unit via a communication device. Secure protocols such as HTTPS are used for communication.

[0166] The server converts the received audio data into text data using a speech recognition engine (for example, industry-standard speech recognition software). The converted text data and the information associated with the audio data are then analyzed by an emotion analysis module to identify the user's emotional state. This analysis includes analyzing the tone, speed, and intonation of the voice. Emotion recognition technology can be utilized in this analysis.

[0167] Based on the analysis results, the server applies natural language processing technology to extract important information from the text data. Subsequently, it dynamically generates new content using a generative AI model. This process can utilize voice generation with tone and style adjusted according to the user's emotions (e.g., text-to-speech services) and image generation technology (e.g., technology that generates images from natural language).

[0168] The generated content is transmitted to the terminal via a communication device, and the terminal provides it to the user audiovisually. The user can listen to the generated audio through a speaker or visually view the generated images on a display. As a result, an emotionally sensitive user experience can be provided.

[0169] A concrete example is a scenario where, if a learner is confused during a lesson, the system generates supplementary explanations in a friendly tone. In this case, an example of a prompt might be: "Convert the following audio data to text, analyze its sentiment, and output it to the user based on the specific content. Adjust the tone of the audio or image output based on the sentiment recognition."

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] The user inputs voice into the terminal using an audio input device. The terminal converts the voice into a digital format and transmits it to the server via a communication device. The input data is an analog audio signal, and the output is digital audio data. This conversion process uses a high-precision microphone to minimize noise and capture clear audio.

[0173] Step 2:

[0174] The server converts received digital audio data into text data using a speech recognition engine. The input is digital audio data, and the output is text data in string format. In this process, the speech recognition algorithm analyzes acoustic features and converts them into strings. During this process, the speech recognition accuracy is optimized to generate grammatically correct sentences.

[0175] Step 3:

[0176] The server uses text data and speech features to identify the user's emotions using an emotion analysis module. The input is text data and speech features, and the output is digital information indicating the emotional state. In this step, the emotion analysis model evaluates the tone, speed, emphasis, etc., of the speech to identify the user's emotional state.

[0177] Step 4:

[0178] The server uses natural language processing techniques to analyze text data while considering sentiment information, and extracts important information. The input is text data including sentiment data, and the output is digital data containing the important information. This process utilizes syntactic analysis and semantic extraction techniques to accurately grasp the key points.

[0179] Step 5:

[0180] The server uses a generative AI model to generate new content based on emotional states and extracted information. Input consists of key information and emotional data, while output is customized audio and image data provided to the user. The generative AI generates output that matches the user's emotional state and style.

[0181] Step 6:

[0182] The server sends the generated content to the terminal. The terminal then plays it back to the user as audio or displays it as an image on the screen. The input is the generated content, and the output is the provision of auditory or visual information to the user. This process ensures that information is presented to the user in an appropriate manner while maintaining the quality of the audio and images.

[0183] (Application Example 2)

[0184] 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".

[0185] Modern speech recognition systems are limited to simply converting speech data into text, and are insufficient for providing content that takes user emotions into account. In particular, content delivery services need technology to provide appropriate content that responds to user emotions, thereby improving the user experience.

[0186] 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. In this invention, the server includes means for converting audio data into text data, means for analyzing the text data to extract important information and recognize emotions, and means for converting the text back into audio based on the extracted information and emotions. This makes it possible to provide content that responds to the user's emotions and to improve the user experience.

[0187] "Audio data" refers to digital information that contains recorded audio.

[0188] "Text data" refers to digital information expressed as character data.

[0189] "Analysis" is the process of analyzing data to understand its content and characteristics.

[0190] "Information extraction" means taking specific, important information from the entire dataset.

[0191] "Recognizing emotions" means analyzing voice and text to identify the emotional state an individual is exhibiting.

[0192] "Re-conversion" refers to the process of converting data that has already been converted into a different format.

[0193] "Visual elements" refer to graphics and images that are displayed to the user.

[0194] "To provide" means to transmit information or services to users.

[0195] "Social media" refers to a platform where users share content with each other via online platforms.

[0196] "Means" refers to the methods and techniques used to achieve an objective.

[0197] The system for realizing the present invention includes a terminal for handling voice data and a server for processing the data transmitted from the terminal. In this system, when a user inputs voice data into the terminal, the terminal captures this voice data and sends it to the server. The server uses a speech recognition algorithm to convert the transmitted voice data into text data. An example of software for speech recognition is the speech_recognition library.

[0198] The converted text data is analyzed using natural language processing techniques. This analysis involves understanding the grammar and context of the text and extracting important information. Simultaneously, by combining this with emotion recognition techniques, the user's emotions are extracted from the speech, and their characteristics are analyzed using an emotion engine. This emotion analysis utilizes emotion analysis models such as the transformers library.

[0199] Based on the information obtained from the analysis, the server either converts the text data back into speech or generates visual elements. The generated speech and visual elements are adjusted according to the user's emotions and delivered through social media. For example, if the system recognizes the user's emotion as "excited" while listening to a podcast, it has a function that automatically recommends related episodes that are likely to evoke similar excitement.

[0200] An example of a prompt is, "Analyze the sentiment in the text of the podcast episode and suggest an appropriate episode." This prompt provides guidance for the server to suggest the most suitable content based on the user's sentiment.

[0201] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0202] Step 1:

[0203] The device captures the user's voice input. At this point, the input is the user's voice data. The device uses the microphone to record the voice as digital data and prepares to send it to the server.

[0204] Step 2:

[0205] The server converts the received audio data into text data using a speech recognition algorithm. The input is audio data, and the output is text data. The speech_recognition library is used for this process, converting the audio into text information to prepare it for analysis.

[0206] Step 3:

[0207] The server analyzes the converted text data using natural language processing techniques to understand the context and extract important information. The input is text data, and the output is data containing important information. The techniques used in this process extract keywords and important phrases from the data.

[0208] Step 4:

[0209] The server simultaneously uses emotion recognition technology to recognize the emotions in the text obtained from the speech. The input is text data, and the output is data about emotional states. The emotion analysis model in the transformers library is responsible for identifying emotions from the text.

[0210] Step 5:

[0211] The server converts the extracted key information and sentiment data back into speech or generates visual elements. The input is key information and sentiment, and the output is processed speech or visual elements. A generative AI model is used to generate content with a tone and style that matches the user's sentiment.

[0212] Step 6:

[0213] The server delivers generated audio or visual elements to the user through social media. The input here is the processed audio or visual elements, and the output is content delivery as a user experience. This allows users to enjoy information and entertainment appropriate to their emotions.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] [Second Embodiment]

[0218] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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".

[0230] This invention is a system aimed at making information more accessible to a diverse range of users, including those with visual and hearing impairments. This system has the functionality to convert audio data into text data, analyze that text data to extract important information, and, if necessary, convert the text back into audio or generate related images. Specific implementations are described below.

[0231] First, the user inputs voice through their device. The voice is typically recorded via a microphone and sent directly to the server as digital data. The server then uses a speech recognition algorithm to convert the received voice data into text. This conversion process utilizes noise filtering technology to improve voice clarity, thereby increasing the accuracy of the converted text.

[0232] Next, the server applies natural language processing techniques to the converted text data to analyze its content. Specifically, it performs contextual understanding and extracts key phrases. Through this process, the system can identify important parts of the information contained and highlight information that is useful to the user.

[0233] Furthermore, based on the analysis results, the server selects the re-output format to suit the user's needs. This includes methods such as converting the extracted text information back into speech, as well as generating appropriate images from the text for visualization. The generated audio and image data are sent to the user's device for display or playback.

[0234] For example, when this system is applied to recording meetings, participants' voices are captured in real time and immediately recorded as text. This transcribed information is provided to participants in a way that highlights the important points and action items of the meeting. Participants with hearing impairments can follow the progress of the meeting by reading the text displayed in real time. In educational settings, it is also possible to use text and images generated from audio as part of multimedia teaching materials to support learners' understanding.

[0235] Thus, this system, which processes audio, text, and images in an integrated manner, will improve information accessibility in a variety of situations.

[0236] The following describes the processing flow.

[0237] Step 1:

[0238] The user inputs voice using the device. The voice is captured through the device's microphone and temporarily stored as digital data.

[0239] Step 2:

[0240] The terminal sends the captured audio data to the server. During this process, the data is converted to an appropriate format and transmitted via the communication line.

[0241] Step 3:

[0242] The server passes the audio data received from the terminal to speech recognition software, which converts the audio into text data. This conversion process applies a noise reduction filter to improve the clarity of the speech.

[0243] Step 4:

[0244] The server passes the obtained text data to a natural language processing algorithm for content analysis. This analysis understands the context from the text and extracts important information and key phrases.

[0245] Step 5:

[0246] Based on the analysis results, the server selects the appropriate output format. Specifically, this includes means of converting text back into speech and generating related images.

[0247] Step 6:

[0248] The generated audio or image data is sent from the server to the terminal. A secure communication protocol is used for transmission to ensure accurate data delivery.

[0249] Step 7:

[0250] The device provides the user with the received data. Audio data is played through the speaker, and image data and text data are displayed on the screen.

[0251] (Example 1)

[0252] 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."

[0253] Conventional information access systems have made it difficult for diverse users, including those with visual and hearing impairments, to efficiently acquire and utilize information. In particular, there were problems with the quality of information degrading due to noise contamination during the conversion of audio information and insufficient identification of important information during text analysis. Furthermore, it was difficult to output the acquired information in a format that met the individual needs of users, posing accessibility challenges.

[0254] 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.

[0255] In this invention, the server includes means for converting audio information into symbolic information, means for analyzing the symbolic information to identify important information, means for converting the symbols back into audio or generating related visual information based on the identified information, and means for selecting an output format according to the user's request based on the integrated information. This makes it possible to process diverse information centrally and provide information in the optimal format to meet the needs of each user while maintaining the quality of the information.

[0256] "Audio information" refers to data acquired as sound waves, such as human voices.

[0257] "Symbolic information" refers to digital data converted from audio information, and is usually represented in text format.

[0258] "Analysis" refers to the process of extracting useful information from data, and includes understanding the context of the data and identifying important information.

[0259] "Visual information" refers to information that has been converted into a visually understandable form, and is provided as data such as images and diagrams.

[0260] "Filtering" is the process of removing unwanted elements from data, and in this context, it refers to the operation of removing noise from an audio signal.

[0261] "Language analysis technology" refers to techniques used to analyze text and understand its grammar and meaning, and includes natural language processing technology.

[0262] "Integrated information" refers to data from different formats that have been combined and compiled into a single, comprehensive set of information.

[0263] "User" refers to an individual who uses this system to obtain or manipulate information.

[0264] "Output format" refers to the final form in which information is presented, and includes formats such as audio, text, and images.

[0265] This invention provides a system that makes information more accessible to a diverse range of users, including those with visual and hearing impairments. This system converts audio information into symbolic information, analyzes that symbolic information to extract important information, and can also convert it back to audio or generate related visual information as needed.

[0266] The user inputs voice through the device. The voice input is transmitted via a microphone, converted to digital format, and then sent to the server. During this process, the device captures the voice in real time, minimizing latency through edge computing technology. The server applies a speech recognition algorithm to the received voice information, converting it into symbolic information. A common speech recognition engine can be used here.

[0267] The server analyzes the acquired symbolic information using language analysis techniques. This analysis process utilizes pre-trained natural language processing techniques to understand the text context and extract key phrases. For example, it can highlight information important to the user and provide a re-output format tailored to the user's intent.

[0268] The server uses the information generated based on the analysis to produce audio and visual information. A general-purpose speech synthesis engine can be used for audio generation, and image generation technology can be utilized for visual information generation. Users receive the generated results through their devices and can view the information, either played back as audio or displayed on the screen.

[0269] As a concrete example, this system can be used to transcribe audio information from a meeting into text and provide it to meeting participants in a format that highlights the key points. In this scenario, participants with hearing impairments can read the text information in real time and follow the progress of the meeting.

[0270] Examples of prompts include, "Transcribe the meeting audio into text and highlight the key points," and "Generate relevant images from the audio data to provide visual supplementation."

[0271] In this way, this system integrates audio, symbolic, and visual information to provide information tailored to the individual needs of users.

[0272] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0273] Step 1:

[0274] The user inputs voice using a terminal. Voice input is typically performed through a microphone, and the analog voice signal is converted into digital data. The terminal sends this digital voice data to the server. At this stage, the input is the user's voice, and the output is the digital voice data sent to the server.

[0275] Step 2:

[0276] The server applies a speech recognition algorithm to the received digital audio data. Noise filtering techniques are used to remove background noise and improve speech clarity. Specifically, noise components are removed from the audio data to generate highly accurate text data. At this stage, the input is digital audio data, and the output is the converted text data.

[0277] Step 3:

[0278] The server uses language analysis techniques to parse the converted text data. This process involves understanding the context of the text and extracting key phrases. Specifically, it uses natural language processing techniques to analyze the syntax within the text and highlight important information. The input at this stage is the text data, and the output is a list of important information or the corrected text data.

[0279] Step 4:

[0280] The server determines the output format that best suits the user's needs based on the analysis results. Using a generative AI model, it converts the data back into audio or generates images as visual complements. At this stage, the input is the analysis results, and the output is the generated audio or image data.

[0281] Step 5:

[0282] The server transmits the generated voice and image data to the user's terminal. The terminal receives this data and provides it to the user in a form suitable for vision or hearing. Specifically, it plays back the voice through a speaker or displays the image on a display. The input at this stage is the generated content, and the output is the information that can be used as the user's experience.

[0283] (Application Example 1)

[0284] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0285] In a factory, it is required to quickly and accurately transmit and understand information. However, with the conventional methods of voice instructions and documents, it was difficult to fully convey information to workers with visual and hearing impairments. Also, due to the influence of background noise in voice instructions, the instruction content could become unclear. Therefore, there is a need to provide a system that allows workers to efficiently receive information and perform their work safely and reliably.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0287] In this invention, the server includes means for converting voice data into text data, means for analyzing the text data to extract important information, means for re-converting the text into voice or generating related images based on the extracted information, means for visually presenting the extracted information using a visual display device, and means for analyzing voice instructions input by the user and selecting appropriate process information. As a result, factory workers can efficiently receive work instructions and perform their work safely without visual and auditory constraints.

[0288] "Voice data" is information obtained by digitizing a voice signal and represents various sounds including human voices.

[0289] "Text data" refers to information composed of strings of characters, and is a data format used to describe sentences, commands, and other similar content.

[0290] A "visual display device" is a device, such as a display or screen, that displays text or images to appeal to human vision.

[0291] "Speech recognition" is a technology that analyzes speech data and converts it into text data.

[0292] "Natural language processing technology" refers to the technology that enables computers to understand, interpret, and generate human language, and is used for analyzing text data and understanding context.

[0293] "Noise filtering" is a technique that removes unwanted background noise and other sounds from audio data to improve speech clarity.

[0294] "Speech re-conversion" is the process of generating speech based on text data and then providing it as speech again.

[0295] "Information extraction" refers to the process of taking out necessary or important parts of data.

[0296] "Process information" refers to information that includes the procedures and instructions necessary to perform a specific task or operation.

[0297] This invention is a system for streamlining information exchange among workers in a factory. This system begins with speech recognition using voice data, then utilizes natural language processing technology to extract important information as text data, and finally provides this information through a visual display device.

[0298] First, the operator inputs voice commands into the system via an input device. The server converts this voice data into a digital signal and uses noise filtering technology to improve the clarity of the voice. Next, speech recognition software converts the voice into text data.

[0299] The server applies natural language processing techniques to the obtained text data to perform contextual understanding and precisely analyze important information and process details. Furthermore, based on the analyzed information, speech re-conversion is performed, or related images are generated, and the information is presented on a visual display device. As a result, the information displayed on the visual display device becomes an important factor for workers to make decisions that enable them to perform their tasks efficiently.

[0300] For example, if a worker wants to know the maintenance procedure for a conveyor belt, they can instruct the smart glasses by saying, "Tell me the procedure for the conveyor belt." The server analyzes this instruction and displays the relevant procedure information on the screen, and can play it back as audio if necessary. This allows workers to receive information through both sight and sound, and acquire the knowledge necessary for the job from multiple perspectives.

[0301] As an example of a prompt, we will use the instruction, "Please describe the inspection procedure for the conveyor belt. Please keep each step simple and highlight the points to pay attention to." This prompt will contribute to the generation of specific and concise work procedures through the generative AI model.

[0302] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0303] Step 1:

[0304] The user inputs voice commands through a terminal. The terminal converts this voice data into a digital signal and sends it to the server. The input is the user's voice, and the output is digital voice data.

[0305] Step 2:

[0306] The server performs noise filtering on the received digital audio data to improve speech clarity. The input is digital audio data, and the output is clear audio data with the noise removed.

[0307] Step 3:

[0308] The server applies the clear voice data to a voice recognition algorithm to convert it into text data. The input is the voice data with noise removed, and the output is the corresponding text data.

[0309] Step 4:

[0310] The server analyzes the generated text data using natural language processing technology to perform context understanding and extraction of important information. The input is the text data, and the output is the extracted important information.

[0311] Step 5:

[0312] Based on the extracted important information, the server performs voice re-conversion or image generation for visual display. In this step, a generation AI model may be used to create related image materials. The input is the extracted information, and the output is the synthesized voice or the generated image data.

[0313] Step 6:

[0314] The terminal receives the synthesized voice or image data sent from the server and provides it to the user through a visual display device. The input is the voice or image data, and the output is the information presentation to the user.

[0315] Step 7:

[0316] The user receives the information and, if necessary, issues the next voice instruction again, starting a cycle of inputting data to the server again. This is part of the user's actions to smoothly progress the entire process.

[0317] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0318] This invention is a system that not only converts audio data into text data, analyzes the text to extract important information, but also recognizes the user's emotions contained in the audio data and customizes the information output based on that. By incorporating an emotion engine, this system aims to enrich the user experience by enabling the provision of content that responds to the user's emotions.

[0319] When a user inputs voice into a device, the device captures the voice data and sends it to a server. The server not only converts the voice to text but also uses an emotion engine to extract emotional characteristics from the voice. Emotion recognition technology, used in conjunction with speech recognition algorithms, recognizes emotional states based on voice tone and speaking style.

[0320] Text data is analyzed through natural language processing to extract important information. During this process, sentiment data obtained from the sentiment engine is used to deepen contextual understanding in the text analysis. This allows for output that considers the user's emotions, rather than simply extracting information.

[0321] Furthermore, emotional information is taken into consideration when converting the extracted information back into speech or generating related images. The server delivers the generated speech or images in an appropriate tone and style according to the user's emotional state.

[0322] For example, in educational settings, if a learner expresses feelings of dissatisfaction or confusion, the system can provide supplementary explanations in a more approachable tone. In business settings, if a user is perceived as stressed, the system can intervene in a way that is appropriate to their emotions, such as suggesting relaxing content.

[0323] In this way, by integrating an emotion engine, the system goes beyond simple information conversion and enables the delivery of dynamic content that responds to the user's emotions.

[0324] The following describes the processing flow.

[0325] Step 1:

[0326] The user uses a device to input voice. The device records this voice as digital data and prepares to send it to the server.

[0327] Step 2:

[0328] The terminal sends the recorded audio data to the server. It is important to use a communication protocol to securely transmit the data.

[0329] Step 3:

[0330] The server processes the received audio data through a speech recognition engine, converting it into text data. During this process, a noise filter is applied to improve the clarity of the speech.

[0331] Step 4:

[0332] The server passes the audio data to the emotion engine, which analyzes the emotional characteristics contained in the audio. It identifies emotions based on factors such as tone, speed, and volume of the voice.

[0333] Step 5:

[0334] The server analyzes the generated text data using natural language processing algorithms. It understands the context and extracts important information and key phrases.

[0335] Step 6:

[0336] The server takes into account the results from the emotion engine and determines the output format based on the extracted information. When converting text to speech, it applies an emotion-appropriate tone. When generating images, it considers emotion-based design.

[0337] Step 7:

[0338] The server generates information that corresponds to the user's emotions and sends it to the terminal. The transmitted data includes content that reflects those emotions.

[0339] Step 8:

[0340] The device provides the user with audio, text, or image data received from the server. This ensures that the information displayed is sensitive to the user's unconscious emotions.

[0341] (Example 2)

[0342] 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".

[0343] In the process of converting audio data into text data and analyzing the data to extract important information, conventional systems have struggled to generate and provide content that takes into account the user's emotional state. Furthermore, as the importance of providing content that responds to emotions increases, there is a need for a mechanism that provides dynamic and personalized output to enrich the user experience. In addition, it is necessary to ensure that users receive the information provided in the most optimal way based on their emotions.

[0344] 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.

[0345] In this invention, the server includes means for converting audio data into text data, means for analyzing the text data and audio data and identifying emotions based on the tone and speed of the voice, and means for dynamically generating content using a generative model from information extracted based on the emotion identification data. This makes it possible to provide information customized with a tone and style that corresponds to the user's emotions.

[0346] "Audio data" refers to information that represents sound in digital format, and involves the electronic recording and processing of acoustic signals.

[0347] "Text data" refers to strings of information written in natural language, which are character information that is recorded and processed electronically.

[0348] "Input device" refers to a device used to capture audio or other sounds, and includes recording devices and microphones.

[0349] A "communication device" is a device used to send and receive data, and it has a network interface and protocol conversion function.

[0350] A "central processing unit" is a key component of a computer system that receives, processes, and analyzes data.

[0351] "Means of conversion" refers to technical processes or devices used to change data from one format to another.

[0352] "Means of analysis" refers to techniques for analyzing data and extracting specific information or features.

[0353] "Emotion recognition" is a technology that determines a user's emotional state from voice and text data, taking into account factors such as tone and speed.

[0354] A "generative model" refers to an algorithm that automatically generates new content and information using machine learning or artificial intelligence.

[0355] "Methods for dynamically generating content" refer to technologies that adaptively generate new information in response to the user's emotions and circumstances.

[0356] A "server" refers to a computer system that processes and provides data in response to client requests.

[0357] This invention is a system that provides content that takes into account the user's emotions based on voice input. Specific embodiments for realizing this system are described below.

[0358] The user first inputs their voice using the terminal's input device. At this stage, the input device, including a microphone, is used to capture the voice with high accuracy. The captured voice data is immediately converted to a digital format and then transmitted to the central processing unit via a communication device. Secure protocols such as HTTPS are used for communication.

[0359] The server converts the received audio data into text data using a speech recognition engine (for example, industry-standard speech recognition software). The converted text data and the information associated with the audio data are then analyzed by an emotion analysis module to identify the user's emotional state. This analysis includes analyzing the tone, speed, and intonation of the voice. Emotion recognition technology can be utilized in this analysis.

[0360] Based on the analysis results, the server applies natural language processing technology to extract important information from the text data. Subsequently, it dynamically generates new content using a generative AI model. This process can utilize voice generation with tone and style adjusted according to the user's emotions (e.g., text-to-speech services) and image generation technology (e.g., technology that generates images from natural language).

[0361] The generated content is transmitted to the terminal via a communication device, and the terminal provides it to the user audiovisually. The user can listen to the generated audio through a speaker or visually view the generated images on a display. As a result, an emotionally sensitive user experience can be provided.

[0362] A concrete example is a scenario where, if a learner is confused during a lesson, the system generates supplementary explanations in a friendly tone. In this case, an example of a prompt might be: "Convert the following audio data to text, analyze its sentiment, and output it to the user based on the specific content. Adjust the tone of the audio or image output based on the sentiment recognition."

[0363] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0364] Step 1:

[0365] The user inputs voice into the terminal using an audio input device. The terminal converts the voice into a digital format and transmits it to the server via a communication device. The input data is an analog audio signal, and the output is digital audio data. This conversion process uses a high-precision microphone to minimize noise and capture clear audio.

[0366] Step 2:

[0367] The server converts received digital audio data into text data using a speech recognition engine. The input is digital audio data, and the output is text data in string format. In this process, the speech recognition algorithm analyzes acoustic features and converts them into strings. During this process, the speech recognition accuracy is optimized to generate grammatically correct sentences.

[0368] Step 3:

[0369] The server uses text data and speech features to identify the user's emotions using an emotion analysis module. The input is text data and speech features, and the output is digital information indicating the emotional state. In this step, the emotion analysis model evaluates the tone, speed, emphasis, etc., of the speech to identify the user's emotional state.

[0370] Step 4:

[0371] The server uses natural language processing techniques to analyze text data while considering sentiment information, and extracts important information. The input is text data including sentiment data, and the output is digital data containing the important information. This process utilizes syntactic analysis and semantic extraction techniques to accurately grasp the key points.

[0372] Step 5:

[0373] The server uses a generative AI model to generate new content based on emotional states and extracted information. Input consists of key information and emotional data, while output is customized audio and image data provided to the user. The generative AI generates output that matches the user's emotional state and style.

[0374] Step 6:

[0375] The server sends the generated content to the terminal. The terminal then plays it back to the user as audio or displays it as an image on the screen. The input is the generated content, and the output is the provision of auditory or visual information to the user. This process ensures that information is presented to the user in an appropriate manner while maintaining the quality of the audio and images.

[0376] (Application Example 2)

[0377] 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."

[0378] Modern speech recognition systems are limited to simply converting speech data into text, and are insufficient for providing content that takes user emotions into account. In particular, content delivery services need technology to provide appropriate content that responds to user emotions, thereby improving the user experience.

[0379] 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. In this invention, the server includes means for converting audio data into text data, means for analyzing the text data to extract important information and recognize emotions, and means for converting the text back into audio based on the extracted information and emotions. This makes it possible to provide content that responds to the user's emotions and to improve the user experience.

[0380] "Audio data" refers to digital information that contains recorded audio.

[0381] "Text data" refers to digital information expressed as character data.

[0382] "Analysis" is the process of analyzing data to understand its content and characteristics.

[0383] "Information extraction" means taking specific, important information from the entire dataset.

[0384] "Recognizing emotions" means analyzing voice and text to identify the emotional state an individual is exhibiting.

[0385] "Re-conversion" refers to the process of converting data that has already been converted into a different format.

[0386] "Visual elements" refer to graphics and images that are displayed to the user.

[0387] "To provide" means to transmit information or services to users.

[0388] "Social media" refers to a platform where users share content with each other via online platforms.

[0389] "Means" refers to the methods and techniques used to achieve an objective.

[0390] The system for realizing the present invention includes a terminal for handling voice data and a server for processing the data transmitted from the terminal. In this system, when a user inputs voice data into the terminal, the terminal captures this voice data and sends it to the server. The server uses a speech recognition algorithm to convert the transmitted voice data into text data. An example of software for speech recognition is the speech_recognition library.

[0391] The converted text data is analyzed using natural language processing techniques. This analysis involves understanding the grammar and context of the text and extracting important information. Simultaneously, by combining this with emotion recognition techniques, the user's emotions are extracted from the speech, and their characteristics are analyzed using an emotion engine. This emotion analysis utilizes emotion analysis models such as the transformers library.

[0392] Based on the information obtained from the analysis, the server either converts the text data back into speech or generates visual elements. The generated speech and visual elements are adjusted according to the user's emotions and delivered through social media. For example, if the system recognizes the user's emotion as "excited" while listening to a podcast, it has a function that automatically recommends related episodes that are likely to evoke similar excitement.

[0393] An example of a prompt is, "Analyze the sentiment in the text of the podcast episode and suggest an appropriate episode." This prompt provides guidance for the server to suggest the most suitable content based on the user's sentiment.

[0394] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0395] Step 1:

[0396] The device captures the user's voice input. At this point, the input is the user's voice data. The device uses the microphone to record the voice as digital data and prepares to send it to the server.

[0397] Step 2:

[0398] The server converts the received audio data into text data using a speech recognition algorithm. The input is audio data, and the output is text data. The speech_recognition library is used for this process, converting the audio into text information to prepare it for analysis.

[0399] Step 3:

[0400] The server analyzes the converted text data using natural language processing techniques to understand the context and extract important information. The input is text data, and the output is data containing important information. The techniques used in this process extract keywords and important phrases from the data.

[0401] Step 4:

[0402] The server simultaneously uses emotion recognition technology to recognize the emotions in the text obtained from the speech. The input is text data, and the output is data about emotional states. The emotion analysis model in the transformers library is responsible for identifying emotions from the text.

[0403] Step 5:

[0404] The server converts the extracted key information and sentiment data back into speech or generates visual elements. The input is key information and sentiment, and the output is processed speech or visual elements. A generative AI model is used to generate content with a tone and style that matches the user's sentiment.

[0405] Step 6:

[0406] The server delivers generated audio or visual elements to the user through social media. The input here is the processed audio or visual elements, and the output is content delivery as a user experience. This allows users to enjoy information and entertainment appropriate to their emotions.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] [Third Embodiment]

[0411] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0412] 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.

[0413] 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).

[0414] 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.

[0415] 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.

[0416] 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).

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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".

[0423] This invention is a system aimed at making information more accessible to a diverse range of users, including those with visual and hearing impairments. This system has the functionality to convert audio data into text data, analyze that text data to extract important information, and, if necessary, convert the text back into audio or generate related images. Specific implementations are described below.

[0424] First, the user inputs voice through their device. The voice is typically recorded via a microphone and sent directly to the server as digital data. The server then uses a speech recognition algorithm to convert the received voice data into text. This conversion process utilizes noise filtering technology to improve voice clarity, thereby increasing the accuracy of the converted text.

[0425] Next, the server applies natural language processing techniques to the converted text data to analyze its content. Specifically, it performs contextual understanding and extracts key phrases. Through this process, the system can identify important parts of the information contained and highlight information that is useful to the user.

[0426] Furthermore, based on the analysis results, the server selects the re-output format to suit the user's needs. This includes methods such as converting the extracted text information back into speech, as well as generating appropriate images from the text for visualization. The generated audio and image data are sent to the user's device for display or playback.

[0427] For example, when this system is applied to recording meetings, participants' voices are captured in real time and immediately recorded as text. This transcribed information is provided to participants in a way that highlights the important points and action items of the meeting. Participants with hearing impairments can follow the progress of the meeting by reading the text displayed in real time. In educational settings, it is also possible to use text and images generated from audio as part of multimedia teaching materials to support learners' understanding.

[0428] Thus, this system, which processes audio, text, and images in an integrated manner, will improve information accessibility in a variety of situations.

[0429] The following describes the processing flow.

[0430] Step 1:

[0431] The user inputs voice using the device. The voice is captured through the device's microphone and temporarily stored as digital data.

[0432] Step 2:

[0433] The terminal sends the captured audio data to the server. During this process, the data is converted to an appropriate format and transmitted via the communication line.

[0434] Step 3:

[0435] The server passes the audio data received from the terminal to speech recognition software, which converts the audio into text data. This conversion process applies a noise reduction filter to improve the clarity of the speech.

[0436] Step 4:

[0437] The server passes the obtained text data to a natural language processing algorithm for content analysis. This analysis understands the context from the text and extracts important information and key phrases.

[0438] Step 5:

[0439] Based on the analysis results, the server selects the appropriate output format. Specifically, this includes means of converting text back into speech and generating related images.

[0440] Step 6:

[0441] The generated audio or image data is sent from the server to the terminal. A secure communication protocol is used for transmission to ensure accurate data delivery.

[0442] Step 7:

[0443] The device provides the user with the received data. Audio data is played through the speaker, and image data and text data are displayed on the screen.

[0444] (Example 1)

[0445] 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."

[0446] Conventional information access systems have made it difficult for diverse users, including those with visual and hearing impairments, to efficiently acquire and utilize information. In particular, there were problems with the quality of information degrading due to noise contamination during the conversion of audio information and insufficient identification of important information during text analysis. Furthermore, it was difficult to output the acquired information in a format that met the individual needs of users, posing accessibility challenges.

[0447] 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.

[0448] In this invention, the server includes means for converting audio information into symbolic information, means for analyzing the symbolic information to identify important information, means for converting the symbols back into audio or generating related visual information based on the identified information, and means for selecting an output format according to the user's request based on the integrated information. This makes it possible to process diverse information centrally and provide information in the optimal format to meet the needs of each user while maintaining the quality of the information.

[0449] "Audio information" refers to data acquired as sound waves, such as human voices.

[0450] "Symbolic information" refers to digital data converted from audio information, and is usually represented in text format.

[0451] "Analysis" refers to the process of extracting useful information from data, and includes understanding the context of the data and identifying important information.

[0452] "Visual information" refers to information that has been converted into a visually understandable form, and is provided as data such as images and diagrams.

[0453] "Filtering" is the process of removing unwanted elements from data, and in this context, it refers to the operation of removing noise from an audio signal.

[0454] "Language analysis technology" refers to techniques used to analyze text and understand its grammar and meaning, and includes natural language processing technology.

[0455] "Integrated information" refers to data from different formats that have been combined and compiled into a single, comprehensive set of information.

[0456] "User" refers to an individual who uses this system to obtain or manipulate information.

[0457] "Output format" refers to the final form in which information is presented, and includes formats such as audio, text, and images.

[0458] This invention provides a system that makes information more accessible to a diverse range of users, including those with visual and hearing impairments. This system converts audio information into symbolic information, analyzes that symbolic information to extract important information, and can also convert it back to audio or generate related visual information as needed.

[0459] The user inputs voice through the device. The voice input is transmitted via a microphone, converted to digital format, and then sent to the server. During this process, the device captures the voice in real time, minimizing latency through edge computing technology. The server applies a speech recognition algorithm to the received voice information, converting it into symbolic information. A common speech recognition engine can be used here.

[0460] The server analyzes the acquired symbolic information using language analysis techniques. This analysis process utilizes pre-trained natural language processing techniques to understand the text context and extract key phrases. For example, it can highlight information important to the user and provide a re-output format tailored to the user's intent.

[0461] The server uses the information generated based on the analysis to produce audio and visual information. A general-purpose speech synthesis engine can be used for audio generation, and image generation technology can be utilized for visual information generation. Users receive the generated results through their devices and can view the information, either played back as audio or displayed on the screen.

[0462] As a concrete example, this system can be used to transcribe audio information from a meeting into text and provide it to meeting participants in a format that highlights the key points. In this scenario, participants with hearing impairments can read the text information in real time and follow the progress of the meeting.

[0463] Examples of prompts include, "Transcribe the meeting audio into text and highlight the key points," and "Generate relevant images from the audio data to provide visual supplementation."

[0464] In this way, this system integrates audio, symbolic, and visual information to provide information tailored to the individual needs of users.

[0465] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0466] Step 1:

[0467] The user inputs voice using a terminal. Voice input is typically performed through a microphone, and the analog voice signal is converted into digital data. The terminal sends this digital voice data to the server. At this stage, the input is the user's voice, and the output is the digital voice data sent to the server.

[0468] Step 2:

[0469] The server applies a speech recognition algorithm to the received digital audio data. Noise filtering techniques are used to remove background noise and improve speech clarity. Specifically, noise components are removed from the audio data to generate highly accurate text data. At this stage, the input is digital audio data, and the output is the converted text data.

[0470] Step 3:

[0471] The server uses language analysis techniques to parse the converted text data. This process involves understanding the context of the text and extracting key phrases. Specifically, it uses natural language processing techniques to analyze the syntax within the text and highlight important information. The input at this stage is the text data, and the output is a list of important information or the corrected text data.

[0472] Step 4:

[0473] The server determines the output format that best suits the user's needs based on the analysis results. Using a generative AI model, it converts the data back into audio or generates images as visual complements. At this stage, the input is the analysis results, and the output is the generated audio or image data.

[0474] Step 5:

[0475] The server sends the generated audio and image data to the user's device. The device receives this data and provides it to the user in a format suitable for visual or auditory use. Specifically, it may play audio through a speaker or display images on a screen. At this stage, the input is the generated content, and the output is usable information for the user's experience.

[0476] (Application Example 1)

[0477] 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."

[0478] In factories, rapid and accurate information transmission and understanding are essential. However, traditional methods such as voice instructions and written documents have made it difficult to adequately convey information to workers with visual and hearing impairments. Furthermore, background noise in voice instructions can sometimes obscure the content. Therefore, there is a need for a system that allows workers to efficiently receive information and perform tasks safely and reliably.

[0479] 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.

[0480] In this invention, the server includes means for converting audio data into text data, means for analyzing the text data to extract important information, means for converting the text back into audio or generating related images based on the extracted information, means for visually presenting the extracted information using a visual display device, and means for analyzing audio instructions entered by the user and selecting appropriate process information. This enables factory workers to efficiently receive work instructions and safely perform their work without visual or auditory limitations.

[0481] "Audio data" refers to information obtained by digitizing audio signals, and it represents various sounds, including human voices.

[0482] "Text data" refers to information composed of strings of characters, and is a data format used to describe sentences, commands, and other similar content.

[0483] A "visual display device" is a device, such as a display or screen, that displays text or images to appeal to human vision.

[0484] "Speech recognition" is a technology that analyzes speech data and converts it into text data.

[0485] "Natural language processing technology" refers to the technology that enables computers to understand, interpret, and generate human language, and is used for analyzing text data and understanding context.

[0486] "Noise filtering" is a technique that removes unwanted background noise and other sounds from audio data to improve speech clarity.

[0487] "Speech re-conversion" is the process of generating speech based on text data and then providing it as speech again.

[0488] "Information extraction" refers to the process of taking out necessary or important parts of data.

[0489] "Process information" refers to information that includes the procedures and instructions necessary to perform a specific task or operation.

[0490] This invention is a system for streamlining information exchange among workers in a factory. This system begins with speech recognition using voice data, then utilizes natural language processing technology to extract important information as text data, and finally provides this information through a visual display device.

[0491] First, the operator inputs voice commands into the system via an input device. The server converts this voice data into a digital signal and uses noise filtering technology to improve the clarity of the voice. Next, speech recognition software converts the voice into text data.

[0492] The server applies natural language processing techniques to the obtained text data to perform contextual understanding and precisely analyze important information and process details. Furthermore, based on the analyzed information, speech re-conversion is performed, or related images are generated, and the information is presented on a visual display device. As a result, the information displayed on the visual display device becomes an important factor for workers to make decisions that enable them to perform their tasks efficiently.

[0493] For example, if a worker wants to know the maintenance procedure for a conveyor belt, they can instruct the smart glasses by saying, "Tell me the procedure for the conveyor belt." The server analyzes this instruction and displays the relevant procedure information on the screen, and can play it back as audio if necessary. This allows workers to receive information through both sight and sound, and acquire the knowledge necessary for the job from multiple perspectives.

[0494] As an example of a prompt, we will use the instruction, "Please describe the inspection procedure for the conveyor belt. Please keep each step simple and highlight the points to pay attention to." This prompt will contribute to the generation of specific and concise work procedures through the generative AI model.

[0495] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0496] Step 1:

[0497] The user inputs voice commands through a terminal. The terminal converts this voice data into a digital signal and sends it to the server. The input is the user's voice, and the output is digital voice data.

[0498] Step 2:

[0499] The server performs noise filtering on the received digital audio data to improve speech clarity. The input is digital audio data, and the output is clear audio data with the noise removed.

[0500] Step 3:

[0501] The server processes clear audio data using a speech recognition algorithm to convert it into text data. The input is noise-removed audio data, and the output is the corresponding text data.

[0502] Step 4:

[0503] The server analyzes the generated text data using natural language processing techniques to understand the context and extract important information. The input is text data, and the output is the extracted important information.

[0504] Step 5:

[0505] The server performs speech re-transmission or generates images for visual display based on the extracted key information. In this step, a generative AI model may be used to create relevant image materials. The input is the extracted information, and the output is synthesized speech or generated image data.

[0506] Step 6:

[0507] The terminal receives synthesized speech or image data transmitted from the server and provides it to the user through a visual display device. The input is speech or image data, and the output is the presentation of information to the user.

[0508] Step 7:

[0509] The user receives the information and, if necessary, issues another voice command, starting a cycle of inputting data into the server again. This is part of the user's actions to ensure the smooth running of the entire process.

[0510] 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.

[0511] This invention is a system that not only converts audio data into text data, analyzes the text to extract important information, but also recognizes the user's emotions contained in the audio data and customizes the information output based on that. By incorporating an emotion engine, this system aims to enrich the user experience by enabling the provision of content that responds to the user's emotions.

[0512] When a user inputs voice into a device, the device captures the voice data and sends it to a server. The server not only converts the voice to text but also uses an emotion engine to extract emotional characteristics from the voice. Emotion recognition technology, used in conjunction with speech recognition algorithms, recognizes emotional states based on voice tone and speaking style.

[0513] Text data is analyzed through natural language processing to extract important information. During this process, sentiment data obtained from the sentiment engine is used to deepen contextual understanding in the text analysis. This allows for output that considers the user's emotions, rather than simply extracting information.

[0514] Furthermore, emotional information is taken into consideration when converting the extracted information back into speech or generating related images. The server delivers the generated speech or images in an appropriate tone and style according to the user's emotional state.

[0515] For example, in educational settings, if a learner expresses feelings of dissatisfaction or confusion, the system can provide supplementary explanations in a more approachable tone. In business settings, if a user is perceived as stressed, the system can intervene in a way that is appropriate to their emotions, such as suggesting relaxing content.

[0516] In this way, by integrating an emotion engine, the system goes beyond simple information conversion and enables the delivery of dynamic content that responds to the user's emotions.

[0517] The following describes the processing flow.

[0518] Step 1:

[0519] The user uses a device to input voice. The device records this voice as digital data and prepares to send it to the server.

[0520] Step 2:

[0521] The terminal sends the recorded audio data to the server. It is important to use a communication protocol to securely transmit the data.

[0522] Step 3:

[0523] The server processes the received audio data through a speech recognition engine, converting it into text data. During this process, a noise filter is applied to improve the clarity of the speech.

[0524] Step 4:

[0525] The server passes the audio data to the emotion engine, which analyzes the emotional characteristics contained in the audio. It identifies emotions based on factors such as tone, speed, and volume of the voice.

[0526] Step 5:

[0527] The server analyzes the generated text data using natural language processing algorithms. It understands the context and extracts important information and key phrases.

[0528] Step 6:

[0529] The server takes into account the results from the emotion engine and determines the output format based on the extracted information. When converting text to speech, it applies an emotion-appropriate tone. When generating images, it considers emotion-based design.

[0530] Step 7:

[0531] The server generates information that corresponds to the user's emotions and sends it to the terminal. The transmitted data includes content that reflects those emotions.

[0532] Step 8:

[0533] The device provides the user with audio, text, or image data received from the server. This ensures that the information displayed is sensitive to the user's unconscious emotions.

[0534] (Example 2)

[0535] 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."

[0536] In the process of converting audio data into text data and analyzing the data to extract important information, conventional systems have struggled to generate and provide content that takes into account the user's emotional state. Furthermore, as the importance of providing content that responds to emotions increases, there is a need for a mechanism that provides dynamic and personalized output to enrich the user experience. In addition, it is necessary to ensure that users receive the information provided in the most optimal way based on their emotions.

[0537] 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.

[0538] In this invention, the server includes means for converting audio data into text data, means for analyzing the text data and audio data and identifying emotions based on the tone and speed of the voice, and means for dynamically generating content using a generative model from information extracted based on the emotion identification data. This makes it possible to provide information customized with a tone and style that corresponds to the user's emotions.

[0539] "Audio data" refers to information that represents sound in digital format, and involves the electronic recording and processing of acoustic signals.

[0540] "Text data" refers to strings of information written in natural language, which are character information that is recorded and processed electronically.

[0541] "Input device" refers to a device used to capture audio or other sounds, and includes recording devices and microphones.

[0542] A "communication device" is a device used to send and receive data, and it has a network interface and protocol conversion function.

[0543] A "central processing unit" is a key component of a computer system that receives, processes, and analyzes data.

[0544] "Means of conversion" refers to technical processes or devices used to change data from one format to another.

[0545] "Means of analysis" refers to techniques for analyzing data and extracting specific information or features.

[0546] "Emotion recognition" is a technology that determines a user's emotional state from voice and text data, taking into account factors such as tone and speed.

[0547] A "generative model" refers to an algorithm that automatically generates new content and information using machine learning or artificial intelligence.

[0548] "Methods for dynamically generating content" refer to technologies that adaptively generate new information in response to the user's emotions and circumstances.

[0549] A "server" refers to a computer system that processes and provides data in response to client requests.

[0550] This invention is a system that provides content that takes into account the user's emotions based on voice input. Specific embodiments for realizing this system are described below.

[0551] The user first inputs their voice using the terminal's input device. At this stage, the input device, including a microphone, is used to capture the voice with high accuracy. The captured voice data is immediately converted to a digital format and then transmitted to the central processing unit via a communication device. Secure protocols such as HTTPS are used for communication.

[0552] The server converts the received audio data into text data using a speech recognition engine (for example, industry-standard speech recognition software). The converted text data and the information associated with the audio data are then analyzed by an emotion analysis module to identify the user's emotional state. This analysis includes analyzing the tone, speed, and intonation of the voice. Emotion recognition technology can be utilized in this analysis.

[0553] Based on the analysis results, the server applies natural language processing technology to extract important information from the text data. Subsequently, it dynamically generates new content using a generative AI model. This process can utilize voice generation with tone and style adjusted according to the user's emotions (e.g., text-to-speech services) and image generation technology (e.g., technology that generates images from natural language).

[0554] The generated content is transmitted to the terminal via a communication device, and the terminal provides it to the user audiovisually. The user can listen to the generated audio through a speaker or visually view the generated images on a display. As a result, an emotionally sensitive user experience can be provided.

[0555] A concrete example is a scenario where, if a learner is confused during a lesson, the system generates supplementary explanations in a friendly tone. In this case, an example of a prompt might be: "Convert the following audio data to text, analyze its sentiment, and output it to the user based on the specific content. Adjust the tone of the audio or image output based on the sentiment recognition."

[0556] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0557] Step 1:

[0558] The user inputs voice into the terminal using an audio input device. The terminal converts the voice into a digital format and transmits it to the server via a communication device. The input data is an analog audio signal, and the output is digital audio data. This conversion process uses a high-precision microphone to minimize noise and capture clear audio.

[0559] Step 2:

[0560] The server converts received digital audio data into text data using a speech recognition engine. The input is digital audio data, and the output is text data in string format. In this process, the speech recognition algorithm analyzes acoustic features and converts them into strings. During this process, the speech recognition accuracy is optimized to generate grammatically correct sentences.

[0561] Step 3:

[0562] The server uses text data and speech features to identify the user's emotions using an emotion analysis module. The input is text data and speech features, and the output is digital information indicating the emotional state. In this step, the emotion analysis model evaluates the tone, speed, emphasis, etc., of the speech to identify the user's emotional state.

[0563] Step 4:

[0564] The server uses natural language processing techniques to analyze text data while considering sentiment information, and extracts important information. The input is text data including sentiment data, and the output is digital data containing the important information. This process utilizes syntactic analysis and semantic extraction techniques to accurately grasp the key points.

[0565] Step 5:

[0566] The server uses a generative AI model to generate new content based on emotional states and extracted information. Input consists of key information and emotional data, while output is customized audio and image data provided to the user. The generative AI generates output that matches the user's emotional state and style.

[0567] Step 6:

[0568] The server sends the generated content to the terminal. The terminal then plays it back to the user as audio or displays it as an image on the screen. The input is the generated content, and the output is the provision of auditory or visual information to the user. This process ensures that information is presented to the user in an appropriate manner while maintaining the quality of the audio and images.

[0569] (Application Example 2)

[0570] 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."

[0571] Modern speech recognition systems are limited to simply converting speech data into text, and are insufficient for providing content that takes user emotions into account. In particular, content delivery services need technology to provide appropriate content that responds to user emotions, thereby improving the user experience.

[0572] 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. In this invention, the server includes means for converting audio data into text data, means for analyzing the text data to extract important information and recognize emotions, and means for converting the text back into audio based on the extracted information and emotions. This makes it possible to provide content that responds to the user's emotions and to improve the user experience.

[0573] "Audio data" refers to digital information that contains recorded audio.

[0574] "Text data" refers to digital information expressed as character data.

[0575] "Analysis" is the process of analyzing data to understand its content and characteristics.

[0576] "Information extraction" means taking specific, important information from the entire dataset.

[0577] "Recognizing emotions" means analyzing voice and text to identify the emotional state an individual is exhibiting.

[0578] "Re-conversion" refers to the process of converting data that has already been converted into a different format.

[0579] "Visual elements" refer to graphics and images that are displayed to the user.

[0580] "To provide" means to transmit information or services to users.

[0581] "Social media" refers to a platform where users share content with each other via online platforms.

[0582] "Means" refers to the methods and techniques used to achieve an objective.

[0583] The system for realizing the present invention includes a terminal for handling voice data and a server for processing the data transmitted from the terminal. In this system, when a user inputs voice data into the terminal, the terminal captures this voice data and sends it to the server. The server uses a speech recognition algorithm to convert the transmitted voice data into text data. An example of software for speech recognition is the speech_recognition library.

[0584] The converted text data is analyzed using natural language processing techniques. This analysis involves understanding the grammar and context of the text and extracting important information. Simultaneously, by combining this with emotion recognition techniques, the user's emotions are extracted from the speech, and their characteristics are analyzed using an emotion engine. This emotion analysis utilizes emotion analysis models such as the transformers library.

[0585] Based on the information obtained from the analysis, the server either converts the text data back into speech or generates visual elements. The generated speech and visual elements are adjusted according to the user's emotions and delivered through social media. For example, if the system recognizes the user's emotion as "excited" while listening to a podcast, it has a function that automatically recommends related episodes that are likely to evoke similar excitement.

[0586] An example of a prompt is, "Analyze the sentiment in the text of the podcast episode and suggest an appropriate episode." This prompt provides guidance for the server to suggest the most suitable content based on the user's sentiment.

[0587] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0588] Step 1:

[0589] The device captures the user's voice input. At this point, the input is the user's voice data. The device uses the microphone to record the voice as digital data and prepares to send it to the server.

[0590] Step 2:

[0591] The server converts the received audio data into text data using a speech recognition algorithm. The input is audio data, and the output is text data. The speech_recognition library is used for this process, converting the audio into text information to prepare it for analysis.

[0592] Step 3:

[0593] The server analyzes the converted text data using natural language processing techniques to understand the context and extract important information. The input is text data, and the output is data containing important information. The techniques used in this process extract keywords and important phrases from the data.

[0594] Step 4:

[0595] The server simultaneously uses emotion recognition technology to recognize the emotions in the text obtained from the speech. The input is text data, and the output is data about emotional states. The emotion analysis model in the transformers library is responsible for identifying emotions from the text.

[0596] Step 5:

[0597] The server converts the extracted key information and sentiment data back into speech or generates visual elements. The input is key information and sentiment, and the output is processed speech or visual elements. A generative AI model is used to generate content with a tone and style that matches the user's sentiment.

[0598] Step 6:

[0599] The server delivers generated audio or visual elements to the user through social media. The input here is the processed audio or visual elements, and the output is content delivery as a user experience. This allows users to enjoy information and entertainment appropriate to their emotions.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] [Fourth Embodiment]

[0604] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0605] 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.

[0606] 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).

[0607] 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.

[0608] 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.

[0609] 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).

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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".

[0617] This invention is a system aimed at making information more accessible to a diverse range of users, including those with visual and hearing impairments. This system has the functionality to convert audio data into text data, analyze that text data to extract important information, and, if necessary, convert the text back into audio or generate related images. Specific implementations are described below.

[0618] First, the user inputs voice through their device. The voice is typically recorded via a microphone and sent directly to the server as digital data. The server then uses a speech recognition algorithm to convert the received voice data into text. This conversion process utilizes noise filtering technology to improve voice clarity, thereby increasing the accuracy of the converted text.

[0619] Next, the server applies natural language processing techniques to the converted text data to analyze its content. Specifically, it performs contextual understanding and extracts key phrases. Through this process, the system can identify important parts of the information contained and highlight information that is useful to the user.

[0620] Furthermore, based on the analysis results, the server selects the re-output format to suit the user's needs. This includes methods such as converting the extracted text information back into speech, as well as generating appropriate images from the text for visualization. The generated audio and image data are sent to the user's device for display or playback.

[0621] For example, when this system is applied to recording meetings, participants' voices are captured in real time and immediately recorded as text. This transcribed information is provided to participants in a way that highlights the important points and action items of the meeting. Participants with hearing impairments can follow the progress of the meeting by reading the text displayed in real time. In educational settings, it is also possible to use text and images generated from audio as part of multimedia teaching materials to support learners' understanding.

[0622] Thus, this system, which processes audio, text, and images in an integrated manner, will improve information accessibility in a variety of situations.

[0623] The following describes the processing flow.

[0624] Step 1:

[0625] The user inputs voice using the device. The voice is captured through the device's microphone and temporarily stored as digital data.

[0626] Step 2:

[0627] The terminal sends the captured audio data to the server. During this process, the data is converted to an appropriate format and transmitted via the communication line.

[0628] Step 3:

[0629] The server passes the audio data received from the terminal to speech recognition software, which converts the audio into text data. This conversion process applies a noise reduction filter to improve the clarity of the speech.

[0630] Step 4:

[0631] The server passes the obtained text data to a natural language processing algorithm for content analysis. This analysis understands the context from the text and extracts important information and key phrases.

[0632] Step 5:

[0633] Based on the analysis results, the server selects the appropriate output format. Specifically, this includes means of converting text back into speech and generating related images.

[0634] Step 6:

[0635] The generated audio or image data is sent from the server to the terminal. A secure communication protocol is used for transmission to ensure accurate data delivery.

[0636] Step 7:

[0637] The device provides the user with the received data. Audio data is played through the speaker, and image data and text data are displayed on the screen.

[0638] (Example 1)

[0639] 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".

[0640] Conventional information access systems have made it difficult for diverse users, including those with visual and hearing impairments, to efficiently acquire and utilize information. In particular, there were problems with the quality of information degrading due to noise contamination during the conversion of audio information and insufficient identification of important information during text analysis. Furthermore, it was difficult to output the acquired information in a format that met the individual needs of users, posing accessibility challenges.

[0641] 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.

[0642] In this invention, the server includes means for converting audio information into symbolic information, means for analyzing the symbolic information to identify important information, means for converting the symbols back into audio or generating related visual information based on the identified information, and means for selecting an output format according to the user's request based on the integrated information. This makes it possible to process diverse information centrally and provide information in the optimal format to meet the needs of each user while maintaining the quality of the information.

[0643] "Audio information" refers to data acquired as sound waves, such as human voices.

[0644] "Symbolic information" refers to digital data converted from audio information, and is usually represented in text format.

[0645] "Analysis" refers to the process of extracting useful information from data, and includes understanding the context of the data and identifying important information.

[0646] "Visual information" refers to information that has been converted into a visually understandable form, and is provided as data such as images and diagrams.

[0647] "Filtering" is the process of removing unwanted elements from data, and in this context, it refers to the operation of removing noise from an audio signal.

[0648] "Language analysis technology" refers to techniques used to analyze text and understand its grammar and meaning, and includes natural language processing technology.

[0649] "Integrated information" refers to data from different formats that have been combined and compiled into a single, comprehensive set of information.

[0650] "User" refers to an individual who uses this system to obtain or manipulate information.

[0651] "Output format" refers to the final form in which information is presented, and includes formats such as audio, text, and images.

[0652] This invention provides a system that makes information more accessible to a diverse range of users, including those with visual and hearing impairments. This system converts audio information into symbolic information, analyzes that symbolic information to extract important information, and can also convert it back to audio or generate related visual information as needed.

[0653] The user inputs voice through the device. The voice input is transmitted via a microphone, converted to digital format, and then sent to the server. During this process, the device captures the voice in real time, minimizing latency through edge computing technology. The server applies a speech recognition algorithm to the received voice information, converting it into symbolic information. A common speech recognition engine can be used here.

[0654] The server analyzes the acquired symbolic information using language analysis techniques. This analysis process utilizes pre-trained natural language processing techniques to understand the text context and extract key phrases. For example, it can highlight information important to the user and provide a re-output format tailored to the user's intent.

[0655] The server uses the information generated based on the analysis to produce audio and visual information. A general-purpose speech synthesis engine can be used for audio generation, and image generation technology can be utilized for visual information generation. Users receive the generated results through their devices and can view the information, either played back as audio or displayed on the screen.

[0656] As a concrete example, this system can be used to transcribe audio information from a meeting into text and provide it to meeting participants in a format that highlights the key points. In this scenario, participants with hearing impairments can read the text information in real time and follow the progress of the meeting.

[0657] Examples of prompts include, "Transcribe the meeting audio into text and highlight the key points," and "Generate relevant images from the audio data to provide visual supplementation."

[0658] In this way, this system integrates audio, symbolic, and visual information to provide information tailored to the individual needs of users.

[0659] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0660] Step 1:

[0661] The user inputs voice using a terminal. Voice input is typically performed through a microphone, and the analog voice signal is converted into digital data. The terminal sends this digital voice data to the server. At this stage, the input is the user's voice, and the output is the digital voice data sent to the server.

[0662] Step 2:

[0663] The server applies a speech recognition algorithm to the received digital audio data. Noise filtering techniques are used to remove background noise and improve speech clarity. Specifically, noise components are removed from the audio data to generate highly accurate text data. At this stage, the input is digital audio data, and the output is the converted text data.

[0664] Step 3:

[0665] The server uses language analysis techniques to parse the converted text data. This process involves understanding the context of the text and extracting key phrases. Specifically, it uses natural language processing techniques to analyze the syntax within the text and highlight important information. The input at this stage is the text data, and the output is a list of important information or the corrected text data.

[0666] Step 4:

[0667] The server determines the output format that best suits the user's needs based on the analysis results. Using a generative AI model, it converts the data back into audio or generates images as visual complements. At this stage, the input is the analysis results, and the output is the generated audio or image data.

[0668] Step 5:

[0669] The server sends the generated audio and image data to the user's device. The device receives this data and provides it to the user in a format suitable for visual or auditory use. Specifically, it may play audio through a speaker or display images on a screen. At this stage, the input is the generated content, and the output is usable information for the user's experience.

[0670] (Application Example 1)

[0671] 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".

[0672] In factories, rapid and accurate information transmission and understanding are essential. However, traditional methods such as voice instructions and written documents have made it difficult to adequately convey information to workers with visual and hearing impairments. Furthermore, background noise in voice instructions can sometimes obscure the content. Therefore, there is a need for a system that allows workers to efficiently receive information and perform tasks safely and reliably.

[0673] 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.

[0674] In this invention, the server includes means for converting audio data into text data, means for analyzing the text data to extract important information, means for converting the text back into audio or generating related images based on the extracted information, means for visually presenting the extracted information using a visual display device, and means for analyzing audio instructions entered by the user and selecting appropriate process information. This enables factory workers to efficiently receive work instructions and safely perform their work without visual or auditory limitations.

[0675] "Audio data" refers to information obtained by digitizing audio signals, and it represents various sounds, including human voices.

[0676] "Text data" refers to information composed of strings of characters, and is a data format used to describe sentences, commands, and other similar content.

[0677] A "visual display device" is a device, such as a display or screen, that displays text or images to appeal to human vision.

[0678] "Speech recognition" is a technology that analyzes speech data and converts it into text data.

[0679] "Natural language processing technology" refers to the technology that enables computers to understand, interpret, and generate human language, and is used for analyzing text data and understanding context.

[0680] "Noise filtering" is a technique that removes unwanted background noise and other sounds from audio data to improve speech clarity.

[0681] "Speech re-conversion" is the process of generating speech based on text data and then providing it as speech again.

[0682] "Information extraction" refers to the process of taking out necessary or important parts of data.

[0683] "Process information" refers to information that includes the procedures and instructions necessary to perform a specific task or operation.

[0684] This invention is a system for streamlining information exchange among workers in a factory. This system begins with speech recognition using voice data, then utilizes natural language processing technology to extract important information as text data, and finally provides this information through a visual display device.

[0685] First, the operator inputs voice commands into the system via an input device. The server converts this voice data into a digital signal and uses noise filtering technology to improve the clarity of the voice. Next, speech recognition software converts the voice into text data.

[0686] The server applies natural language processing techniques to the obtained text data to perform contextual understanding and precisely analyze important information and process details. Furthermore, based on the analyzed information, speech re-conversion is performed, or related images are generated, and the information is presented on a visual display device. As a result, the information displayed on the visual display device becomes an important factor for workers to make decisions that enable them to perform their tasks efficiently.

[0687] For example, if a worker wants to know the maintenance procedure for a conveyor belt, they can instruct the smart glasses by saying, "Tell me the procedure for the conveyor belt." The server analyzes this instruction and displays the relevant procedure information on the screen, and can play it back as audio if necessary. This allows workers to receive information through both sight and sound, and acquire the knowledge necessary for the job from multiple perspectives.

[0688] As an example of a prompt, we will use the instruction, "Please describe the inspection procedure for the conveyor belt. Please keep each step simple and highlight the points to pay attention to." This prompt will contribute to the generation of specific and concise work procedures through the generative AI model.

[0689] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0690] Step 1:

[0691] The user inputs voice commands through a terminal. The terminal converts this voice data into a digital signal and sends it to the server. The input is the user's voice, and the output is digital voice data.

[0692] Step 2:

[0693] The server performs noise filtering on the received digital audio data to improve speech clarity. The input is digital audio data, and the output is clear audio data with the noise removed.

[0694] Step 3:

[0695] The server processes clear audio data using a speech recognition algorithm to convert it into text data. The input is noise-removed audio data, and the output is the corresponding text data.

[0696] Step 4:

[0697] The server analyzes the generated text data using natural language processing techniques to understand the context and extract important information. The input is text data, and the output is the extracted important information.

[0698] Step 5:

[0699] The server performs speech re-transmission or generates images for visual display based on the extracted key information. In this step, a generative AI model may be used to create relevant image materials. The input is the extracted information, and the output is synthesized speech or generated image data.

[0700] Step 6:

[0701] The terminal receives synthesized speech or image data transmitted from the server and provides it to the user through a visual display device. The input is speech or image data, and the output is the presentation of information to the user.

[0702] Step 7:

[0703] The user receives the information and, if necessary, issues another voice command, starting a cycle of inputting data into the server again. This is part of the user's actions to ensure the smooth running of the entire process.

[0704] 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.

[0705] This invention is a system that not only converts audio data into text data, analyzes the text to extract important information, but also recognizes the user's emotions contained in the audio data and customizes the information output based on that. By incorporating an emotion engine, this system aims to enrich the user experience by enabling the provision of content that responds to the user's emotions.

[0706] When a user inputs voice into a device, the device captures the voice data and sends it to a server. The server not only converts the voice to text but also uses an emotion engine to extract emotional characteristics from the voice. Emotion recognition technology, used in conjunction with speech recognition algorithms, recognizes emotional states based on voice tone and speaking style.

[0707] Text data is analyzed through natural language processing to extract important information. During this process, sentiment data obtained from the sentiment engine is used to deepen contextual understanding in the text analysis. This allows for output that considers the user's emotions, rather than simply extracting information.

[0708] Furthermore, emotional information is taken into consideration when converting the extracted information back into speech or generating related images. The server delivers the generated speech or images in an appropriate tone and style according to the user's emotional state.

[0709] For example, in educational settings, if a learner expresses feelings of dissatisfaction or confusion, the system can provide supplementary explanations in a more approachable tone. In business settings, if a user is perceived as stressed, the system can intervene in a way that is appropriate to their emotions, such as suggesting relaxing content.

[0710] In this way, by integrating an emotion engine, the system goes beyond simple information conversion and enables the delivery of dynamic content that responds to the user's emotions.

[0711] The following describes the processing flow.

[0712] Step 1:

[0713] The user uses a device to input voice. The device records this voice as digital data and prepares to send it to the server.

[0714] Step 2:

[0715] The terminal sends the recorded audio data to the server. It is important to use a communication protocol to securely transmit the data.

[0716] Step 3:

[0717] The server processes the received audio data through a speech recognition engine, converting it into text data. During this process, a noise filter is applied to improve the clarity of the speech.

[0718] Step 4:

[0719] The server passes the audio data to the emotion engine, which analyzes the emotional characteristics contained in the audio. It identifies emotions based on factors such as tone, speed, and volume of the voice.

[0720] Step 5:

[0721] The server analyzes the generated text data using natural language processing algorithms. It understands the context and extracts important information and key phrases.

[0722] Step 6:

[0723] The server takes into account the results from the emotion engine and determines the output format based on the extracted information. When converting text to speech, it applies an emotion-appropriate tone. When generating images, it considers emotion-based design.

[0724] Step 7:

[0725] The server generates information that corresponds to the user's emotions and sends it to the terminal. The transmitted data includes content that reflects those emotions.

[0726] Step 8:

[0727] The device provides the user with audio, text, or image data received from the server. This ensures that the information displayed is sensitive to the user's unconscious emotions.

[0728] (Example 2)

[0729] 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".

[0730] In the process of converting audio data into text data and analyzing the data to extract important information, conventional systems have struggled to generate and provide content that takes into account the user's emotional state. Furthermore, as the importance of providing content that responds to emotions increases, there is a need for a mechanism that provides dynamic and personalized output to enrich the user experience. In addition, it is necessary to ensure that users receive the information provided in the most optimal way based on their emotions.

[0731] 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.

[0732] In this invention, the server includes means for converting audio data into text data, means for analyzing the text data and audio data and identifying emotions based on the tone and speed of the voice, and means for dynamically generating content using a generative model from information extracted based on the emotion identification data. This makes it possible to provide information customized with a tone and style that corresponds to the user's emotions.

[0733] "Audio data" refers to information that represents sound in digital format, and involves the electronic recording and processing of acoustic signals.

[0734] "Text data" refers to strings of information written in natural language, which are character information that is recorded and processed electronically.

[0735] "Input device" refers to a device used to capture audio or other sounds, and includes recording devices and microphones.

[0736] A "communication device" is a device used to send and receive data, and it has a network interface and protocol conversion function.

[0737] A "central processing unit" is a key component of a computer system that receives, processes, and analyzes data.

[0738] "Means of conversion" refers to technical processes or devices used to change data from one format to another.

[0739] "Means of analysis" refers to techniques for analyzing data and extracting specific information or features.

[0740] "Emotion recognition" is a technology that determines a user's emotional state from voice and text data, taking into account factors such as tone and speed.

[0741] A "generative model" refers to an algorithm that automatically generates new content and information using machine learning or artificial intelligence.

[0742] "Methods for dynamically generating content" refer to technologies that adaptively generate new information in response to the user's emotions and circumstances.

[0743] A "server" refers to a computer system that processes and provides data in response to client requests.

[0744] This invention is a system that provides content that takes into account the user's emotions based on voice input. Specific embodiments for realizing this system are described below.

[0745] The user first inputs their voice using the terminal's input device. At this stage, the input device, including a microphone, is used to capture the voice with high accuracy. The captured voice data is immediately converted to a digital format and then transmitted to the central processing unit via a communication device. Secure protocols such as HTTPS are used for communication.

[0746] The server converts the received audio data into text data using a speech recognition engine (for example, industry-standard speech recognition software). The converted text data and the information associated with the audio data are then analyzed by an emotion analysis module to identify the user's emotional state. This analysis includes analyzing the tone, speed, and intonation of the voice. Emotion recognition technology can be utilized in this analysis.

[0747] Based on the analysis results, the server applies natural language processing technology to extract important information from the text data. Subsequently, it dynamically generates new content using a generative AI model. This process can utilize voice generation with tone and style adjusted according to the user's emotions (e.g., text-to-speech services) and image generation technology (e.g., technology that generates images from natural language).

[0748] The generated content is transmitted to the terminal via a communication device, and the terminal provides it to the user audiovisually. The user can listen to the generated audio through a speaker or visually view the generated images on a display. As a result, an emotionally sensitive user experience can be provided.

[0749] A concrete example is a scenario where, if a learner is confused during a lesson, the system generates supplementary explanations in a friendly tone. In this case, an example of a prompt might be: "Convert the following audio data to text, analyze its sentiment, and output it to the user based on the specific content. Adjust the tone of the audio or image output based on the sentiment recognition."

[0750] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0751] Step 1:

[0752] The user inputs voice into the terminal using an audio input device. The terminal converts the voice into a digital format and transmits it to the server via a communication device. The input data is an analog audio signal, and the output is digital audio data. This conversion process uses a high-precision microphone to minimize noise and capture clear audio.

[0753] Step 2:

[0754] The server converts received digital audio data into text data using a speech recognition engine. The input is digital audio data, and the output is text data in string format. In this process, the speech recognition algorithm analyzes acoustic features and converts them into strings. During this process, the speech recognition accuracy is optimized to generate grammatically correct sentences.

[0755] Step 3:

[0756] The server uses text data and speech features to identify the user's emotions using an emotion analysis module. The input is text data and speech features, and the output is digital information indicating the emotional state. In this step, the emotion analysis model evaluates the tone, speed, emphasis, etc., of the speech to identify the user's emotional state.

[0757] Step 4:

[0758] The server uses natural language processing techniques to analyze text data while considering sentiment information, and extracts important information. The input is text data including sentiment data, and the output is digital data containing the important information. This process utilizes syntactic analysis and semantic extraction techniques to accurately grasp the key points.

[0759] Step 5:

[0760] The server uses a generative AI model to generate new content based on emotional states and extracted information. Input consists of key information and emotional data, while output is customized audio and image data provided to the user. The generative AI generates output that matches the user's emotional state and style.

[0761] Step 6:

[0762] The server sends the generated content to the terminal. The terminal then plays it back to the user as audio or displays it as an image on the screen. The input is the generated content, and the output is the provision of auditory or visual information to the user. This process ensures that information is presented to the user in an appropriate manner while maintaining the quality of the audio and images.

[0763] (Application Example 2)

[0764] 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".

[0765] Modern speech recognition systems are limited to simply converting speech data into text, and are insufficient for providing content that takes user emotions into account. In particular, content delivery services need technology to provide appropriate content that responds to user emotions, thereby improving the user experience.

[0766] 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. In this invention, the server includes means for converting audio data into text data, means for analyzing the text data to extract important information and recognize emotions, and means for converting the text back into audio based on the extracted information and emotions. This makes it possible to provide content that responds to the user's emotions and to improve the user experience.

[0767] "Audio data" refers to digital information that contains recorded audio.

[0768] "Text data" refers to digital information expressed as character data.

[0769] "Analysis" is the process of analyzing data to understand its content and characteristics.

[0770] "Information extraction" means taking specific, important information from the entire dataset.

[0771] "Recognizing emotions" means analyzing voice and text to identify the emotional state an individual is exhibiting.

[0772] "Re-conversion" refers to the process of converting data that has already been converted into a different format.

[0773] "Visual elements" refer to graphics and images that are displayed to the user.

[0774] "To provide" means to transmit information or services to users.

[0775] "Social media" refers to a platform where users share content with each other via online platforms.

[0776] "Means" refers to the methods and techniques used to achieve an objective.

[0777] The system for realizing the present invention includes a terminal for handling voice data and a server for processing the data transmitted from the terminal. In this system, when a user inputs voice data into the terminal, the terminal captures this voice data and sends it to the server. The server uses a speech recognition algorithm to convert the transmitted voice data into text data. An example of software for speech recognition is the speech_recognition library.

[0778] The converted text data is analyzed using natural language processing techniques. This analysis involves understanding the grammar and context of the text and extracting important information. Simultaneously, by combining this with emotion recognition techniques, the user's emotions are extracted from the speech, and their characteristics are analyzed using an emotion engine. This emotion analysis utilizes emotion analysis models such as the transformers library.

[0779] Based on the information obtained from the analysis, the server either converts the text data back into speech or generates visual elements. The generated speech and visual elements are adjusted according to the user's emotions and delivered through social media. For example, if the system recognizes the user's emotion as "excited" while listening to a podcast, it has a function that automatically recommends related episodes that are likely to evoke similar excitement.

[0780] An example of a prompt is, "Analyze the sentiment in the text of the podcast episode and suggest an appropriate episode." This prompt provides guidance for the server to suggest the most suitable content based on the user's sentiment.

[0781] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0782] Step 1:

[0783] The device captures the user's voice input. At this point, the input is the user's voice data. The device uses the microphone to record the voice as digital data and prepares to send it to the server.

[0784] Step 2:

[0785] The server converts the received audio data into text data using a speech recognition algorithm. The input is audio data, and the output is text data. The speech_recognition library is used for this process, converting the audio into text information to prepare it for analysis.

[0786] Step 3:

[0787] The server analyzes the converted text data using natural language processing techniques to understand the context and extract important information. The input is text data, and the output is data containing important information. The techniques used in this process extract keywords and important phrases from the data.

[0788] Step 4:

[0789] The server simultaneously uses emotion recognition technology to recognize the emotions in the text obtained from the speech. The input is text data, and the output is data about emotional states. The emotion analysis model in the transformers library is responsible for identifying emotions from the text.

[0790] Step 5:

[0791] The server converts the extracted key information and sentiment data back into speech or generates visual elements. The input is key information and sentiment, and the output is processed speech or visual elements. A generative AI model is used to generate content with a tone and style that matches the user's sentiment.

[0792] Step 6:

[0793] The server delivers generated audio or visual elements to the user through social media. The input here is the processed audio or visual elements, and the output is content delivery as a user experience. This allows users to enjoy information and entertainment appropriate to their emotions.

[0794] 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.

[0795] 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.

[0796] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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."

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] The following is further disclosed regarding the embodiments described above.

[0816] (Claim 1)

[0817] A means of converting audio data into text data,

[0818] A means for analyzing the text data and extracting important information,

[0819] Means for converting text back into speech or generating related images based on the extracted information,

[0820] Means for providing the generated audio or image to the user,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, comprising means for removing background noise and clarifying the audio during the conversion of audio data.

[0824] (Claim 3)

[0825] The system according to claim 1, comprising means for using natural language processing techniques in the analysis of text data to perform grammatical correction and contextual completion.

[0826] "Example 1"

[0827] (Claim 1)

[0828] A means of converting audio information into symbolic information,

[0829] A means for analyzing the symbolic information to identify important information,

[0830] Means for converting symbols back into sound based on the identified information or means for generating related visual information,

[0831] Means for providing the generated audio or visual information to the user,

[0832] A means of selecting the output format according to the user's request based on integrated information,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, comprising means for filtering background noise and improving sound quality in the conversion of audio information.

[0836] (Claim 3)

[0837] The system according to claim 1, comprising means for using language analysis techniques in the analysis of symbolic information, and for performing grammatical correction and contextual information emphasis.

[0838] "Application Example 1"

[0839] (Claim 1)

[0840] A means of converting audio data into text data,

[0841] A means for analyzing the text data and extracting important information,

[0842] Means for converting text back into speech or generating related images based on the extracted information,

[0843] A means of visually presenting the extracted information using a visual display device,

[0844] A means for analyzing voice instructions entered by the user and selecting appropriate process information,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, comprising means for removing background noise and clarifying the audio data during conversion and for displaying the information on a visual presentation device.

[0848] (Claim 3)

[0849] The system according to claim 1, comprising means for using natural language processing techniques in the analysis of text data to perform grammatical correction and contextual completion, and for presenting information in real time.

[0850] "Example 2 of combining an emotion engine"

[0851] (Claim 1)

[0852] A means of capturing audio data with an input device and converting it to a digital format,

[0853] Means for transmitting the audio data to a central processing unit via a communication device,

[0854] A means of converting audio data into text data,

[0855] A means of analyzing text and audio data to identify emotions based on factors such as tone and speed of speech,

[0856] A means of analyzing text data using natural language processing technology and extracting important information,

[0857] A means of dynamically generating content using a generative model from information extracted based on sentiment recognition data,

[0858] A means of providing generated audio or images in a tone and style that matches the user's emotional state,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, comprising means for removing background noise and clarifying the audio during the conversion of audio data.

[0862] (Claim 3)

[0863] The system according to claim 1, comprising means for using natural language processing techniques in the analysis of text data to perform grammatical accuracy and contextual completion.

[0864] "Application example 2 when combining with an emotional engine"

[0865] (Claim 1)

[0866] A means of converting audio data into text data,

[0867] A means for analyzing the text data and extracting important information,

[0868] A means for recognizing an individual's emotions in voice data and adjusting the information provided based on those emotions,

[0869] Means for converting text back into speech or generating related visual elements based on the extracted information and emotions,

[0870] Means for providing the generated audio or visual elements to users through social media,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, comprising means for removing background noise and clarifying speech during the conversion of audio data.

[0874] (Claim 3)

[0875] The system according to claim 1, comprising means for using natural language processing techniques in the analysis of text data to perform language structure modification and contextual completion. [Explanation of Symbols]

[0876] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of converting audio data into text data, A means for analyzing the text data and extracting important information, Means for converting text back into speech or generating related images based on the extracted information, Means for providing the generated audio or image to the user, A system that includes this.

2. The system according to claim 1, comprising means for removing background noise and clarifying the audio during the conversion of audio data.

3. The system according to claim 1, comprising means for using natural language processing techniques in the analysis of text data to perform grammatical correction and contextual completion.

Images