system

Abstract

We provide the system. [Solution] A means for receiving audio data and performing a process to convert said audio data into text data, A means for receiving text data and performing a process to convert said text data into audio data, A means for performing a process that generates images according to a specified theme or style based on user instructions, A means for sending and receiving user input data via a communication network and for appropriately formatting said data, A system that includes means for displaying or playing back generated data to present it to the user.

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, the method including the 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] In modern information and communication society, users are required to efficiently generate and utilize various contents such as voice, text, and images. However, still a great deal of labor is required for the generation and analysis of these contents, and particularly when converting different types of data into each other, a lot of specialized knowledge is often required. For this reason, there is a demand for a system that users can intuitively operate and that can handle multiple purposes.

Means for Solving the Problems

[0005] The present invention solves the above problems by providing a system equipped with means for converting audio data into text data, means for converting text data into audio data, means for generating images based on user instructions, and means for sending, receiving, and presenting data. This system utilizes speech recognition algorithms and image generation algorithms to enable intuitive and efficient data generation and use for the user.

[0006] "Audio data" refers to digital or analog information used to electronically record or transmit sound.

[0007] "Text data" refers to digital data that handles information expressed using characters and symbols.

[0008] "Image generation" is the process of creating visual content using a computer based on specified conditions and parameters.

[0009] "Sending and receiving" refers to the process of sending and receiving data via a communication network.

[0010] "Display or playback" refers to the process of presenting information to a user visually or audibly.

[0011] A "speech recognition algorithm" is a part of a computer program that analyzes speech input and converts it into corresponding text.

[0012] "User instructions" refer to the content or requests entered by the user to control the operation of the system. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] 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 an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] 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.

[0017] 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.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a system for efficiently generating and analyzing speech, text, and images. The system aims to support users in various creative activities using smart devices. Specifically, it is configured as follows:

[0035] Users install the system's application on devices such as smartphones and tablets. When a user records audio, the device sends the audio data to the server. The server uses advanced speech recognition algorithms to convert the received audio data into text. The converted text data is returned to the device, allowing the user to immediately review and save it.

[0036] Furthermore, when a user inputs text data, the device sends that data to the server. The server uses speech synthesis technology to convert the text into speech and sends it back to the device. This allows users to use the audio playback function to create audiobooks and provide content for visually impaired individuals.

[0037] Users can further select image generation options within the app. After entering instructions regarding the theme and style to use, the device sends these instructions to the server. The server uses an image generation algorithm to create visual content based on the specified conditions and sends the generated image to the device. Users can then review the received image and save or share it as needed.

[0038] For example, a user could record audio during a meeting, instantly transcribe it into text, and save it as meeting minutes. Additionally, when creating children's picture books, the system could generate audio from text and then create corresponding illustrations, easily creating interactive content.

[0039] This system provides users with an innovative and efficient creative experience through its voice, text, and image conversion and generation capabilities.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user launches the application on their device and selects the function for voice input. When the user taps the voice input start button, recording begins.

[0043] Step 2:

[0044] The device buffers the audio data recorded by the user in real time and temporarily stores the data. When the recording end button is pressed, the audio data is finally saved and prepared for transmission to the server.

[0045] Step 3:

[0046] The device compresses the audio data and sends it to the server via the internet connection. HTTPS is used as the communication protocol to ensure secure data transmission.

[0047] Step 4:

[0048] The server analyzes the audio data received from the terminal and passes it to the speech recognition engine. The engine then begins the analysis to convert the audio into text.

[0049] Step 5:

[0050] The server retrieves the text data generated as a result of the analysis and formats it appropriately. The converted text data is then formatted to be easy for the user to use.

[0051] Step 6:

[0052] The server sends the converted text data back to the terminal. The data is then securely transmitted again using the HTTPS protocol.

[0053] Step 7:

[0054] The device displays the received text data on the screen. Users can review, edit, save, or share the displayed text with other applications.

[0055] Step 8:

[0056] When a user wants to convert text data into speech, the speech synthesis process begins when they send a request from their device to the server.

[0057] Step 9:

[0058] The server receives the transmitted text data and generates speech data using a speech synthesis engine. During this process, parameters such as the language and tone of the speech are adjusted according to the user's settings.

[0059] Step 10:

[0060] The server sends the generated audio data to the terminal. The user can then play and verify the audio through the terminal's speaker.

[0061] (Example 1)

[0062] 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."

[0063] In today's digital environment, methods for the rapid and accurate conversion and generation of audio, text, and visual information are particularly needed in creative industries and education. However, existing systems often fall short in terms of accuracy and efficiency, and fail to provide intuitive and user-friendly interfaces. This invention aims to efficiently perform the interconversion of audio, text, and visual information and improve the user experience.

[0064] 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.

[0065] In this invention, the server includes means for receiving audio information and performing a process to convert the audio information into text information, means for receiving text information and performing a process to convert the text information into audio information, and means for generating visual information using a generative AI model. This enables rapid and accurate mutual conversion of audio, text, and visual information, allowing users to engage in creative activities intuitively and effectively.

[0066] "Audio information" refers to data obtained by converting speech or sounds, which are expressed as sound waves, into a digital format.

[0067] "Character information" refers to a combination of symbols expressed as text, and is typically data represented in digital format using character codes.

[0068] "Visual information" refers to data that represents information perceived through sight, such as images and videos, in digital format.

[0069] "Means" refers to methods, devices, or system components established to achieve a specific objective.

[0070] A "generative AI model" is a machine learning model trained to perform generative tasks using artificial intelligence technology, and is a model that has the ability to generate creative output based on a specific input.

[0071] A "protocol" is a set of procedures and rules that define how communication takes place on a computer network.

[0072] "Secure" refers to a state or technology that protects access so that only authorized individuals can access it.

[0073] An "information processing device" is a computer system or device used to perform processing such as analysis, transformation, and generation of data and information.

[0074] This invention is a system for the efficient conversion and generation of audio, text, and visual information. Users install and use the application on a smart device with an internet connection (e.g., a smartphone or tablet).

[0075] When a user generates voice information, the device uses its built-in microphone to collect the audio and sends the data to a server via a secure protocol (e.g., HTTPS). The server then uses a speech understanding algorithm (e.g., a speech recognition API) to convert the voice information into text. This makes it possible, for example, to accurately transcribe speech during a meeting and save it as meeting minutes.

[0076] Furthermore, when a user inputs text information, the terminal sends the text data to the server. The server uses speech synthesis technology (e.g., a speech synthesis engine) to convert the text information into speech information. This functionality enables the creation of audiobooks and the provision of audio content for people with visual impairments.

[0077] Furthermore, if the user wants to generate visual information, the device sends a prompt message to the server based on the user's instructions. The server uses a generative AI model (e.g., an image generation algorithm) to generate visual information according to the specified subject and style. For example, using a prompt message such as "landscape of blue sky and grassland" can generate a specific image based on the instructions.

[0078] This system provides users with an intuitive and effective experience in the mutual conversion and generation of speech, text, and visual information.

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

[0080] Step 1:

[0081] The user launches an application on the device and records audio. The audio data collected using the built-in microphone is temporarily stored on the device. This audio information is then used as input.

[0082] Step 2:

[0083] The terminal sends the recorded audio data to the server using a secure protocol. The input data is transferred securely and prepared for processing on the server.

[0084] Step 3:

[0085] The server converts the received audio data into text information using an advanced speech understanding algorithm. This process transforms the audio information into text, which is then output as highly accurate text data.

[0086] Step 4:

[0087] The server sends the converted text information back to the terminal. The terminal receives the data and displays it on the user interface. This allows the user to see the text generated from the speech.

[0088] Step 5:

[0089] The user can make any necessary corrections based on the displayed text information and then resend that information to the server from their device. The corrected text information becomes the new input data.

[0090] Step 6:

[0091] The server converts the received text information back into speech information using speech synthesis technology. By utilizing a generative AI model in this process, the text is output as speech data in a specific speech style specified by the user.

[0092] Step 7:

[0093] The generated audio information is sent from the server to the terminal. The terminal plays it back, and the user can hear the result.

[0094] Step 8:

[0095] When a user wants to generate visual information, they enter a prompt message containing a specific theme or style, which is then sent from the terminal to the server. This serves as input data for image generation.

[0096] Step 9:

[0097] The server generates visual information based on prompts using a generative AI model. A specific image is output by the image generation algorithm according to the specified conditions.

[0098] Step 10:

[0099] The generated visual information is sent to the device and displayed for the user to review, save, and share. The user can then use this information to complete specific creative tasks.

[0100] (Application Example 1)

[0101] 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."

[0102] To support the creative activities of diverse users, a system is needed that can efficiently generate and edit audio, text, and images bidirectionally, and freely customize them to individual preferences. However, existing technologies are insufficient in terms of mutual conversion between each medium and flexible customization of generated content, making it difficult to meet user needs.

[0103] 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.

[0104] In this invention, the server includes means for receiving audio information and performing a process to convert the audio information into document information, means for receiving document information and performing a process to convert the document information into audio information, and means for generating visual data according to a specified concept or expression based on user instructions. This enables the mutual conversion of audio, text, and images, and makes it possible to flexibly generate diverse creative content in response to the individual requests of users.

[0105] "Audio information" refers to auditory data recorded or acquired through microphones or other sound acquisition devices.

[0106] "Document information" refers to data expressed in text format, which is a string of characters that serves a semantic or information-transmitting purpose.

[0107] "Visual data" refers to data that is visually represented, such as digital images and videos, and is used to convey visual information to users.

[0108] An "information system" is a technological infrastructure consisting of software and hardware for collecting, storing, processing, and transmitting data.

[0109] "Individual preferences" refer to the characteristics of individual users' tastes and preferences, which influence the choices and decisions they make.

[0110] The system for carrying out this invention includes functions for the mutual conversion and generation of audio, document, and visual data. First, the user may record audio information using a smart device and send that audio information to a server. The server uses acoustic analysis technology to convert the audio information into document information and sends that document information to the user's device. The user can then edit the received document information as needed.

[0111] Next, the user sends document information to the server, which converts that document information into audio information using sound synthesis technology. This audio information is then sent back to the user's terminal, where the user can review or play the audio information.

[0112] Thirdly, the user sends a request for visual data generation from their device to the server. Based on the user's instructions, the server uses a generation AI model to generate visual data according to the specified theme or style, and sends the generated visual data to the user's device. This visual data can then be viewed, saved, or shared by the user.

[0113] One concrete example is when a user records audio on their smartphone during a meeting and immediately converts it into meeting minutes as document information. Furthermore, users can play back the document information as audio, which can be useful for creating podcasts. It is also possible to convert children's stories from text to audio, generate visual data that matches the content, and create interactive content.

[0114] An example of a prompt is, "If a user speaks to their smartphone and says, 'Create an image of a natural landscape,' then a relevant image will be generated based on that instruction." Based on this prompt, the generation AI model can instantly generate visual data and provide it to the user.

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The user records voice information using a smart device. The recorded voice information is stored in the device's application and prepared to be sent to a server. The input is the user's voice, and the output is digital voice data.

[0118] Step 2:

[0119] The terminal sends audio information to the server. The server converts the received audio data into document information using acoustic analysis technology. This process involves analyzing the acoustic waveform and identifying phonemes. The input is audio data, and the output is text data.

[0120] Step 3:

[0121] The server sends the generated document information to the terminal. The terminal provides an interface that allows the user to review the document information and edit the content as needed. The input is text data, and the output is editable text provided to the user.

[0122] Step 4:

[0123] The user sends the edited document information back to the server via their terminal. The server then uses sound synthesis technology to convert this document information into audio. At this stage, an audio waveform is generated from the text. The input is edited text data, and the output is audio data.

[0124] Step 5:

[0125] The server sends the generated audio information to the terminal. The terminal uses this audio information to provide the user with the function to play the audio. The input is audio data, and the output is the audio heard by the user.

[0126] Step 6:

[0127] The user sends a prompt message from their terminal to the server for generating visual data. The server generates the visual data using a generative AI model based on the prompt message. In this process, conceptual instructions are converted into concrete visual representations. The input is the prompt message, and the output is image data.

[0128] Step 7:

[0129] The server sends the generated visual data to the terminal. The terminal presents this visual data to the user and provides functions to save and share it as needed. The input is image data, and the output is an image that the user can view.

[0130] 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.

[0131] This invention is a system that combines the generation and analysis of speech, text, and images with an emotion engine that recognizes user emotions. The system aims to provide a more personalized experience by optimizing content generation and the interface based on user emotions. Details are provided below.

[0132] Users input voice and text using an application on their device. This input information is sent from the device to the server. The server uses an emotion engine to analyze the user's emotions from this data and identify the type of emotion. This analysis result is used in the content generation process.

[0133] For example, if a user expresses positive emotions, the server can generate and provide cheerful music or images based on that information. On the other hand, if negative emotions are detected, the server can take appropriate action, such as suggesting content that promotes relaxation.

[0134] Furthermore, this emotional information also influences the customization of the user interface. For example, if a user is feeling stressed, the device may display a simpler interface and provide support to reduce the user's burden.

[0135] Furthermore, this system can dynamically adjust the style and theme of the generated images according to the user's emotions. As a result, the generated content is more in line with the user's psychological state.

[0136] As a concrete example, when a user wants a little positive inspiration during a break from work, the system could analyze the user's current mood and generate and provide quotes or images appropriate to that state. This would allow the user to quickly reduce psychological stress and refresh their mind.

[0137] In this way, by combining emotion recognition with voice, text, and image generation and analysis capabilities, this system can provide users with a deeper level of personalized and innovative creative experience.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] The user launches the application on their device and selects either voice input or text input. When the user presses the record button, voice data recording begins, or text is entered into the text box.

[0141] Step 2:

[0142] The device temporarily stores the acquired audio data and reads the text data in real time. After recording is finished, or after text input is complete, it is ready to send the data to the server.

[0143] Step 3:

[0144] The terminal compresses the audio data and sends it to the server, and similarly sends the text data. A secure communication protocol (e.g., HTTPS) is used for this.

[0145] Step 4:

[0146] The server passes the received audio data to the speech recognition engine, which converts the audio into text data. Simultaneously, the emotion engine analyzes both the audio and the transmitted text data.

[0147] Step 5:

[0148] The server's emotion engine identifies the user's current emotional state using features extracted from text and audio data. Based on these results, it then begins generating optimized content.

[0149] Step 6:

[0150] The server generates user-appropriate content (audio, images, etc.) based on analyzed emotions. For example, when a user is feeling happy, it selects content with a cheerful theme.

[0151] Step 7:

[0152] The server sends the generated content to the terminal. In the case of audio data, it is converted to a playable format, and in the case of image data, it is sent according to its resolution and format.

[0153] Step 8:

[0154] The device presents the received data to the user. Specifically, it plays audio data through the speaker and displays images on the screen. Furthermore, the user interface can be customized with color schemes and layouts that respond to emotions.

[0155] (Example 2)

[0156] 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".

[0157] In today's information society, a challenge is the insufficient personalization based on emotions when users engage with diverse digital content. Conventional technologies have struggled to dynamically generate content that considers the user's psychological state, often failing to provide a practical experience. This has limited the user experience and made it difficult to meet individual needs.

[0158] 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.

[0159] In this invention, the server includes means for receiving diverse data input from information devices and analyzing the data to identify the user's emotions; means for dynamically generating content based on the user's emotions using a predetermined artificial intelligence model; and means for displaying or playing the generated content in a manner that adapts to the user's psychological state. This makes it possible to achieve deep personalization that is in line with the user's emotions and to provide an individually optimized experience.

[0160] "Audio data" refers to sound information represented in digital format, and is primarily used for recording and playing back audio.

[0161] "Text data" refers to information represented by strings of characters, in a format that can be processed and stored by a computer.

[0162] "Information equipment" refers to devices used for inputting, processing, and outputting data, and includes electronic devices such as computers and smartphones.

[0163] A "generative AI model" refers to artificial intelligence technology that has the ability to generate new data and content based on large datasets.

[0164] "User emotions" refers to the psychological state estimated from the user's input data using emotion recognition technology.

[0165] "Content" refers to information and media consumed by users, and includes a variety of forms such as music, images, and text.

[0166] This invention is a system that generates and analyzes audio data, text data, and image data to provide personalized content tailored to the user's emotional state. The system uses a terminal and a server as its main components.

[0167] First, the user inputs voice or text through an application on their device. This device is connected to input devices such as a microphone and keyboard. The input data is then transmitted to a server via a communication network.

[0168] The server processes the received data through an emotion recognition engine to analyze the user's emotions. This process uses a specific algorithm to perform highly accurate emotion analysis. Based on the analysis results, a predetermined generative AI model (for example, a model using a Deep Learning framework) is used to generate content that matches the user's emotions.

[0169] The generated content is delivered to the user via the device. The user interface also dynamically changes in response to the user's emotions, incorporating features to improve usability.

[0170] As a concrete example, if a user expresses positive emotions, the system could input a prompt message such as "Please tell me a quote to make you smile more today" into an AI model, which could then generate appropriate quotes and images.

[0171] This system allows users to receive content tailored to their individual psychological state, enabling them to enjoy a better experience.

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

[0173] Step 1:

[0174] Users input voice or text data using applications on their devices. This includes recording their voice using the device's microphone or typing text using the keyboard. The input data is saved on the device as a temporary file.

[0175] Step 2:

[0176] The terminal sends the input voice or text data to the server. Transfer over the communication network primarily uses the HTTPS protocol. During transmission, the data is encoded in the appropriate format.

[0177] Step 3:

[0178] The server analyzes the received audio and text data. Using an emotion recognition engine, it applies natural language processing (NLP) and speech analysis algorithms to the data to identify the user's emotions. As a result of the analysis, the user's emotional state (e.g., joy, surprise, stress) is generated.

[0179] Step 4:

[0180] The server inputs a prompt message into the generative AI model based on the obtained sentiment analysis results. This prompt message includes a request such as, "Generate appropriate content based on the user's sentiment." The generative AI model (for example, a large-scale language model) is executed, and content is output.

[0181] Step 5:

[0182] The device receives content generated from the server and provides it to the user. The content is played or displayed as music, text, or images. The display interface also adapts to the user's emotional state, improving user interaction.

[0183] Step 6:

[0184] Users engage with the presented content and input their feedback into their device. This feedback is sent to the server as data that contributes to further system improvements and the learning process.

[0185] (Application Example 2)

[0186] 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".

[0187] In recent years, there has been a growing demand for personalizing information and content based on user emotions, but existing systems are unable to adequately meet this need. In particular, there is a lack of technology that can accurately analyze diverse user emotions and provide appropriate media content based on the results. This has led to decreased user satisfaction, and there is a need to realize content delivery based on more sophisticated emotion recognition.

[0188] 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.

[0189] In this invention, the server includes means for receiving audio data and performing a process to convert the audio data into text data; means for analyzing the user's emotions and performing a process to dynamically present content based on those emotions; and means for recommending and providing media content that corresponds to the emotions to the user. This makes it possible to provide personalized content that accurately reflects the user's emotional state.

[0190] "Audio data" refers to data obtained by digitizing audio signals, which is a conversion of sound information into a format that can be processed numerically.

[0191] "Text data" refers to data composed of characters and symbols, which is human language information in a format that can be processed by a computer.

[0192] An "image" is data that represents visual information, a visual representation expressed in digital format as a collection of pixels.

[0193] "User emotions" refer to information that indicates the feelings and psychological state a user is experiencing at a given point in time, and are subjective states inferred from voice and text data.

[0194] "Content" refers to a collection of digital information displayed or played by a user, provided in formats such as images, audio, text, and video.

[0195] "Sentiment analysis" is a data analysis technique performed by algorithms that identifies a user's emotional state using voice, text, or other data.

[0196] "Presentation" refers to the act of showing information or data to a user visually or audibly, and is a representation of data carried out through a user interface.

[0197] "Recommendation" is the process of selecting and presenting relevant content based on the user's preferences and circumstances, and is an information delivery method aimed at improving the user experience.

[0198] This system consists of a server for analyzing emotions and a terminal that receives user input. The terminal receives voice and text data from the user and transfers this data to the server. The server converts the voice data into text data using an advanced speech recognition algorithm. In this process, it utilizes existing speech recognition libraries (e.g., TextBlob). Furthermore, a generative AI model analyzes the user's emotions and generates or recommends appropriate content based on those emotions.

[0199] The server uses an emotion analysis engine to generate media content that responds to the user's emotions. This ensures that the digital content provided to the user matches their current emotions, creating a more immersive experience. Recommended content is sent to the device and displayed or played on the user's device.

[0200] For example, if a user types "I'm tired today," the server analyzes the user's emotion as "fatigue" and provides relaxation music or relaxing images to the device. An example of an input prompt for the generating AI model could be: "The user typed 'I'm tired today.' Determine the user's emotion from this input and recommend a video that matches that emotion."

[0201] By structuring the content in this way, it becomes possible to provide content that matches the user's emotional state and realize a more personalized user experience.

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The user inputs voice or text into the device. The input voice data is stored in its original format, while the text data is stored as a string on the device.

[0205] Step 2:

[0206] The terminal converts the input audio data into a digital signal and then applies a speech recognition algorithm to convert it into text data. This converted text data is then prepared to be transferred to the server.

[0207] Step 3:

[0208] The terminal transmits text data converted from voice data or text data directly entered by the user to the server via the communication network. During this process, the data is converted to an appropriate format and transferred according to a specific communication protocol.

[0209] Step 4:

[0210] The server performs sentiment analysis on the received text data. The sentiment analysis engine analyzes the text data and identifies the user's emotional state (e.g., positive, negative, neutral). Based on this analysis, it selects an appropriate generative AI model.

[0211] Step 5:

[0212] The server generates or selects content based on the identified user's emotions. Content such as images, music, and videos that match the emotions are generated by a generation AI model. The generated content is packaged as components and prepared for display.

[0213] Step 6:

[0214] The server sends the generated content to the terminal and presents it to the user in an appropriate format. The terminal analyzes the received content data and displays or plays it in the way that is easiest for the user to understand.

[0215] Through this series of processes, users can experience personalized content on their devices that responds to their emotions.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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".

[0232] This invention is a system for efficiently generating and analyzing speech, text, and images. The system aims to support users in various creative activities using smart devices. Specifically, it is configured as follows:

[0233] Users install the system's application on devices such as smartphones and tablets. When a user records audio, the device sends the audio data to the server. The server uses advanced speech recognition algorithms to convert the received audio data into text. The converted text data is returned to the device, allowing the user to immediately review and save it.

[0234] Furthermore, when a user inputs text data, the device sends that data to the server. The server uses speech synthesis technology to convert the text into speech and sends it back to the device. This allows users to use the audio playback function to create audiobooks and provide content for visually impaired individuals.

[0235] Users can further select image generation options within the app. After entering instructions regarding the theme and style to use, the device sends these instructions to the server. The server uses an image generation algorithm to create visual content based on the specified conditions and sends the generated image to the device. Users can then review the received image and save or share it as needed.

[0236] For example, a user could record audio during a meeting, instantly transcribe it into text, and save it as meeting minutes. Additionally, when creating children's picture books, the system could generate audio from text and then create corresponding illustrations, easily creating interactive content.

[0237] This system provides users with an innovative and efficient creative experience through its voice, text, and image conversion and generation capabilities.

[0238] The following describes the processing flow.

[0239] Step 1:

[0240] The user launches the application on their device and selects the function for voice input. When the user taps the voice input start button, recording begins.

[0241] Step 2:

[0242] The device buffers the audio data recorded by the user in real time and temporarily stores the data. When the recording stop button is pressed, the audio data is finally saved and prepared for transmission to the server.

[0243] Step 3:

[0244] The device compresses the audio data and sends it to the server via the internet connection. HTTPS is used as the communication protocol to ensure secure data transmission.

[0245] Step 4:

[0246] The server analyzes the audio data received from the terminal and passes it to the speech recognition engine. The engine then begins the analysis to convert the audio into text.

[0247] Step 5:

[0248] The server retrieves the text data generated as a result of the analysis and formats it appropriately. The converted text data is then formatted to be easy for the user to use.

[0249] Step 6:

[0250] The server sends the converted text data back to the terminal. The data is then securely transmitted again using the HTTPS protocol.

[0251] Step 7:

[0252] The device displays the received text data on the screen. Users can review, edit, save, or share the displayed text with other applications.

[0253] Step 8:

[0254] When a user wants to convert text data into speech, the speech synthesis process begins when they send a request from their device to the server.

[0255] Step 9:

[0256] The server receives the transmitted text data and generates speech data using a speech synthesis engine. During this process, parameters such as the language and tone of the speech are adjusted according to the user's settings.

[0257] Step 10:

[0258] The server sends the generated audio data to the terminal. The user can then play and verify the audio through the terminal's speaker.

[0259] (Example 1)

[0260] 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."

[0261] In today's digital environment, methods for the rapid and accurate conversion and generation of audio, text, and visual information are particularly needed in creative industries and education. However, existing systems often fall short in terms of accuracy and efficiency, and fail to provide intuitive and user-friendly interfaces. This invention aims to efficiently perform the interconversion of audio, text, and visual information and improve the user experience.

[0262] 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.

[0263] In this invention, the server includes means for receiving audio information and performing a process to convert the audio information into text information, means for receiving text information and performing a process to convert the text information into audio information, and means for generating visual information using a generative AI model. This enables rapid and accurate mutual conversion of audio, text, and visual information, allowing users to engage in creative activities intuitively and effectively.

[0264] "Audio information" refers to data obtained by converting speech or sounds, which are expressed as sound waves, into a digital format.

[0265] "Character information" refers to a combination of symbols expressed as text, and is typically data represented in digital format using character codes.

[0266] "Visual information" refers to data that represents information perceived through sight, such as images and videos, in digital format.

[0267] "Means" refers to methods, devices, or system components established to achieve a specific objective.

[0268] A "generative AI model" is a machine learning model trained to perform generative tasks using artificial intelligence technology, and is a model that has the ability to generate creative output based on a specific input.

[0269] A "protocol" is a set of procedures and rules that define how communication takes place on a computer network.

[0270] "Secure" refers to a state or technology that protects access so that only authorized individuals can access it.

[0271] An "information processing device" is a computer system or device used to perform processing such as analysis, transformation, and generation of data and information.

[0272] This invention is a system for the efficient conversion and generation of audio, text, and visual information. Users install and use the application on a smart device with an internet connection (e.g., a smartphone or tablet).

[0273] When a user generates voice information, the device uses its built-in microphone to collect the audio and sends the data to a server via a secure protocol (e.g., HTTPS). The server then uses a speech understanding algorithm (e.g., a speech recognition API) to convert the voice information into text. This makes it possible, for example, to accurately transcribe speech during a meeting and save it as meeting minutes.

[0274] Furthermore, when a user inputs text information, the terminal sends the text data to the server. The server uses speech synthesis technology (e.g., a speech synthesis engine) to convert the text information into speech information. This functionality enables the creation of audiobooks and the provision of audio content for people with visual impairments.

[0275] Furthermore, if the user wants to generate visual information, the device sends a prompt message to the server based on the user's instructions. The server uses a generative AI model (e.g., an image generation algorithm) to generate visual information according to the specified subject and style. For example, using a prompt message such as "landscape of blue sky and grassland" can generate a specific image based on the instructions.

[0276] This system provides users with an intuitive and effective experience in the mutual conversion and generation of speech, text, and visual information.

[0277] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0278] Step 1:

[0279] The user starts the application on the terminal and records voice information. The voice data collected using the built-in microphone is temporarily stored on the terminal. This voice information is used as input.

[0280] Step 2:

[0281] The terminal sends the recorded voice data to the server using a secure protocol. The input data is transferred in a secure state, and the server is prepared for processing.

[0282] Step 3:

[0283] The server converts the received voice data into character information using an advanced voice understanding algorithm. Through this operation, the voice information is converted into text and output as highly accurate character data.

[0284] Step 4:

[0285] The server returns the converted character information to the terminal. The terminal receives the data and displays it on the user interface. The user can thus view the text generated from the voice.

[0286] Step 5:

[0287] After making the necessary corrections based on the displayed character information, the user can resend the information from the terminal to the server. The corrected character information becomes new input data.

[0288] Step 6:

[0289] The server reconverts the received character information into voice information using voice synthesis technology. By leveraging the generated AI model in this process, the text is output as voice data in a specific voice style specified by the user.

[0290] Step 7:

[0291] The generated audio information is sent from the server to the terminal. The terminal plays it back, and the user can hear the result.

[0292] Step 8:

[0293] When a user wants to generate visual information, they enter a prompt message containing a specific theme or style, which is then sent from the terminal to the server. This serves as input data for image generation.

[0294] Step 9:

[0295] The server generates visual information based on prompts using a generative AI model. The image generation algorithm outputs a specific image according to the specified conditions.

[0296] Step 10:

[0297] The generated visual information is sent to the device and displayed for the user to review, save, and share. The user can then use this information to complete specific creative tasks.

[0298] (Application Example 1)

[0299] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0300] To support the creative activities of diverse users, a system is needed that can efficiently generate and edit audio, text, and images bidirectionally, and freely customize them to individual preferences. However, existing technologies are insufficient in terms of mutual conversion between each medium and flexible customization of generated content, making it difficult to meet user needs.

[0301] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means.

[0302] In this invention, the server includes means for receiving voice information and performing a process of converting the voice information into document information, means for receiving document information and performing a process of converting the document information into voice information, and means for generating visual data according to a specified concept or expression based on a user's instruction. Thereby, the mutual conversion of voice, text, and images becomes possible, and it becomes possible to flexibly generate various creative contents according to the individual requirements of the user.

[0303] "Voice information" is auditory data recorded or acquired through a microphone or other acoustic collection devices.

[0304] "Document information" is data expressed in text format and is a character string having a semantic or information transmission purpose.

[0305] "Visual data" is data visually represented such as digital images and videos, and is used to convey visual information to the user.

[0306] "Information system" is a technical infrastructure composed of software and hardware for collecting, storing, processing, and transmitting data.

[0307] "Personal preference" is the preference and selection characteristics that an individual user has, and it affects the choices and decisions made by the user.

[0308] The system for implementing this invention has functions for mutual conversion and generation of voice, document, and visual data. First, the user may record voice information using a smart device, and the terminal may transmit the voice information to the server. The server utilizes acoustic analysis technology to convert the voice information into document information and transmits the document information to the user's terminal. Thereafter, the user can edit the received document information as needed.

[0309] Next, the user sends document information to the server, which converts that document information into audio information using sound synthesis technology. This audio information is then sent back to the user's terminal, where the user can review or play the audio information.

[0310] Thirdly, the user sends a request for visual data generation from their device to the server. Based on the user's instructions, the server uses a generation AI model to generate visual data according to the specified theme or style, and sends the generated visual data to the user's device. This visual data can then be viewed, saved, or shared by the user.

[0311] One concrete example is when a user records audio on their smartphone during a meeting and immediately converts it into meeting minutes as document information. Furthermore, users can play back the document information as audio, which can be useful for creating podcasts. It is also possible to convert children's stories from text to audio, generate visual data that matches the content, and create interactive content.

[0312] An example of a prompt is, "If a user speaks to their smartphone and says, 'Create an image of a natural landscape,' then a relevant image will be generated based on that instruction." Based on this prompt, the generation AI model can instantly generate visual data and provide it to the user.

[0313] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0314] Step 1:

[0315] The user records voice information using a smart device. The recorded voice information is stored in the device's application and prepared to be sent to a server. The input is the user's voice, and the output is digital voice data.

[0316] Step 2:

[0317] The terminal sends audio information to the server. The server converts the received audio data into document information using acoustic analysis technology. This process involves analyzing the acoustic waveform and identifying phonemes. The input is audio data, and the output is text data.

[0318] Step 3:

[0319] The server sends the generated document information to the terminal. The terminal provides an interface that allows the user to review the document information and edit the content as needed. The input is text data, and the output is editable text provided to the user.

[0320] Step 4:

[0321] The user sends the edited document information back to the server via their terminal. The server then uses sound synthesis technology to convert this document information into audio. At this stage, an audio waveform is generated from the text. The input is edited text data, and the output is audio data.

[0322] Step 5:

[0323] The server sends the generated audio information to the terminal. The terminal uses this audio information to provide the user with the function to play the audio. The input is audio data, and the output is the audio that the user hears.

[0324] Step 6:

[0325] The user sends a prompt message from their terminal to the server for generating visual data. The server generates the visual data using a generative AI model based on the prompt message. In this process, conceptual instructions are converted into concrete visual representations. The input is the prompt message, and the output is image data.

[0326] Step 7:

[0327] The server sends the generated visual data to the terminal. The terminal presents this visual data to the user and provides functions for saving and sharing it as needed. The input is image data, and the output is an image that the user can view.

[0328] 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.

[0329] This invention is a system that combines the generation and analysis of speech, text, and images with an emotion engine that recognizes user emotions. The system aims to provide a more personalized experience by optimizing content generation and the interface based on user emotions. Details are provided below.

[0330] Users input voice and text using an application on their device. This input information is sent from the device to the server. The server uses an emotion engine to analyze the user's emotions from this data and identify the type of emotion. This analysis result is used in the content generation process.

[0331] For example, if a user expresses positive emotions, the server can generate and provide cheerful music or images based on that information. On the other hand, if negative emotions are detected, the server can take appropriate action, such as suggesting content that promotes relaxation.

[0332] Furthermore, this emotional information also influences the customization of the user interface. For example, if a user is feeling stressed, the device may display a simpler interface and provide support to reduce the user's burden.

[0333] Furthermore, this system can dynamically adjust the style and theme of the generated images according to the user's emotions. As a result, the generated content is more in line with the user's psychological state.

[0334] As a concrete example, when a user wants a little positive inspiration during a work break, the system could analyze the user's current mood and generate and provide quotes or images appropriate to that state. This would allow the user to quickly reduce psychological stress and refresh their mind.

[0335] In this way, by combining emotion recognition with voice, text, and image generation and analysis capabilities, this system can provide users with a deeper level of personalized and innovative creative experience.

[0336] The following describes the processing flow.

[0337] Step 1:

[0338] The user launches the application on their device and selects either voice input or text input. When the user presses the record button, voice data recording begins, or text is entered into the text box.

[0339] Step 2:

[0340] The device temporarily stores the acquired audio data and reads the text data in real time. After recording is finished, or after text input is complete, it is ready to send the data to the server.

[0341] Step 3:

[0342] The terminal compresses the audio data and sends it to the server, and similarly sends the text data. A secure communication protocol (e.g., HTTPS) is used for this.

[0343] Step 4:

[0344] The server passes the received audio data to the speech recognition engine, which converts the audio into text data. Simultaneously, the emotion engine analyzes both the audio and the transmitted text data.

[0345] Step 5:

[0346] The server's emotion engine identifies the user's current emotional state using features extracted from text and audio data. Based on these results, it then begins generating optimized content.

[0347] Step 6:

[0348] The server generates user-appropriate content (audio, images, etc.) based on analyzed emotions. For example, when a user is feeling happy, it selects content with a cheerful theme.

[0349] Step 7:

[0350] The server sends the generated content to the terminal. In the case of audio data, it is converted to a playable format, and in the case of image data, it is sent according to its resolution and format.

[0351] Step 8:

[0352] The device presents the received data to the user. Specifically, it plays audio data through the speaker and displays images on the screen. Furthermore, the user interface can be customized with color schemes and layouts that respond to emotions.

[0353] (Example 2)

[0354] 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".

[0355] In today's information society, a challenge is the insufficient personalization based on emotions when users engage with diverse digital content. Conventional technologies have struggled to dynamically generate content that considers the user's psychological state, often failing to provide a practical experience. This has limited the user experience and made it difficult to meet individual needs.

[0356] 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.

[0357] In this invention, the server includes means for receiving diverse data input from information devices and analyzing the data to identify the user's emotions; means for dynamically generating content based on the user's emotions using a predetermined artificial intelligence model; and means for displaying or playing the generated content in a manner that adapts to the user's psychological state. This makes it possible to achieve deep personalization that is in line with the user's emotions and to provide an individually optimized experience.

[0358] "Audio data" refers to sound information represented in digital format, and is primarily used for recording and playing back audio.

[0359] "Text data" refers to information represented by strings of characters, in a format that can be processed and stored by a computer.

[0360] "Information equipment" refers to devices used for inputting, processing, and outputting data, and includes electronic devices such as computers and smartphones.

[0361] A "generative AI model" refers to artificial intelligence technology that has the ability to generate new data and content based on large datasets.

[0362] "User emotions" refers to the psychological state estimated from the user's input data using emotion recognition technology.

[0363] "Content" refers to information and media consumed by users, and includes a variety of forms such as music, images, and text.

[0364] This invention is a system that generates and analyzes audio data, text data, and image data to provide personalized content tailored to the user's emotional state. The system uses a terminal and a server as its main components.

[0365] First, the user inputs voice or text through an application on their device. This device is connected to input devices such as a microphone and keyboard. The input data is then transmitted to a server via a communication network.

[0366] The server processes the received data through an emotion recognition engine to analyze the user's emotions. This process uses a specific algorithm to perform highly accurate emotion analysis. Based on the analysis results, a predetermined generative AI model (for example, a model using a Deep Learning framework) is used to generate content that matches the user's emotions.

[0367] The generated content is delivered to the user via the device. The user interface also dynamically changes in response to the user's emotions, incorporating features to improve usability.

[0368] As a concrete example, if a user expresses positive emotions, the system could input a prompt message such as "Please tell me a quote to make you smile more today" into an AI model, which could then generate appropriate quotes and images.

[0369] This system allows users to receive content tailored to their individual psychological state, enabling them to enjoy a better experience.

[0370] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0371] Step 1:

[0372] Users input voice or text data using applications on their devices. This includes recording their voice using the device's microphone or typing text using the keyboard. The input data is saved on the device as a temporary file.

[0373] Step 2:

[0374] The terminal sends the input voice or text data to the server. Transfer over the communication network primarily uses the HTTPS protocol. During transmission, the data is encoded in the appropriate format.

[0375] Step 3:

[0376] The server analyzes the received audio and text data. Using an emotion recognition engine, it applies natural language processing (NLP) and speech analysis algorithms to the data to identify the user's emotions. As a result of the analysis, the user's emotional state (e.g., joy, surprise, stress) is generated.

[0377] Step 4:

[0378] The server inputs a prompt message into the generative AI model based on the obtained sentiment analysis results. This prompt message includes a request such as, "Generate appropriate content based on the user's sentiment." The generative AI model (for example, a large-scale language model) is executed, and content is output.

[0379] Step 5:

[0380] The device receives content generated from the server and provides it to the user. The content is played or displayed as music, text, or images. The display interface also adapts to the user's emotional state, improving user interaction.

[0381] Step 6:

[0382] Users engage with the presented content and input their feedback into their device. This feedback is sent to the server as data that contributes to further system improvements and the learning process.

[0383] (Application Example 2)

[0384] 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."

[0385] In recent years, there has been a growing demand for personalizing information and content based on user emotions, but existing systems are unable to adequately meet this need. In particular, there is a lack of technology that can accurately analyze diverse user emotions and provide appropriate media content based on the results. This has led to decreased user satisfaction, and there is a need to realize content delivery based on more sophisticated emotion recognition.

[0386] 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.

[0387] In this invention, the server includes means for receiving audio data and performing a process to convert the audio data into text data; means for analyzing the user's emotions and performing a process to dynamically present content based on those emotions; and means for recommending and providing media content that corresponds to the emotions to the user. This makes it possible to provide personalized content that accurately reflects the user's emotional state.

[0388] "Audio data" refers to data obtained by digitizing audio signals, which is a conversion of sound information into a format that can be processed numerically.

[0389] "Text data" refers to data composed of characters and symbols, which is human language information in a format that can be processed by a computer.

[0390] An "image" is data that represents visual information, a visual representation expressed in digital format as a collection of pixels.

[0391] "User emotions" refer to information that indicates the feelings and psychological state a user is experiencing at a given point in time, and are subjective states inferred from voice and text data.

[0392] "Content" refers to a collection of digital information displayed or played by a user, provided in formats such as images, audio, text, and video.

[0393] "Sentiment analysis" is a data analysis technique performed by algorithms that identifies a user's emotional state using voice, text, or other data.

[0394] "Presentation" refers to the act of showing information or data to a user visually or audibly, and is a representation of data carried out through a user interface.

[0395] "Recommendation" is the process of selecting and presenting relevant content based on the user's preferences and circumstances, and is an information delivery method aimed at improving the user experience.

[0396] This system consists of a server for analyzing emotions and a terminal that receives user input. The terminal receives voice and text data from the user and transfers this data to the server. The server converts the voice data into text data using an advanced speech recognition algorithm. In this process, it utilizes existing speech recognition libraries (e.g., TextBlob). Furthermore, a generative AI model analyzes the user's emotions and generates or recommends appropriate content based on those emotions.

[0397] The server uses an emotion analysis engine to generate media content that responds to the user's emotions. This ensures that the digital content provided to the user matches their current emotions, creating a more immersive experience. Recommended content is sent to the device and displayed or played on the user's device.

[0398] For example, if a user types "I'm tired today," the server analyzes the user's emotion as "fatigue" and provides relaxation music or relaxing images to the device. An example of an input prompt for the generating AI model could be: "The user typed 'I'm tired today.' Determine the user's emotion from this input and recommend a video that matches that emotion."

[0399] By structuring the content in this way, it becomes possible to provide content that matches the user's emotional state and realize a more personalized user experience.

[0400] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0401] Step 1:

[0402] The user inputs voice or text into the device. The input voice data is stored in its original format, while the text data is stored as a string on the device.

[0403] Step 2:

[0404] The terminal converts the input audio data into a digital signal and then applies a speech recognition algorithm to convert it into text data. This converted text data is then prepared to be transferred to the server.

[0405] Step 3:

[0406] The terminal transmits text data converted from voice data or text data directly entered by the user to the server via the communication network. During this process, the data is converted to an appropriate format and transferred according to a specific communication protocol.

[0407] Step 4:

[0408] The server performs sentiment analysis on the received text data. The sentiment analysis engine analyzes the text data and identifies the user's emotional state (e.g., positive, negative, neutral). Based on this analysis, it selects an appropriate generative AI model.

[0409] Step 5:

[0410] The server generates or selects content based on the identified user's emotions. Content such as images, music, and videos that match the emotions are generated by a generation AI model. The generated content is packaged as components and prepared for display.

[0411] Step 6:

[0412] The server sends the generated content to the terminal and presents it to the user in an appropriate format. The terminal analyzes the received content data and displays or plays it in the way that is easiest for the user to understand.

[0413] Through this series of processes, users can experience personalized content on their devices that responds to their emotions.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] [Third Embodiment]

[0418] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0419] 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.

[0420] 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).

[0421] 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.

[0422] 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.

[0423] 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).

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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".

[0430] This invention is a system for efficiently generating and analyzing speech, text, and images. The system aims to support users in various creative activities using smart devices. Specifically, it is configured as follows:

[0431] Users install the system's application on devices such as smartphones and tablets. When a user records audio, the device sends the audio data to the server. The server uses advanced speech recognition algorithms to convert the received audio data into text. The converted text data is returned to the device, allowing the user to immediately review and save it.

[0432] Furthermore, when a user inputs text data, the device sends that data to the server. The server uses speech synthesis technology to convert the text into speech and sends it back to the device. This allows users to use the audio playback function to create audiobooks and provide content for visually impaired individuals.

[0433] Users can further select image generation options within the app. After entering instructions regarding the theme and style to use, the device sends these instructions to the server. The server uses an image generation algorithm to create visual content based on the specified conditions and sends the generated image to the device. Users can then review the received image and save or share it as needed.

[0434] For example, a user could record audio during a meeting, instantly transcribe it into text, and save it as meeting minutes. Additionally, when creating children's picture books, the system could generate audio from text and then create corresponding illustrations, easily creating interactive content.

[0435] This system provides users with an innovative and efficient creative experience through its voice, text, and image conversion and generation capabilities.

[0436] The following describes the processing flow.

[0437] Step 1:

[0438] The user launches the application on their device and selects the function for voice input. When the user taps the voice input start button, recording begins.

[0439] Step 2:

[0440] The device buffers the audio data recorded by the user in real time and temporarily stores the data. When the recording end button is pressed, the audio data is finally saved and prepared for transmission to the server.

[0441] Step 3:

[0442] The device compresses the audio data and sends it to the server via the internet connection. HTTPS is used as the communication protocol to ensure secure data transmission.

[0443] Step 4:

[0444] The server analyzes the audio data received from the terminal and passes it to the speech recognition engine. The engine then begins the analysis to convert the audio into text.

[0445] Step 5:

[0446] The server retrieves the text data generated as a result of the analysis and formats it appropriately. The converted text data is then formatted to be easy for the user to use.

[0447] Step 6:

[0448] The server sends the converted text data back to the terminal. The data is then securely transmitted again using the HTTPS protocol.

[0449] Step 7:

[0450] The device displays the received text data on the screen. Users can review, edit, save, or share the displayed text with other applications.

[0451] Step 8:

[0452] When a user wants to convert text data into speech, the speech synthesis process begins when they send a request from their device to the server.

[0453] Step 9:

[0454] The server receives the transmitted text data and generates speech data using a speech synthesis engine. During this process, parameters such as the language and tone of the speech are adjusted according to the user's settings.

[0455] Step 10:

[0456] The server sends the generated audio data to the terminal. The user can then play and verify the audio through the terminal's speaker.

[0457] (Example 1)

[0458] 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."

[0459] In today's digital environment, methods for the rapid and accurate conversion and generation of audio, text, and visual information are particularly needed in creative industries and education. However, existing systems often fall short in terms of accuracy and efficiency, and fail to provide intuitive and user-friendly interfaces. This invention aims to efficiently perform the interconversion of audio, text, and visual information and improve the user experience.

[0460] 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.

[0461] In this invention, the server includes means for receiving audio information and performing a process to convert the audio information into text information, means for receiving text information and performing a process to convert the text information into audio information, and means for generating visual information using a generative AI model. This enables rapid and accurate mutual conversion of audio, text, and visual information, allowing users to engage in creative activities intuitively and effectively.

[0462] "Audio information" refers to data obtained by converting speech or sounds, which are expressed as sound waves, into a digital format.

[0463] "Character information" refers to a combination of symbols expressed as text, and is typically data represented in digital format using character codes.

[0464] "Visual information" refers to data that represents information perceived through sight, such as images and videos, in digital format.

[0465] "Means" refers to methods, devices, or system components established to achieve a specific objective.

[0466] A "generative AI model" is a machine learning model trained to perform generative tasks using artificial intelligence technology, and is a model that has the ability to generate creative output based on a specific input.

[0467] A "protocol" is a set of procedures and rules that define how communication takes place on a computer network.

[0468] "Secure" refers to a state or technology that protects access so that only authorized individuals can access it.

[0469] An "information processing device" is a computer system or device used to perform processing such as analysis, transformation, and generation of data and information.

[0470] This invention is a system for the efficient conversion and generation of audio, text, and visual information. Users install and use the application on a smart device with an internet connection (e.g., a smartphone or tablet).

[0471] When a user generates voice information, the device uses its built-in microphone to collect the audio and sends the data to a server via a secure protocol (e.g., HTTPS). The server then uses a speech understanding algorithm (e.g., a speech recognition API) to convert the voice information into text. This makes it possible, for example, to accurately transcribe speech during a meeting and save it as meeting minutes.

[0472] Furthermore, when a user inputs text information, the terminal sends the text data to the server. The server uses speech synthesis technology (e.g., a speech synthesis engine) to convert the text information into speech information. This functionality enables the creation of audiobooks and the provision of audio content for people with visual impairments.

[0473] Furthermore, if the user wants to generate visual information, the device sends a prompt message to the server based on the user's instructions. The server uses a generative AI model (e.g., an image generation algorithm) to generate visual information according to the specified subject and style. For example, using a prompt message such as "landscape of blue sky and grassland" can generate a specific image based on the instructions.

[0474] This system provides users with an intuitive and effective experience in the mutual conversion and generation of speech, text, and visual information.

[0475] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0476] Step 1:

[0477] The user launches an application on the device and records audio. The audio data collected using the built-in microphone is temporarily stored on the device. This audio information is then used as input.

[0478] Step 2:

[0479] The terminal sends the recorded audio data to the server using a secure protocol. The input data is transferred securely and prepared for processing on the server.

[0480] Step 3:

[0481] The server converts the received audio data into text information using an advanced speech understanding algorithm. This process transforms the audio information into text, which is then output as highly accurate text data.

[0482] Step 4:

[0483] The server sends the converted text information back to the terminal. The terminal receives the data and displays it on the user interface. This allows the user to see the text generated from the speech.

[0484] Step 5:

[0485] The user can make any necessary corrections based on the displayed text information and then resend that information to the server from their device. The corrected text information becomes the new input data.

[0486] Step 6:

[0487] The server converts the received text information back into speech information using speech synthesis technology. By utilizing a generative AI model in this process, the text is output as speech data in a specific speech style specified by the user.

[0488] Step 7:

[0489] The generated audio information is sent from the server to the terminal. The terminal plays it back, and the user can hear the result.

[0490] Step 8:

[0491] When a user wants to generate visual information, they enter a prompt message containing a specific theme or style, which is then sent from the terminal to the server. This serves as input data for image generation.

[0492] Step 9:

[0493] The server generates visual information based on prompts using a generative AI model. The image generation algorithm outputs a specific image according to the specified conditions.

[0494] Step 10:

[0495] The generated visual information is sent to the device and displayed for the user to review, save, and share. The user can then use this information to complete specific creative tasks.

[0496] (Application Example 1)

[0497] 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."

[0498] To support the creative activities of diverse users, a system is needed that can efficiently generate and edit audio, text, and images bidirectionally, and freely customize them to individual preferences. However, existing technologies are insufficient in terms of mutual conversion between each medium and flexible customization of generated content, making it difficult to meet user needs.

[0499] 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.

[0500] In this invention, the server includes means for receiving audio information and performing a process to convert the audio information into document information, means for receiving document information and performing a process to convert the document information into audio information, and means for generating visual data according to a specified concept or expression based on user instructions. This enables the mutual conversion of audio, text, and images, and makes it possible to flexibly generate diverse creative content in response to the individual requests of users.

[0501] "Audio information" refers to auditory data recorded or acquired through microphones or other sound acquisition devices.

[0502] "Document information" refers to data expressed in text format, which is a string of characters that serves a semantic or information-transmitting purpose.

[0503] "Visual data" refers to data that is visually represented, such as digital images and videos, and is used to convey visual information to users.

[0504] An "information system" is a technological infrastructure consisting of software and hardware for collecting, storing, processing, and transmitting data.

[0505] "Individual preferences" refer to the characteristics of individual users' tastes and preferences, which influence the choices and decisions they make.

[0506] The system for carrying out this invention includes functions for the mutual conversion and generation of audio, document, and visual data. First, the user may record audio information using a smart device and send that audio information to a server. The server uses acoustic analysis technology to convert the audio information into document information and sends that document information to the user's device. The user can then edit the received document information as needed.

[0507] Next, the user sends document information to the server, which converts that document information into audio information using sound synthesis technology. This audio information is then sent back to the user's terminal, where the user can review or play the audio information.

[0508] Thirdly, the user sends a request for visual data generation from their device to the server. Based on the user's instructions, the server uses a generation AI model to generate visual data according to the specified theme or style, and sends the generated visual data to the user's device. This visual data can then be viewed, saved, or shared by the user.

[0509] One concrete example is when a user records audio on their smartphone during a meeting and immediately converts it into meeting minutes as document information. Furthermore, users can play back the document information as audio, which can be useful for creating podcasts. It is also possible to convert children's stories from text to audio, generate visual data that matches the content, and create interactive content.

[0510] An example of a prompt is, "If a user speaks to their smartphone and says, 'Create an image of a natural landscape,' then a relevant image will be generated based on that instruction." Based on this prompt, the generation AI model can instantly generate visual data and provide it to the user.

[0511] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0512] Step 1:

[0513] The user records voice information using a smart device. The recorded voice information is stored in the device's application and prepared to be sent to a server. The input is the user's voice, and the output is digital voice data.

[0514] Step 2:

[0515] The terminal sends audio information to the server. The server converts the received audio data into document information using acoustic analysis technology. This process involves analyzing the acoustic waveform and identifying phonemes. The input is audio data, and the output is text data.

[0516] Step 3:

[0517] The server sends the generated document information to the terminal. The terminal provides an interface that allows the user to review the document information and edit the content as needed. The input is text data, and the output is editable text provided to the user.

[0518] Step 4:

[0519] The user sends the edited document information back to the server via their terminal. The server then uses sound synthesis technology to convert this document information into audio information. At this stage, an audio waveform is generated from the text. The input is edited text data, and the output is audio data.

[0520] Step 5:

[0521] The server sends the generated audio information to the terminal. The terminal uses this audio information to provide the user with the function to play the audio. The input is audio data, and the output is the audio heard by the user.

[0522] Step 6:

[0523] The user sends a prompt message from their terminal to the server for generating visual data. The server generates the visual data using a generative AI model based on the prompt message. In this process, conceptual instructions are converted into concrete visual representations. The input is the prompt message, and the output is image data.

[0524] Step 7:

[0525] The server sends the generated visual data to the terminal. The terminal presents this visual data to the user and provides functions to save and share it as needed. The input is image data, and the output is an image that the user can view.

[0526] 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.

[0527] This invention is a system that combines the generation and analysis of speech, text, and images with an emotion engine that recognizes user emotions. The system aims to provide a more personalized experience by optimizing content generation and the interface based on user emotions. Details are provided below.

[0528] Users input voice and text using an application on their device. This input information is sent from the device to the server. The server uses an emotion engine to analyze the user's emotions from this data and identify the type of emotion. This analysis result is used in the content generation process.

[0529] For example, if a user expresses positive emotions, the server can generate and provide cheerful music or images based on that information. On the other hand, if negative emotions are detected, the server can take appropriate action, such as suggesting content that promotes relaxation.

[0530] Furthermore, this emotional information also influences the customization of the user interface. For example, if a user is feeling stressed, the device may display a simpler interface and provide support to reduce the user's burden.

[0531] Furthermore, this system can dynamically adjust the style and theme of the generated images according to the user's emotions. As a result, the generated content is more in line with the user's psychological state.

[0532] As a concrete example, when a user wants a little positive inspiration during a work break, the system could analyze the user's current mood and generate and provide quotes or images appropriate to that state. This would allow the user to quickly reduce psychological stress and refresh their mind.

[0533] In this way, by combining emotion recognition with voice, text, and image generation and analysis capabilities, this system can provide users with a deeper level of personalized and innovative creative experience.

[0534] The following describes the processing flow.

[0535] Step 1:

[0536] The user launches the application on their device and selects either voice input or text input. When the user presses the record button, voice data recording begins, or text is entered into the text box.

[0537] Step 2:

[0538] The device temporarily stores the acquired audio data and reads the text data in real time. After recording is finished, or after text input is complete, it is ready to send the data to the server.

[0539] Step 3:

[0540] The terminal compresses the audio data and sends it to the server, and similarly sends the text data. A secure communication protocol (e.g., HTTPS) is used for this.

[0541] Step 4:

[0542] The server passes the received audio data to the speech recognition engine, which converts the audio into text data. Simultaneously, the emotion engine analyzes both the audio and the transmitted text data.

[0543] Step 5:

[0544] The server's emotion engine identifies the user's current emotional state using features extracted from text and audio data. Based on these results, it then begins generating optimized content.

[0545] Step 6:

[0546] The server generates user-appropriate content (audio, images, etc.) based on analyzed emotions. For example, when a user is feeling happy, it selects content with a cheerful theme.

[0547] Step 7:

[0548] The server sends the generated content to the terminal. In the case of audio data, it is converted to a playable format, and in the case of image data, it is sent according to its resolution and format.

[0549] Step 8:

[0550] The device presents the received data to the user. Specifically, it plays audio data through the speaker and displays images on the screen. Furthermore, the user interface can be customized with color schemes and layouts that respond to emotions.

[0551] (Example 2)

[0552] 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."

[0553] In today's information society, a challenge is the insufficient personalization based on emotions when users engage with diverse digital content. Conventional technologies have struggled to dynamically generate content that considers the user's psychological state, often failing to provide a practical experience. This has limited the user experience and made it difficult to meet individual needs.

[0554] 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.

[0555] In this invention, the server includes means for receiving diverse data input from information devices and analyzing the data to identify the user's emotions; means for dynamically generating content based on the user's emotions using a predetermined artificial intelligence model; and means for displaying or playing the generated content in a manner that adapts to the user's psychological state. This makes it possible to achieve deep personalization that is in line with the user's emotions and to provide an individually optimized experience.

[0556] "Audio data" refers to sound information represented in digital format, and is primarily used for recording and playing back audio.

[0557] "Text data" refers to information represented by strings of characters, in a format that can be processed and stored by a computer.

[0558] "Information equipment" refers to devices used for inputting, processing, and outputting data, and includes electronic devices such as computers and smartphones.

[0559] A "generative AI model" refers to artificial intelligence technology that has the ability to generate new data and content based on large datasets.

[0560] "User emotions" refers to the psychological state estimated from the user's input data using emotion recognition technology.

[0561] "Content" refers to information and media consumed by users, and includes a variety of forms such as music, images, and text.

[0562] This invention is a system that generates and analyzes audio data, text data, and image data to provide personalized content tailored to the user's emotional state. The system uses a terminal and a server as its main components.

[0563] First, the user inputs voice or text through an application on their device. This device is connected to input devices such as a microphone and keyboard. The input data is then transmitted to a server via a communication network.

[0564] The server processes the received data through an emotion recognition engine to analyze the user's emotions. This process uses a specific algorithm to perform highly accurate emotion analysis. Based on the analysis results, a predetermined generative AI model (for example, a model using a Deep Learning framework) is used to generate content that matches the user's emotions.

[0565] The generated content is delivered to the user via the device. The user interface also dynamically changes in response to the user's emotions, incorporating features to improve usability.

[0566] As a concrete example, if a user expresses positive emotions, the system could input a prompt message such as "Please tell me a quote to make you smile more today" into an AI model, which could then generate appropriate quotes and images.

[0567] This system allows users to receive content tailored to their individual psychological state, enabling them to enjoy a better experience.

[0568] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0569] Step 1:

[0570] Users input voice or text data using applications on their devices. This includes recording their voice using the device's microphone or typing text using the keyboard. The input data is saved on the device as a temporary file.

[0571] Step 2:

[0572] The terminal sends the input voice or text data to the server. Transfer over the communication network primarily uses the HTTPS protocol. During transmission, the data is encoded in the appropriate format.

[0573] Step 3:

[0574] The server analyzes the received audio and text data. Using an emotion recognition engine, it applies natural language processing (NLP) and speech analysis algorithms to the data to identify the user's emotions. As a result of the analysis, the user's emotional state (e.g., joy, surprise, stress) is generated.

[0575] Step 4:

[0576] The server inputs a prompt message into the generative AI model based on the obtained sentiment analysis results. This prompt message includes a request such as, "Generate appropriate content based on the user's sentiment." The generative AI model (for example, a large-scale language model) is executed, and content is output.

[0577] Step 5:

[0578] The device receives content generated from the server and provides it to the user. The content is played or displayed as music, text, or images. The display interface also adapts to the user's emotional state, improving user interaction.

[0579] Step 6:

[0580] Users engage with the presented content and input their feedback into their device. This feedback is sent to the server as data that contributes to further system improvements and the learning process.

[0581] (Application Example 2)

[0582] 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."

[0583] In recent years, there has been a growing demand for personalizing information and content based on user emotions, but existing systems are unable to adequately meet this need. In particular, there is a lack of technology that can accurately analyze diverse user emotions and provide appropriate media content based on the results. This has led to decreased user satisfaction, and there is a need to realize content delivery based on more sophisticated emotion recognition.

[0584] 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.

[0585] In this invention, the server includes means for receiving audio data and performing a process to convert the audio data into text data; means for analyzing the user's emotions and performing a process to dynamically present content based on those emotions; and means for recommending and providing media content that corresponds to the emotions to the user. This makes it possible to provide personalized content that accurately reflects the user's emotional state.

[0586] "Audio data" refers to data obtained by digitizing audio signals, which is a conversion of sound information into a format that can be processed as numerical data.

[0587] "Text data" refers to data composed of characters and symbols, which is human language information in a format that can be processed by a computer.

[0588] An "image" is data that represents visual information, a visual representation expressed in digital format as a collection of pixels.

[0589] "User emotions" refer to information that indicates the feelings and psychological state a user is experiencing at a given point in time, and are subjective states inferred from voice and text data.

[0590] "Content" refers to a collection of digital information displayed or played by a user, provided in formats such as images, audio, text, and video.

[0591] "Sentiment analysis" is a data analysis technique performed by algorithms that identifies a user's emotional state using voice, text, or other data.

[0592] "Presentation" refers to the act of showing information or data to a user visually or audibly, and is a representation of data carried out through a user interface.

[0593] "Recommendation" is the process of selecting and presenting relevant content based on the user's preferences and circumstances, and is an information delivery method aimed at improving the user experience.

[0594] This system consists of a server for analyzing emotions and a terminal that receives user input. The terminal receives voice and text data from the user and transfers this data to the server. The server converts the voice data into text data using an advanced speech recognition algorithm. In this process, it utilizes existing speech recognition libraries (e.g., TextBlob). Furthermore, a generative AI model analyzes the user's emotions and generates or recommends appropriate content based on those emotions.

[0595] The server uses an emotion analysis engine to generate media content that responds to the user's emotions. This ensures that the digital content provided to the user matches their current emotions, creating a more immersive experience. Recommended content is sent to the device and displayed or played on the user's device.

[0596] For example, if a user types "I'm tired today," the server analyzes the user's emotion as "fatigue" and provides relaxation music or relaxing images to the device. An example of an input prompt for the generating AI model could be: "The user typed 'I'm tired today.' Determine the user's emotion from this input and recommend a video that matches that emotion."

[0597] By structuring the content in this way, it becomes possible to provide content that matches the user's emotional state and realize a more personalized user experience.

[0598] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0599] Step 1:

[0600] The user inputs voice or text into the device. The input voice data is stored in its original format, while the text data is stored as a string on the device.

[0601] Step 2:

[0602] The terminal converts the input audio data into a digital signal and then applies a speech recognition algorithm to convert it into text data. This converted text data is then prepared to be transferred to the server.

[0603] Step 3:

[0604] The terminal transmits text data converted from voice data or text data directly entered by the user to the server via the communication network. During this process, the data is converted to an appropriate format and transferred according to a specific communication protocol.

[0605] Step 4:

[0606] The server performs sentiment analysis on the received text data. The sentiment analysis engine analyzes the text data and identifies the user's emotional state (e.g., positive, negative, neutral). Based on this analysis, it selects an appropriate generative AI model.

[0607] Step 5:

[0608] The server generates or selects content based on the identified user's emotions. Content such as images, music, and videos that match the emotions are generated by a generation AI model. The generated content is packaged as components and prepared for display.

[0609] Step 6:

[0610] The server sends the generated content to the terminal and presents it to the user in an appropriate format. The terminal analyzes the received content data and displays or plays it in the way that is easiest for the user to understand.

[0611] Through this series of processes, users can experience personalized content on their devices that responds to their emotions.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] [Fourth Embodiment]

[0616] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0617] 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.

[0618] 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).

[0619] 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.

[0620] 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.

[0621] 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).

[0622] 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.

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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".

[0629] This invention is a system for efficiently generating and analyzing speech, text, and images. The system aims to support users in various creative activities using smart devices. Specifically, it is configured as follows:

[0630] Users install the system's application on devices such as smartphones and tablets. When a user records audio, the device sends the audio data to the server. The server uses advanced speech recognition algorithms to convert the received audio data into text. The converted text data is returned to the device, allowing the user to immediately review and save it.

[0631] Furthermore, when a user inputs text data, the device sends that data to the server. The server uses speech synthesis technology to convert the text into speech and sends it back to the device. This allows users to use the audio playback function to create audiobooks and provide content for visually impaired individuals.

[0632] Users can further select image generation options within the app. After entering instructions regarding the theme and style to use, the device sends these instructions to the server. The server uses an image generation algorithm to create visual content based on the specified conditions and sends the generated image to the device. Users can then review the received image and save or share it as needed.

[0633] For example, a user could record audio during a meeting, instantly transcribe it into text, and save it as meeting minutes. Additionally, when creating children's picture books, the system could generate audio from text and then create corresponding illustrations, easily creating interactive content.

[0634] This system provides users with an innovative and efficient creative experience through its voice, text, and image conversion and generation capabilities.

[0635] The following describes the processing flow.

[0636] Step 1:

[0637] The user launches the application on their device and selects the function for voice input. When the user taps the voice input start button, recording begins.

[0638] Step 2:

[0639] The device buffers the audio data recorded by the user in real time and temporarily stores the data. When the recording end button is pressed, the audio data is finally saved and prepared for transmission to the server.

[0640] Step 3:

[0641] The device compresses the audio data and sends it to the server via the internet connection. HTTPS is used as the communication protocol to ensure secure data transmission.

[0642] Step 4:

[0643] The server analyzes the audio data received from the terminal and passes it to the speech recognition engine. The engine then begins the analysis to convert the audio into text.

[0644] Step 5:

[0645] The server retrieves the text data generated as a result of the analysis and formats it appropriately. The converted text data is then formatted to be easy for the user to use.

[0646] Step 6:

[0647] The server sends the converted text data back to the terminal. The data is then securely transmitted again using the HTTPS protocol.

[0648] Step 7:

[0649] The device displays the received text data on the screen. Users can review, edit, save, or share the displayed text with other applications.

[0650] Step 8:

[0651] When a user wants to convert text data into speech, the speech synthesis process begins when they send a request from their device to the server.

[0652] Step 9:

[0653] The server receives the transmitted text data and generates speech data using a speech synthesis engine. During this process, parameters such as the language and tone of the speech are adjusted according to the user's settings.

[0654] Step 10:

[0655] The server sends the generated audio data to the terminal. The user can then play and verify the audio through the terminal's speaker.

[0656] (Example 1)

[0657] 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".

[0658] In today's digital environment, methods for the rapid and accurate conversion and generation of audio, text, and visual information are particularly needed in creative industries and education. However, existing systems often fall short in terms of accuracy and efficiency, and fail to provide intuitive and user-friendly interfaces. This invention aims to efficiently perform the interconversion of audio, text, and visual information and improve the user experience.

[0659] 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.

[0660] In this invention, the server includes means for receiving audio information and performing a process to convert the audio information into text information, means for receiving text information and performing a process to convert the text information into audio information, and means for generating visual information using a generative AI model. This enables rapid and accurate mutual conversion of audio, text, and visual information, allowing users to engage in creative activities intuitively and effectively.

[0661] "Audio information" refers to data obtained by converting speech or sounds, which are expressed as sound waves, into a digital format.

[0662] "Character information" refers to a combination of symbols expressed as text, and is typically data represented in digital format using character codes.

[0663] "Visual information" refers to data that represents information perceived through sight, such as images and videos, in digital format.

[0664] "Means" refers to methods, devices, or system components established to achieve a specific objective.

[0665] A "generative AI model" is a machine learning model trained to perform generative tasks using artificial intelligence technology, and is a model that has the ability to generate creative output based on a specific input.

[0666] A "protocol" is a set of procedures and rules that define how communication takes place on a computer network.

[0667] "Secure" refers to a state or technology that protects access so that only authorized individuals can access it.

[0668] An "information processing device" is a computer system or device used to perform processing such as analysis, transformation, and generation of data and information.

[0669] This invention is a system for the efficient conversion and generation of audio, text, and visual information. Users install and use the application on a smart device with an internet connection (e.g., a smartphone or tablet).

[0670] When a user generates voice information, the device uses its built-in microphone to collect the audio and sends the data to a server via a secure protocol (e.g., HTTPS). The server then uses a speech understanding algorithm (e.g., a speech recognition API) to convert the voice information into text. This makes it possible, for example, to accurately transcribe speech during a meeting and save it as meeting minutes.

[0671] Furthermore, when a user inputs text information, the terminal sends the text data to the server. The server uses speech synthesis technology (e.g., a speech synthesis engine) to convert the text information into speech information. This functionality enables the creation of audiobooks and the provision of audio content for people with visual impairments.

[0672] Furthermore, if the user wants to generate visual information, the device sends a prompt message to the server based on the user's instructions. The server uses a generative AI model (e.g., an image generation algorithm) to generate visual information according to the specified subject and style. For example, using a prompt message such as "landscape of blue sky and grassland" can generate a specific image based on the instructions.

[0673] This system provides users with an intuitive and effective experience in the mutual conversion and generation of speech, text, and visual information.

[0674] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0675] Step 1:

[0676] The user launches an application on the device and records audio. The audio data collected using the built-in microphone is temporarily stored on the device. This audio information is then used as input.

[0677] Step 2:

[0678] The terminal sends the recorded audio data to the server using a secure protocol. The input data is transferred securely and prepared for processing on the server.

[0679] Step 3:

[0680] The server converts the received audio data into text information using an advanced speech understanding algorithm. This process transforms the audio information into text, which is then output as highly accurate text data.

[0681] Step 4:

[0682] The server sends the converted text information back to the terminal. The terminal receives the data and displays it on the user interface. This allows the user to see the text generated from the speech.

[0683] Step 5:

[0684] The user can make any necessary corrections based on the displayed text information and then resend that information to the server from their device. The corrected text information becomes the new input data.

[0685] Step 6:

[0686] The server converts the received text information back into speech information using speech synthesis technology. By utilizing a generative AI model in this process, the text is output as speech data in a specific speech style specified by the user.

[0687] Step 7:

[0688] The generated audio information is sent from the server to the terminal. The terminal plays it back, and the user can hear the result.

[0689] Step 8:

[0690] When a user wants to generate visual information, they enter a prompt message containing a specific theme or style, which is then sent from the terminal to the server. This serves as input data for image generation.

[0691] Step 9:

[0692] The server generates visual information based on prompts using a generative AI model. The image generation algorithm outputs a specific image according to the specified conditions.

[0693] Step 10:

[0694] The generated visual information is sent to the device and displayed for the user to review, save, and share. The user can then use this information to complete specific creative tasks.

[0695] (Application Example 1)

[0696] 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".

[0697] To support the creative activities of diverse users, a system is needed that can efficiently generate and edit audio, text, and images bidirectionally, and freely customize them to individual preferences. However, existing technologies are insufficient in terms of mutual conversion between each medium and flexible customization of generated content, making it difficult to meet user needs.

[0698] 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.

[0699] In this invention, the server includes means for receiving audio information and performing a process to convert the audio information into document information, means for receiving document information and performing a process to convert the document information into audio information, and means for generating visual data according to a specified concept or expression based on user instructions. This enables the mutual conversion of audio, text, and images, and makes it possible to flexibly generate diverse creative content in response to the individual requests of users.

[0700] "Audio information" refers to auditory data recorded or acquired through microphones or other sound acquisition devices.

[0701] "Document information" refers to data expressed in text format, which is a string of characters that serves a semantic or information-transmitting purpose.

[0702] "Visual data" refers to data that is visually represented, such as digital images and videos, and is used to convey visual information to users.

[0703] An "information system" is a technological infrastructure consisting of software and hardware for collecting, storing, processing, and transmitting data.

[0704] "Individual preferences" refer to the characteristics of individual users' tastes and preferences, which influence the choices and decisions they make.

[0705] The system for carrying out this invention includes functions for the mutual conversion and generation of audio, document, and visual data. First, the user may record audio information using a smart device and send that audio information to a server. The server uses acoustic analysis technology to convert the audio information into document information and sends that document information to the user's device. The user can then edit the received document information as needed.

[0706] Next, the user sends document information to the server, which converts that document information into audio information using sound synthesis technology. This audio information is then sent back to the user's terminal, where the user can review or play the audio information.

[0707] Thirdly, the user sends a request for visual data generation from their device to the server. Based on the user's instructions, the server uses a generation AI model to generate visual data according to the specified theme or style, and sends the generated visual data to the user's device. This visual data can then be viewed, saved, or shared by the user.

[0708] One concrete example is when a user records audio on their smartphone during a meeting and immediately converts it into meeting minutes as document information. Furthermore, users can play back the document information as audio, which can be useful for creating podcasts. It is also possible to convert children's stories from text to audio, generate visual data that matches the content, and create interactive content.

[0709] An example of a prompt is, "If a user speaks to their smartphone and says, 'Create an image of a natural landscape,' then a relevant image will be generated based on that instruction." Based on this prompt, the generation AI model can instantly generate visual data and provide it to the user.

[0710] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0711] Step 1:

[0712] The user records voice information using a smart device. The recorded voice information is stored in the device's application and prepared to be sent to a server. The input is the user's voice, and the output is digital voice data.

[0713] Step 2:

[0714] The terminal sends audio information to the server. The server converts the received audio data into document information using acoustic analysis technology. This process involves analyzing the acoustic waveform and identifying phonemes. The input is audio data, and the output is text data.

[0715] Step 3:

[0716] The server sends the generated document information to the terminal. The terminal provides an interface that allows the user to review the document information and edit the content as needed. The input is text data, and the output is editable text provided to the user.

[0717] Step 4:

[0718] The user sends the edited document information back to the server via their terminal. The server then uses sound synthesis technology to convert this document information into audio information. At this stage, an audio waveform is generated from the text. The input is edited text data, and the output is audio data.

[0719] Step 5:

[0720] The server sends the generated audio information to the terminal. The terminal uses this audio information to provide the user with the function to play the audio. The input is audio data, and the output is the audio heard by the user.

[0721] Step 6:

[0722] The user sends a prompt message from their terminal to the server for generating visual data. The server generates the visual data using a generative AI model based on the prompt message. In this process, conceptual instructions are converted into concrete visual representations. The input is the prompt message, and the output is image data.

[0723] Step 7:

[0724] The server sends the generated visual data to the terminal. The terminal presents this visual data to the user and provides functions to save and share it as needed. The input is image data, and the output is an image that the user can view.

[0725] 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.

[0726] This invention is a system that combines the generation and analysis of speech, text, and images with an emotion engine that recognizes user emotions. The system aims to provide a more personalized experience by optimizing content generation and the interface based on user emotions. Details are provided below.

[0727] Users input voice and text using an application on their device. This input information is sent from the device to the server. The server uses an emotion engine to analyze the user's emotions from this data and identify the type of emotion. This analysis result is used in the content generation process.

[0728] For example, if a user expresses positive emotions, the server can generate and provide cheerful music or images based on that information. On the other hand, if negative emotions are detected, the server can take appropriate action, such as suggesting content that promotes relaxation.

[0729] Furthermore, this emotional information also influences the customization of the user interface. For example, if a user is feeling stressed, the device may display a simpler interface and provide support to reduce the user's burden.

[0730] Furthermore, this system can dynamically adjust the style and theme of the generated images according to the user's emotions. As a result, the generated content is more in line with the user's psychological state.

[0731] As a concrete example, when a user wants a little positive inspiration during a work break, the system could analyze the user's current mood and generate and provide quotes or images appropriate to that state. This would allow the user to quickly reduce psychological stress and refresh their mind.

[0732] In this way, by combining emotion recognition with voice, text, and image generation and analysis capabilities, this system can provide users with a deeper level of personalized and innovative creative experience.

[0733] The following describes the processing flow.

[0734] Step 1:

[0735] The user launches the application on their device and selects either voice input or text input. When the user presses the record button, voice data recording begins, or text is entered into the text box.

[0736] Step 2:

[0737] The device temporarily stores the acquired audio data and reads the text data in real time. After recording is finished, or after text input is complete, it is ready to send the data to the server.

[0738] Step 3:

[0739] The terminal compresses the audio data and sends it to the server, and similarly sends the text data. A secure communication protocol (e.g., HTTPS) is used for this.

[0740] Step 4:

[0741] The server passes the received audio data to the speech recognition engine, which converts the audio into text data. Simultaneously, the emotion engine analyzes both the audio and the transmitted text data.

[0742] Step 5:

[0743] The server's emotion engine identifies the user's current emotional state using features extracted from text and audio data. Based on these results, it then begins generating optimized content.

[0744] Step 6:

[0745] The server generates user-appropriate content (audio, images, etc.) based on analyzed emotions. For example, when a user is feeling happy, it selects content with a cheerful theme.

[0746] Step 7:

[0747] The server sends the generated content to the terminal. In the case of audio data, it is converted to a playable format, and in the case of image data, it is sent according to its resolution and format.

[0748] Step 8:

[0749] The device presents the received data to the user. Specifically, it plays audio data through the speaker and displays images on the screen. Furthermore, the user interface can be customized with color schemes and layouts that respond to emotions.

[0750] (Example 2)

[0751] 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".

[0752] In today's information society, a challenge is the insufficient personalization based on emotions when users engage with diverse digital content. Conventional technologies have struggled to dynamically generate content that considers the user's psychological state, often failing to provide a practical experience. This has limited the user experience and made it difficult to meet individual needs.

[0753] 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.

[0754] In this invention, the server includes means for receiving diverse data input from information devices and analyzing the data to identify the user's emotions; means for dynamically generating content based on the user's emotions using a predetermined artificial intelligence model; and means for displaying or playing the generated content in a manner that adapts to the user's psychological state. This makes it possible to achieve deep personalization that is in line with the user's emotions and to provide an individually optimized experience.

[0755] "Audio data" refers to sound information represented in digital format, and is primarily used for recording and playing back audio.

[0756] "Text data" refers to information represented by strings of characters, in a format that can be processed and stored by a computer.

[0757] "Information equipment" refers to devices used for inputting, processing, and outputting data, and includes electronic devices such as computers and smartphones.

[0758] A "generative AI model" refers to artificial intelligence technology that has the ability to generate new data and content based on large datasets.

[0759] "User emotions" refers to the psychological state estimated from the user's input data using emotion recognition technology.

[0760] "Content" refers to information and media consumed by users, and includes a variety of forms such as music, images, and text.

[0761] This invention is a system that generates and analyzes audio data, text data, and image data to provide personalized content tailored to the user's emotional state. The system uses a terminal and a server as its main components.

[0762] First, the user inputs voice or text through an application on their device. This device is connected to input devices such as a microphone and keyboard. The input data is then transmitted to a server via a communication network.

[0763] The server processes the received data through an emotion recognition engine to analyze the user's emotions. This process uses a specific algorithm to perform highly accurate emotion analysis. Based on the analysis results, a predetermined generative AI model (for example, a model using a Deep Learning framework) is used to generate content that matches the user's emotions.

[0764] The generated content is delivered to the user via the device. The user interface also dynamically changes in response to the user's emotions, incorporating features to improve usability.

[0765] As a concrete example, if a user expresses positive emotions, the system could input a prompt message such as "Please tell me a quote to make you smile more today" into an AI model, which could then generate appropriate quotes and images.

[0766] This system allows users to receive content tailored to their individual psychological state, enabling them to enjoy a better experience.

[0767] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0768] Step 1:

[0769] Users input voice or text data using applications on their devices. This includes recording their voice using the device's microphone or typing text using the keyboard. The input data is saved on the device as a temporary file.

[0770] Step 2:

[0771] The terminal sends the input voice or text data to the server. Transfer over the communication network primarily uses the HTTPS protocol. During transmission, the data is encoded in the appropriate format.

[0772] Step 3:

[0773] The server analyzes the received audio and text data. Using an emotion recognition engine, it applies natural language processing (NLP) and speech analysis algorithms to the data to identify the user's emotions. As a result of the analysis, the user's emotional state (e.g., joy, surprise, stress) is generated.

[0774] Step 4:

[0775] The server inputs a prompt message into the generative AI model based on the obtained sentiment analysis results. This prompt message includes a request such as, "Generate appropriate content based on the user's sentiment." The generative AI model (for example, a large-scale language model) is executed, and content is output.

[0776] Step 5:

[0777] The device receives content generated from the server and provides it to the user. The content is played or displayed as music, text, or images. The display interface also adapts to the user's emotional state, improving user interaction.

[0778] Step 6:

[0779] Users engage with the presented content and input their feedback into their device. This feedback is sent to the server as data that contributes to further system improvements and the learning process.

[0780] (Application Example 2)

[0781] 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".

[0782] In recent years, there has been a growing demand for personalizing information and content based on user emotions, but existing systems are unable to adequately meet this need. In particular, there is a lack of technology that can accurately analyze diverse user emotions and provide appropriate media content based on the results. This has led to decreased user satisfaction, and there is a need to realize content delivery based on more sophisticated emotion recognition.

[0783] 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.

[0784] In this invention, the server includes means for receiving audio data and performing a process to convert the audio data into text data; means for analyzing the user's emotions and performing a process to dynamically present content based on those emotions; and means for recommending and providing media content that corresponds to the emotions to the user. This makes it possible to provide personalized content that accurately reflects the user's emotional state.

[0785] "Audio data" refers to data obtained by digitizing audio signals, which is a conversion of sound information into a format that can be processed numerically.

[0786] "Text data" refers to data composed of characters and symbols, which is human language information in a format that can be processed by a computer.

[0787] An "image" is data that represents visual information, a visual representation expressed in digital format as a collection of pixels.

[0788] "User emotions" refer to information that indicates the feelings and psychological state a user is experiencing at a given point in time, and are subjective states inferred from voice and text data.

[0789] "Content" refers to a collection of digital information displayed or played by a user, provided in formats such as images, audio, text, and video.

[0790] "Sentiment analysis" is a data analysis technique performed by algorithms that identifies a user's emotional state using voice, text, or other data.

[0791] "Presentation" refers to the act of showing information or data to a user visually or audibly, and is a representation of data carried out through a user interface.

[0792] "Recommendation" is the process of selecting and presenting relevant content based on the user's preferences and circumstances, and is an information delivery method aimed at improving the user experience.

[0793] This system consists of a server for analyzing emotions and a terminal that receives user input. The terminal receives voice and text data from the user and transfers this data to the server. The server converts the voice data into text data using an advanced speech recognition algorithm. In this process, it utilizes existing speech recognition libraries (e.g., TextBlob). Furthermore, a generative AI model analyzes the user's emotions and generates or recommends appropriate content based on those emotions.

[0794] The server uses an emotion analysis engine to generate media content that responds to the user's emotions. This ensures that the digital content provided to the user matches their current emotions, creating a more immersive experience. Recommended content is sent to the device and displayed or played on the user's device.

[0795] For example, if a user types "I'm tired today," the server analyzes the user's emotion as "fatigue" and provides relaxation music or relaxing images to the device. An example of an input prompt for the generating AI model could be: "The user typed 'I'm tired today.' Determine the user's emotion from this input and recommend a video that matches that emotion."

[0796] By structuring the content in this way, it becomes possible to provide content that matches the user's emotional state and realize a more personalized user experience.

[0797] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0798] Step 1:

[0799] The user inputs voice or text into the device. The input voice data is stored in its original format, while the text data is stored as a string on the device.

[0800] Step 2:

[0801] The terminal converts the input audio data into a digital signal and then applies a speech recognition algorithm to convert it into text data. This converted text data is then prepared to be transferred to the server.

[0802] Step 3:

[0803] The terminal transmits text data converted from voice data or text data directly entered by the user to the server via the communication network. During this process, the data is converted to an appropriate format and transferred according to a specific communication protocol.

[0804] Step 4:

[0805] The server performs sentiment analysis on the received text data. The sentiment analysis engine analyzes the text data and identifies the user's emotional state (e.g., positive, negative, neutral). Based on this analysis, it selects an appropriate generative AI model.

[0806] Step 5:

[0807] The server generates or selects content based on the identified user's emotions. Content such as images, music, and videos that match the emotions are generated by a generation AI model. The generated content is packaged as components and prepared for display.

[0808] Step 6:

[0809] The server sends the generated content to the terminal and presents it to the user in an appropriate format. The terminal analyzes the received content data and displays or plays it in the way that is easiest for the user to understand.

[0810] Through this series of processes, users can experience personalized content on their devices that responds to their emotions.

[0811] 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.

[0812] 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.

[0813] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0814] 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.

[0815] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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."

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0832] The following is further disclosed regarding the embodiments described above.

[0833] (Claim 1)

[0834] A means for receiving audio data and performing a process to convert said audio data into text data,

[0835] A means for receiving text data and performing a process to convert said text data into audio data,

[0836] A means for performing a process that generates images according to a specified theme or style based on user instructions,

[0837] A means for sending and receiving user input data via a communication network and for appropriately formatting said data,

[0838] A system that includes means for displaying or playing back generated data to present it to the user.

[0839] (Claim 2)

[0840] The system according to claim 1, which improves accuracy in converting audio data using a speech recognition algorithm.

[0841] (Claim 3)

[0842] The system according to claim 1, having a function to dynamically customize the generated image based on the user's request.

[0843] "Example 1"

[0844] (Claim 1)

[0845] A means for receiving audio information and performing a process to convert said audio information into text information,

[0846] A means for receiving text information and performing a process to convert said text information into audio information,

[0847] A means for performing a process that generates visual information according to a specified subject or format based on user instructions,

[0848] A means for sending and receiving user input information via communication means and for appropriately formatting said information,

[0849] A means for displaying or playing back the generated information to the user,

[0850] A means for transmitting audio collected from a terminal to an information processing device using a secure protocol,

[0851] A system that includes means for generating visual information using a generative AI model.

[0852] (Claim 2)

[0853] The system according to claim 1, which improves accuracy in converting speech information using a speech understanding algorithm.

[0854] (Claim 3)

[0855] The system according to claim 1, which has a function to dynamically modify the generated visual information based on the user's request.

[0856] "Application Example 1"

[0857] (Claim 1)

[0858] A means for receiving audio information and performing a process to convert said audio information into document information,

[0859] A means for receiving document information and performing a process to convert said document information into audio information,

[0860] A means for performing a process that generates visual data according to a specified concept or representation based on user instructions,

[0861] A means for sending and receiving user input information via an information system and for appropriately processing said information,

[0862] A means for displaying or playing back the generated information to the user,

[0863] A system that includes means for automatically generating visual or auditory content and organizing it according to individual preferences.

[0864] (Claim 2)

[0865] The system according to claim 1, which improves accuracy in the conversion of audio information by using acoustic analysis technology.

[0866] (Claim 3)

[0867] The system according to claim 1, having the ability to dynamically change generated visual data based on user requests.

[0868] "Example 2 of combining an emotion engine"

[0869] (Claim 1)

[0870] A means for receiving audio data and performing a process to convert said audio data into text data,

[0871] A means for receiving text data and performing a process to convert said text data into audio data,

[0872] A means of receiving diverse data input from information devices, analyzing the data, and identifying the user's emotions.

[0873] A means for dynamically generating content based on user emotions using a predetermined artificial intelligence model,

[0874] A system including means for displaying or playing generated content in a manner that adapts to the user's psychological state.

[0875] (Claim 2)

[0876] The system according to claim 1, which uses a specific algorithm to accurately identify a user's emotions in the analysis of emotion recognition.

[0877] (Claim 3)

[0878] The system according to claim 1, which has a function to dynamically adjust the style and theme of generated content in accordance with the user's emotions.

[0879] "Application example 2 when combining with an emotional engine"

[0880] (Claim 1)

[0881] A means for receiving audio data and performing a process to convert said audio data into text data,

[0882] A means for receiving text data and performing a process to convert said text data into audio data,

[0883] A means for performing a process that generates images according to a specified theme or style based on user instructions,

[0884] A means for sending and receiving user input data via a communication network and for appropriately formatting said data,

[0885] A means for displaying or playing back the generated data to the user,

[0886] A means for analyzing user emotions and performing a process to dynamically present content based on those emotions,

[0887] A system that includes means of recommending and providing media content to users based on their emotions.

[0888] (Claim 2)

[0889] The system according to claim 1, which improves accuracy in converting audio data using a speech recognition algorithm.

[0890] (Claim 3)

[0891] The system according to claim 1, which has the function of dynamically customizing the generated image based on the user's request, and further adjusts the customization based on the user's emotions. [Explanation of Symbols]

[0892] 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 for receiving audio data and performing a process to convert said audio data into text data, A means for receiving text data and performing a process to convert said text data into audio data, A means for performing a process that generates images according to a specified theme or style based on user instructions, A means for sending and receiving user input data via a communication network and for appropriately formatting said data, A system that includes means for displaying or playing back generated data to present it to the user.

2. The system according to claim 1, which improves accuracy in converting audio data using a speech recognition algorithm.

3. The system according to claim 1, which has a function to dynamically customize the generated image based on the user's request.

Images