system

Abstract

We provide the system. [Solution] A means of analyzing emotional information entered by the user and identifying the emotional classification, A means of creating original musical works based on the aforementioned emotional classification, A means for saving and sharing the aforementioned musical works on a digital recording medium, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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 society, there is a problem that means for continuously and emotionally recording special moments of individual lives are limited. In particular, it is difficult to accurately preserve various emotions felt in daily life and easily relive them at any time. With conventional methods, it has been difficult to effectively generate a recording medium associated with emotions and difficult to retain rich moments of individual lives over a long period.

Means for Solving the Problems

[0005] This invention solves the above problems by providing means for analyzing emotional information entered by a user and identifying an emotional category, and means for generating original lyrics based on this emotional category. Furthermore, by using means for generating music that matches these lyrics, it becomes possible to save personal emotional moments as music. As a result, the generated lyrics and music are stored on a dedicated recording medium, allowing the user to relive special moments in their life at any time later. In addition, means are provided to improve the accuracy of emotional analysis based on image data entered by the user, thereby realizing multifaceted emotional recording. Furthermore, by including means for adjusting the generated music to different music genres based on the user's preferences, a more personalized music experience is provided.

[0006] A "user" refers to an individual who wishes to use the system to input emotional information and image data in order to generate original music.

[0007] "Emotional information" refers to text data entered by the user, including words and phrases used to express specific emotional states.

[0008] "Analysis" refers to the process of processing input emotional information to identify the underlying emotional category.

[0009] An "emotional category" refers to the classification of emotions identified as a result of the analysis, and examples include joy, sadness, and surprise.

[0010] "Lyric generation" refers to the process of creating original lyrics based on specific emotional categories.

[0011] "Music generation" refers to the process of creating melodies and accompaniments to match the generated lyrics.

[0012] "Recording medium" refers to a storage method, such as a digital album, for saving generated lyrics and music.

[0013] "Image data" refers to digital data containing visual information that users input and which is used to improve the accuracy of sentiment analysis.

[0014] "Preferences" refer to individual tastes regarding the style and genre of music that a user enjoys.

[0015] "Adjustment" refers to the process of modifying or optimizing the elements of the generated music to suit the user's preferences. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]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 Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

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

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

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

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

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

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention relates to a system that generates original music using emotional information and image data entered by the user. The user first enters emotions related to specific moments or events in their daily life in text format via a terminal. They can also add image data capturing those moments as needed.

[0038] These input data are sent from the terminal to the server. The server uses natural language processing technology to analyze the emotional information and classify the input text into specific emotional categories. This analysis accurately identifies the emotions the user was experiencing at that moment. Furthermore, image data is also used in the analysis to improve the accuracy of emotion identification.

[0039] Next, the server generates original lyrics based on the identified emotions. This lyric generation process selects appropriate themes and vocabulary to match the emotions, creating text that best expresses the user's feelings. This lyric generation is a core process for richly recreating specific moments that the user wants to remember.

[0040] Furthermore, the server generates music based on these lyrics. The music generation process considers melodies, harmonies, and rhythms that match the emotional category, musically expressing the user's emotions. It's also possible to adjust the generated music to different genres according to the user's preferences.

[0041] The generated lyrics and music are stored in a user-specific digital album. This allows the user to play the generated content anytime and relive those special emotions. The device presents the user with playback options for the generated music, providing a personalized music experience.

[0042] As a concrete example, consider a scenario where a user wants to record an emotional moment from their graduation ceremony. In this case, the user inputs "graduation ceremony" and a photo, and describes their emotions as "gratitude" or "pride." The server analyzes this information, assigns a specific emotion category, and generates corresponding lyrics. Then, it generates an uplifting melody or moving accompaniment to match the emotion, and saves it as a final musical work.

[0043] This invention provides a novel method that allows users to save important moments in their lives as music and relive them emotionally.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user inputs emotional information as text from their device and selects relevant photo data as needed. They then press the send button to prepare to send this data to the server.

[0047] Step 2:

[0048] The terminal packages the text and image data entered by the user and sends it to the server using an encrypted communication protocol.

[0049] Step 3:

[0050] The server passes the received emotional text data to a natural language processing engine, which analyzes the user's emotions. This identifies emotional categories such as joy, sadness, and surprise from the text.

[0051] Step 4:

[0052] The server uses an AI generative model to generate original lyrics based on identified emotion categories. The AI ​​generative model selects themes and appropriate vocabulary corresponding to the emotion and creates lyrics that express that emotion.

[0053] Step 5:

[0054] The server activates a music generation engine based on the generated lyrics. This engine designs melodies, harmonies, and rhythms that match the emotion category, expressing the emotion in musical form.

[0055] Step 6:

[0056] The server saves the generated music data to the user's personalized digital album and updates the album chronologically. This allows users to organize special moments in their lives as music.

[0057] Step 7:

[0058] The server sends the generated lyrics and music data to the terminal, making them accessible to the user.

[0059] Step 8:

[0060] The device provides the user with playback options for the generated music, allowing them to play the music whenever they choose and relive special moments.

[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] Traditionally, systems that analyze user emotions and generate creative works based on them have had limitations in terms of the accuracy of emotion recognition and the quality of the generated content. Furthermore, ensuring the security of emotional information transmission and data conversion, and generating optimal content tailored to diverse user preferences, have been challenges.

[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 analyzing emotional information entered by the user and identifying an emotional category, means for generating an original string based on the emotional category, and means for generating audio that matches the string. This enables high-precision analysis of the user's emotions and the generation of high-quality content that matches those emotions.

[0066] A "user" is an entity that utilizes this system, inputting emotional information and additional data to request the generation of original content.

[0067] "Emotional information" refers to information about a user's emotional state or events, which they input as text.

[0068] An "emotional category" is a concept that indicates a specific group of emotions to which analyzed emotional information is classified.

[0069] "Strings of characters" refer to linguistic expressions generated based on emotional information, and take the form of lyrics or poetry.

[0070] "Sound" refers to music or audio information generated in a format that matches a string of characters.

[0071] "Recording medium" refers to the digital storage or physical media on which the generated content is stored.

[0072] "Natural language processing technology" refers to the technology that uses computers to understand and manipulate human language.

[0073] "Image analysis technology" refers to computational methods for extracting information from digital images.

[0074] A "generative model" refers to an algorithmic framework for automatically generating new content based on input data.

[0075] This invention is a system that allows users to record specific moments in their daily lives and generate original audio content that reflects their emotions. Users first use a terminal to input emotional information in text format. They can also upload image data capturing the moment if necessary. This data is transmitted from the terminal to a server. To ensure security during data transmission, encryption technologies such as HTTP are used.

[0076] The server efficiently analyzes emotional information using natural language processing techniques and identifies emotional categories. This analysis utilizes toolkits such as natural language processing APIs. Furthermore, image analysis techniques are used to analyze image data and improve the accuracy of emotional analysis. A general-purpose library is used as a digital image processing framework for the analysis.

[0077] After identifying an emotion category, the server uses a generative model to generate original strings based on that emotion. Various generative AI algorithms are used for this generation. Furthermore, in the process of generating speech based on the generated strings, a speech generation model is used to select a tone and structure that matches the emotion.

[0078] The generated text and audio are saved to a user-specific storage medium, which the user can then play back through their device to enjoy a personalized experience. For example, if a user wants to record a fun moment with their family, they can input text such as "Today was so much fun. I'm so happy to be with my family" along with an image capturing that scene, and audio content that captures that precious family memory will be generated.

[0079] Examples of prompts for a generative AI model are as follows:

[0080] "Text: Today was really fun. I'm happy to spend time with my family. Emotion: Joy, happiness. Please generate text and audio based on this emotion."

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] Users use their devices to input emotional information about specific moments in their daily lives in text format, and sometimes attach image data. The input information is prepared as emotional information in text format and as visual data in image format. This input data is then prepared for transmission to the server using an information transmission protocol.

[0084] Step 2:

[0085] The device sends emotional information and image data received from the user to the server. The data sent includes the input text and images. The data is transmitted using a secure communication protocol (e.g., HTTPS), and the server is prepared for analysis.

[0086] Step 3:

[0087] The server analyzes the received text data using natural language processing tools to identify sentiment categories. In this process, text data is used as input, and the identified sentiment categories are provided as output. For example, sentiments are classified as positive, negative, neutral, etc., based on keywords extracted from the text.

[0088] Step 4:

[0089] The server analyzes the transmitted image data using image analysis technology. Here, image data is taken as input, and selected information to reinforce emotions is extracted as output. Specifically, objects and scenes within the image are recognized, and this is integrated with the results of text analysis to make the emotional judgment more accurate.

[0090] Step 5:

[0091] The server uses a generative AI model to generate original strings using already identified emotion categories. The input here is the emotion category, and the output is the newly generated string. This process selects themes and vocabulary appropriate to the emotion.

[0092] Step 6:

[0093] The server uses a speech generation model to create music based on the generated string. The input is the generated string, and the output is audio data. In speech generation, the tone, melody, and harmony that match the emotion are considered, and music that reflects the user's feelings is created.

[0094] Step 7:

[0095] The server saves the generated text and audio to a user-specific storage medium. The saved data consists of text and audio. This allows the user to later play back the generated content through their device and relive the emotions they felt at the time.

[0096] (Application Example 1)

[0097] 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."

[0098] There is a lack of technological means to reproduce and record users' emotional experiences in a deeper, more personalized way. Furthermore, there is a need to provide generated content in a form that can be shared not only individually but also socially.

[0099] 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.

[0100] In this invention, the server includes means for analyzing emotional information entered by a user and identifying an emotional classification; means for creating an original musical work based on the emotional classification; and means for storing and sharing the musical work on a digital recording medium. This enables users to embody their emotional experiences as original music and share it with society.

[0101] "Emotional information" refers to data entered by a user to express the emotions they feel in response to a particular moment or event.

[0102] "Analysis" is the process of analyzing information provided by users and deriving specific conclusions or results from that information.

[0103] "Emotional classification" is the act of classifying emotional information into specific categories based on analysis results.

[0104] An "original musical work" is music that is specially generated based on the user's emotional classification and reflects the user's personality and experiences.

[0105] A "digital recording medium" is a medium that can store information electronically and is used to store generated musical works.

[0106] "Sharing" is the act of sharing generated content with others, whether through social means or information transmission technologies.

[0107] The system for carrying out this invention has a series of processes for analyzing emotional information and image data entered by a user, generating original musical works based on that information, and saving and sharing them on a digital recording medium. This system mainly consists of a server, a terminal, and related software.

[0108] Users input text information expressing specific emotions and associated image data using their devices. This data is collected on the device and sent to a server. Typically, this process utilizes an internet connection and cloud-based data storage.

[0109] The server analyzes the received data using natural language processing libraries developed in Python (e.g., NLTK and Transformers). This analysis categorizes emotional information into specific emotional classifications, preparing it for subsequent data processing. Furthermore, image data is analyzed using TENSORFLOW® and PyTorch, and integrated with text data to improve the accuracy of emotion identification.

[0110] Next, the server generates lyrics using a generative AI model. Here, the OpenAI® API is applied, and lyrics are created with appropriate themes and vocabulary based on emotion classification. Subsequently, the music generation module utilizes librosa and scikit-learn to create melodies and harmonies that match the user's emotions.

[0111] The generated musical works are stored on digital recording media, and users can play and share them on their devices. This sharing is typically done through information transmission technologies such as social networking services (SNS).

[0112] As a concrete example, consider a scenario where a user inputs a photo of a sunrise taken during a trip and provides emotional information such as "awe-inspiring." Based on this input, the system generates lyrics and music that reflect the user's feelings. The generated music, with its refreshing morning melody, is themed around the inspiring start of a day.

[0113] As an example of a prompt statement,

[0114] "Let's turn your emotions into music! Add a photo you've taken and tell us what you're feeling in one word (e.g., 'joy'). We'll create an original musical piece for you."

[0115] This provides instructions to the user, encouraging them to quickly input emotional information.

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The user inputs emotional information and image data using a device. A prompt appears on the device screen for inputting emotions, where the user uploads emotional information in text format and the captured image. The input data is then prepared by the device to be sent to a cloud server.

[0119] Step 2:

[0120] The server receives data sent from the terminal. The server analyzes the received sentiment information using natural language processing techniques. Using the Python NLTK library, text data is processed with a sentiment classification algorithm and classified into specific sentiment categories. The input is string data, and the output is a sentiment category label.

[0121] Step 3:

[0122] The server analyzes image data using TensorFlow and identifies emotional elements extracted from the images. This process is performed by an image recognition model, and its consistency with the emotional categories obtained from text analysis is verified. The input is image data, and the output is additional emotional feature information.

[0123] Step 4:

[0124] The server generates original lyrics based on emotion categories. The lyrics are automatically generated using an AI model utilizing the OpenAI API. This process involves selecting themes and words that match the emotions. The input is the emotion category labels, and the output is the generated lyric text.

[0125] Step 5:

[0126] The server creates music to match the generated lyrics. It utilizes librosa and scikit-learn to build a music generation module, synthesizing melodies and harmonies that correspond to emotions. The input is the generated lyrics text, and the output is in music file format.

[0127] Step 6:

[0128] The server saves the final musical work to a digital recording medium. This music is uploaded to the user's digital storage, and access is managed. The user can play this music through their device. The input is music data, and the output is the saved music file.

[0129] Step 7:

[0130] Users share their generated music works with others through information transmission technology. Sharing takes place via social media or email, and users do so using their device's sharing options. The input is a saved music file, and the output is a music file placed in a state where other users can access it.

[0131] 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.

[0132] This invention relates to a system equipped with an emotion engine that analyzes emotional information, image data, and voice input entered by a user to accurately recognize emotions. The user uses a terminal to input emotions felt at a specific moment as text, voice, or a photograph. All input data is transmitted from the terminal to the server.

[0133] Upon receiving this information, the server initiates a series of emotion recognition processes. Using an emotion engine, it extracts emotion categories from the input text using natural language processing techniques. Simultaneously, it analyzes facial expressions from image data and detects emotion signals from voice input. This enables multifaceted emotion recognition.

[0134] The recognized emotion information is used in the process of generating appropriate lyrics to match the user's intended emotions. The server uses an AI model to automatically generate original lyrics that are most relevant and reflect the user's emotions, based on the identified emotion categories. This lyric generation process is crucial for more deeply expressing the user's emotional experience.

[0135] Next, the server uses the generated lyric information to operate the music generation engine, creating a melody and accompaniment that matches the emotions. Furthermore, it can adjust the musical style based on the user's musical genre preferences. This results in a musical experience that resonates with the individual's emotions.

[0136] The generated lyrics and music are saved in a user-exclusive digital album, allowing the user to relive the experience at any time. This process offers a new experience that enables users to record and reflect on special emotional moments in their lives.

[0137] As a concrete example, consider a scenario where a user wants to record the fun and excitement they felt during a trip with friends. In this case, the user takes photos, inputs phrases like "the best trip" or "an unforgettable moment," and possibly adds cheerful voice comments. The server uses this multimodal data to perform a complex sentiment analysis and generates lyrics and music that express those emotions. Finally, these creations are saved in a digital album, allowing the user to relive those special moments through music.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] The user uses the device to input text, audio, or photos that express their emotions. Once input is complete, they press the send button to prepare these data for the next processing stage.

[0141] Step 2:

[0142] The terminal packages the input data (text, audio, images) and sends it to the server using a secure data communication protocol. This process ensures that information is transmitted to the server without external interference.

[0143] Step 3:

[0144] The server passes the received text data to a natural language processing engine for sentiment analysis. It extracts sentiment categories (e.g., joy, surprise, sadness, etc.) from the text to identify the main emotions the user is feeling.

[0145] Step 4:

[0146] The server also performs sentiment analysis on audio data. Using a speech recognition engine, it analyzes emotional signals from voice tone and word choice, and integrates them with the results from the text data.

[0147] Step 5:

[0148] The server uses image data to analyze the user's facial expressions. Using advanced computer vision technology, it extracts emotions from the facial expressions in the image and integrates them with the previously obtained emotional information.

[0149] Step 6:

[0150] The server uses an AI model to generate original lyrics based on integrated emotional information. These lyrics are designed to match specific emotional categories and accurately reflect the user's feelings.

[0151] Step 7:

[0152] The server activates a music generation engine based on the generated lyrics, creating music that best suits the emotions. Furthermore, it selects a music genre that reflects the user's preferences and fine-tunes the details of the music.

[0153] Step 8:

[0154] The server saves the completed lyrics and music data to the user's personal digital album. This allows the user to relive those special emotional moments through music at any time.

[0155] Step 9:

[0156] The device provides the user with the option to play the generated music. The user can use this option to listen to the music again and re-experience the emotions.

[0157] (Example 2)

[0158] 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".

[0159] The problem lies in the lack of methods to integrate input data consisting of multiple formats, such as emotional information, images, and audio, to analyze the user's multifaceted emotional state and provide a music experience based on that analysis. In particular, the process of expressing individual emotions and generating music appropriate to them is cumbersome, and there is a challenge in customizing music to match the user's preferences.

[0160] 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.

[0161] In this invention, the server includes means for analyzing emotional information input through a device used by the user and extracting an emotional classification; means for generating a unique sentence based on the emotional classification; and means for creating a sound that matches the sentence. This enables emotional analysis integrating multiple data formats, as well as the automatic generation and storage of music tailored to the user's individual emotional experience.

[0162] "Device" refers to an electronic device used by users to input emotional information such as text, images, and audio.

[0163] "Emotional information" refers to data that expresses emotions such as joy, sadness, or surprise that a user felt at a specific moment, in one of three forms: text, images, or audio.

[0164] "Emotion classification" refers to the emotional categories extracted from input emotional information through natural language processing, image analysis, and voice analysis.

[0165] "Methods for generating text" refers to the process of automatically creating lyrics or text that reflect emotions using AI technology based on emotion classification.

[0166] "Means of creating sound" refers to the music generation process that creates melodies and accompaniments that fit the generated sentences.

[0167] A "digital storage device" refers to a recording medium that saves generated music and lyrics for each user, allowing them to be played back later.

[0168] "Song format" refers to different methods of musical expression depending on the genre and style of music, and in particular includes musical characteristics that can be adjusted according to the user's preferences.

[0169] "Facial expression analysis" refers to a technology that analyzes the features of a user's face from image data and identifies their emotional state.

[0170] "Audio signals" refer to the patterns and characteristics of speech necessary to detect intonation and emotion from the audio data input by the user.

[0171] This invention is a system that analyzes emotional information input from a user's device and generates music based on that information. Users can input emotionally related text, images, and audio through their device. All of this input data is transmitted to a server in digital format. The device can be an electronic device such as a smartphone or tablet.

[0172] Upon receiving data, the server first analyzes the emotional information using techniques such as natural language processing, image processing, and speech analysis. For natural language processing, software libraries for emotion analysis, such as open-source NLP toolkits, are used. Image data is analyzed using image analysis algorithms to determine facial expressions, and speech recognition software is used to analyze speech signals from audio data.

[0173] Based on the analyzed emotion data, the server uses a generative AI model to generate original lyrics. In this process, the AI ​​creates words and phrases according to the emotion category. The generated lyrics are fed into a music generation engine, which creates music by appropriately combining melody and accompaniment. Here, an AI-based composition system is used, adjusting the genre and style to match the user's musical preferences.

[0174] As a concrete example, consider a scenario where a user wants to record the enjoyment they felt while traveling with friends. The user takes fun photos with their smartphone, enters text such as "the best moment," and possibly records their joyful voice. When this data is sent to a server, the server comprehensively analyzes the emotions from these various data points and generates lyrics and a melody based on the results.

[0175] An example of a prompt message might be, "Please create lyrics that express the joyful feelings you have while traveling." Through this system, users can continuously record and re-experience their individual, emotion-based musical experiences.

[0176] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0177] Step 1:

[0178] Users input emotional information using a device. This input data can be in the form of text, images, audio, or a combination of these. For example, a user might take a photo of a moment they feel "happy" and record a short phrase or comment expressing that emotion. This input data is then converted into a digital format by the device. As a result, multiple emotional data formats are generated and ready to be sent to the server.

[0179] Step 2:

[0180] The server analyzes the digital data received from the terminal. Specifically, text data is analyzed using natural language processing techniques to extract emotion classifications from the input words. Image data is analyzed using image processing algorithms to determine emotions from facial features. Audio data is analyzed using speech analysis software to determine intonation from the audio signal and infer emotions. The output of this step is an integrated emotion profile derived from text, images, and audio.

[0181] Step 3:

[0182] The server generates original lyrics using a generative AI model based on an integrated emotional profile. This process creates words and phrases related to emotional categories. Specifically, the AI ​​model selects positive words to express "joy" and forms creative sentences based on prompt sentences. The generated lyrics are the output of this step.

[0183] Step 4:

[0184] Based on the generated lyrics, the server uses a music generation engine to create a melody and accompaniment. Here, the user's musical genre preferences are taken into consideration, and the song format is adjusted. For example, a pop song with a cheerful atmosphere might be generated. The output of this step is fully original music that matches the user's emotional experience.

[0185] Step 5:

[0186] Finally, the server saves the generated lyrics and music to the user's personal digital storage device. This allows the user to relive the musical experience at any time. The saved data includes the generated lyrics and music files.

[0187] (Application Example 2)

[0188] 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".

[0189] In modern times, there is a growing need for deeper self-expression by recording emotional moments experienced in daily life and expressing and sharing them as music. However, there is a lack of effective systems for comprehensively analyzing emotions and automatically generating empathetic music or poetry based on those analyses. The challenge lies in easily creating original musical experiences that appropriately reflect users' emotions, resonate with individual experiences, and then easily share them through digital media.

[0190] 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.

[0191] In this invention, the server includes means for analyzing emotional information entered by the user and identifying multifaceted emotional categories, means for generating original verses and sounds, and means for storing the generated verses and sounds on a recording medium dedicated to the user and making them shareable via digital media. As a result, the user can have their emotional moments analyzed with high accuracy, express themselves richly through original music, and form emotional connections with others through sharing and re-experiencing.

[0192] "Emotional information" refers to audio, text, and image data that users input to express the emotions they felt at a particular moment.

[0193] An "emotional category" is a classification of emotions identified based on analyzed emotional information.

[0194] "Verse" refers to original lyrics or poetic writing generated based on emotional categories.

[0195] "Acoustics" refers to music or a combination of sounds created to fit the generated verse.

[0196] A "recording medium" is a digital data storage device for saving the generated verse and sound.

[0197] "Multifaceted analysis" is a method that recognizes emotions with greater accuracy by integrating and analyzing audio, text, and images.

[0198] "Digital media" refers to electronic communication platforms used by users to share generated music, poetry, and other forms of expression with others.

[0199] "Preferences" refer to a user's personal tastes regarding music genres or sound formats they particularly enjoy.

[0200] The system implementing this invention includes a terminal for the user to input emotional information and a server for emotion analysis and managing the generated content. The user uses a terminal such as a smartphone or computer glasses to input the emotions they feel at a particular moment as voice, text, or images. This data is transmitted from the terminal to the server.

[0201] The server uses the "speech_recognition" library for speech processing, converting speech into text data. This makes it possible to extract emotions from the speech data. Next, the "transformers" library is used to analyze the emotions in the text data, identifying multifaceted emotional categories. At the same time, the "emotion_recognition_module" is used to analyze the emotions in image data, analyzing facial expressions and extracting emotional information from the images.

[0202] The server integrates these analysis results to determine an emotion category and instructs the generative AI model to generate a verse that fits the emotion category as a "prompt sentence." This generation process uses generative AI models such as "GPT-2" to create creative verses that reflect the user's emotions. Furthermore, it manipulates a music generation engine to generate sounds that match the generated verses, adjusting them based on the user's preferences and different sound formats.

[0203] The verses and sounds generated during this process are saved to a user-specific digital storage medium. Users can then share these results with others through digital media. Furthermore, by using prompts such as "Create lyrics that match the joy and excitement you felt while traveling," users can generate creative content tailored to specific emotions. This allows users to re-experience their emotional experiences as music.

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The user uses a device to input emotions they feel at a specific moment as voice, text, and images. The input data is sent from the device to the server. The input in this step consists of the user's voice, text, and image data, which are used for subsequent processing.

[0207] Step 2:

[0208] The server uses the "speech_recognition" library to convert audio data into text. It analyzes the audio file and generates a string using speech recognition technology. The output is the converted text data.

[0209] Step 3:

[0210] The server uses a natural language processing engine to identify sentiment categories for both the converted text and the text directly entered by the user. Specifically, the "transformers" library performs sentiment analysis and extracts sentiment labels such as positive, negative, and neutral from the text. The output of this step is the identified sentiment category.

[0211] Step 4:

[0212] The server uses the "emotion_recognition_module" with image data to analyze the user's facial expressions. Through this analysis, it obtains emotion data extracted from the image. The output of this step is emotion information based on the image.

[0213] Step 5:

[0214] The server integrates the emotion data obtained from steps 3 and 4 and determines a synthetic emotion category. Based on this synthetic emotion information, it inputs the emotion category into the generative AI model and generates a prompt sentence. The output is a prompt sentence formatted for the generative AI model.

[0215] Step 6:

[0216] The server uses a prompt to generate a verse using a generative AI model (e.g., "GPT-2"). The AI ​​model creates creative text based on the provided sentiment categories. The output of this step is the generated verse.

[0217] Step 7:

[0218] The server operates a music generation engine to produce sounds that match the generated verse. It creates melodies and accompaniments based on user preferences and context, and adjusts the musical style. The output of this step is the generated sound data.

[0219] Step 8:

[0220] The server saves the generated verses and sounds to a user-specific digital storage medium. The user can later play back this content and share it with others via digital media. The output of this step is the saved media file.

[0221] 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.

[0222] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0223] 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.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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".

[0237] This invention relates to a system that generates original music using emotional information and image data entered by the user. The user first enters emotions related to specific moments or events in their daily life in text format via a terminal. They can also add image data capturing those moments as needed.

[0238] These input data are sent from the terminal to the server. The server uses natural language processing technology to analyze the emotional information and classify the input text into specific emotional categories. This analysis accurately identifies the emotions the user was experiencing at that moment. Furthermore, image data is also used in the analysis to improve the accuracy of emotion identification.

[0239] Next, the server generates original lyrics based on the identified emotions. This lyric generation process selects appropriate themes and vocabulary to match the emotions, creating text that best expresses the user's feelings. This lyric generation is a core process for richly recreating specific moments that the user wants to remember.

[0240] Furthermore, the server generates music based on these lyrics. The music generation process considers melodies, harmonies, and rhythms that match the emotional category, musically expressing the user's emotions. It's also possible to adjust the generated music to different genres according to the user's preferences.

[0241] The generated lyrics and music are stored in a user-specific digital album. This allows the user to play the generated content anytime and relive those special emotions. The device presents the user with playback options for the generated music, providing a personalized music experience.

[0242] As a concrete example, consider a scenario where a user wants to record an emotional moment from their graduation ceremony. In this case, the user inputs "graduation ceremony" and a photo, and describes their emotions as "gratitude" or "pride." The server analyzes this information, assigns a specific emotion category, and generates corresponding lyrics. Then, it generates an uplifting melody or moving accompaniment to match the emotion, and saves it as a final musical work.

[0243] This invention provides a novel method that allows users to save important moments in their lives as music and relive them emotionally.

[0244] The following describes the processing flow.

[0245] Step 1:

[0246] The user inputs emotional information as text from their device and selects relevant photo data as needed. They then press the send button to prepare to send this data to the server.

[0247] Step 2:

[0248] The terminal packages the text and image data entered by the user and sends it to the server using an encrypted communication protocol.

[0249] Step 3:

[0250] The server passes the received emotional text data to a natural language processing engine, which analyzes the user's emotions. This identifies emotional categories such as joy, sadness, and surprise from the text.

[0251] Step 4:

[0252] The server uses an AI generative model to generate original lyrics based on identified emotion categories. The AI ​​generative model selects themes and appropriate vocabulary corresponding to the emotion and creates lyrics that express that emotion.

[0253] Step 5:

[0254] The server activates a music generation engine based on the generated lyrics. This engine designs melodies, harmonies, and rhythms that match the emotion category, expressing the emotion in musical form.

[0255] Step 6:

[0256] The server saves the generated music data to the user's personalized digital album and updates the album chronologically. This allows users to organize special moments in their lives as music.

[0257] Step 7:

[0258] The server sends the generated lyrics and music data to the terminal, making them accessible to the user.

[0259] Step 8:

[0260] The device provides the user with playback options for the generated music, allowing them to play the music whenever they choose and relive special moments.

[0261] (Example 1)

[0262] 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."

[0263] Traditionally, systems that analyze user emotions and generate creative works based on them have had limitations in terms of the accuracy of emotion recognition and the quality of the generated content. Furthermore, ensuring the security of emotional information transmission and data conversion, and generating optimal content tailored to diverse user preferences, have been challenges.

[0264] 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.

[0265] In this invention, the server includes means for analyzing emotional information entered by the user and identifying an emotional category, means for generating an original string based on the emotional category, and means for generating audio that matches the string. This enables high-precision analysis of the user's emotions and the generation of high-quality content that matches those emotions.

[0266] A "user" is an entity that utilizes this system, inputting emotional information and additional data to request the generation of original content.

[0267] "Emotional information" refers to information about a user's emotional state or events, which they input as text.

[0268] An "emotional category" is a concept that indicates a specific group of emotions to which analyzed emotional information is classified.

[0269] "Strings of characters" refer to linguistic expressions generated based on emotional information, and take the form of lyrics or poetry.

[0270] "Sound" refers to music or audio information generated in a format that matches a string of characters.

[0271] "Recording medium" refers to the digital storage or physical media on which the generated content is stored.

[0272] "Natural language processing technology" refers to the technology that uses computers to understand and manipulate human language.

[0273] "Image analysis technology" refers to computational methods for extracting information from digital images.

[0274] A "generative model" refers to an algorithmic framework for automatically generating new content based on input data.

[0275] This invention is a system that allows users to record specific moments in their daily lives and generate original audio content that reflects their emotions. Users first use a terminal to input emotional information in text format. They can also upload image data capturing the moment if necessary. This data is transmitted from the terminal to a server. To ensure security during data transmission, encryption technologies such as HTTP are used.

[0276] The server efficiently analyzes emotional information using natural language processing techniques and identifies emotional categories. This analysis utilizes toolkits such as natural language processing APIs. Furthermore, image analysis techniques are used to analyze image data and improve the accuracy of emotional analysis. A general-purpose library is used as a digital image processing framework for the analysis.

[0277] After identifying an emotion category, the server uses a generative model to generate original strings based on that emotion. Various generative AI algorithms are used for this generation. Furthermore, in the process of generating speech based on the generated strings, a speech generation model is used to select a tone and structure that matches the emotion.

[0278] The generated text and audio are saved to a user-specific storage medium, which the user can then play back through their device to enjoy a personalized experience. For example, if a user wants to record a fun moment with their family, they can input text such as "Today was so much fun. I'm so happy to be with my family" along with an image capturing that scene, and audio content that captures that precious family memory will be generated.

[0279] Examples of prompts for a generative AI model are as follows:

[0280] "Text: Today was really fun. I'm happy to spend time with my family. Emotion: Joy, happiness. Please generate text and audio based on this emotion."

[0281] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0282] Step 1:

[0283] The user uses the terminal to input emotional information regarding specific moments in daily life in text form, and in some cases, also attaches image data. The input information prepares the emotional information as text data and the visual data as image data. This input data is prepared for transmission to the server using an information transmission protocol.

[0284] Step 2:

[0285] The terminal transmits the emotional information and image data received from the user to the server. The data to be transmitted includes the input text and image. The data is transmitted using a secure communication protocol (e.g., HTTPS), and preparations for analysis on the server side are completed.

[0286] Step 3:

[0287] The server analyzes the received text data using natural language processing tools to identify the emotional category. In this process, text data is used as input, and the identified emotional category is provided as output. For example, based on keywords extracted from the text, the emotion is classified as positive, negative, neutral, etc.

[0288] Step 4:

[0289] The server analyzes the transmitted image data using image analysis technology. Here, the image data is taken as input, and selected information for reinforcing the emotion is extracted as output. Specifically, objects and scenes within the image are recognized, and it is integrated with the results of text analysis to make the emotion judgment more accurate.

[0290] Step 5:

[0291] The server uses the already identified emotional category to generate an original string using a generative AI model. The input here is the emotional category, and the output is the newly generated string. In this process, themes and vocabulary corresponding to the emotion are selected.

[0292] Step 6:

[0293] The server uses a speech generation model to create music based on the generated string. The input is the generated string, and the output is audio data. In speech generation, the tone, melody, and harmony that match the emotion are considered, and music that reflects the user's feelings is created.

[0294] Step 7:

[0295] The server saves the generated text and audio to a user-specific storage medium. The saved data consists of text and audio. This allows the user to later play back the generated content through their device and relive the emotions they felt at the time.

[0296] (Application Example 1)

[0297] 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."

[0298] There is a lack of technological means to reproduce and record users' emotional experiences in a deeper, more personalized way. Furthermore, there is a need to provide generated content in a form that can be shared not only individually but also socially.

[0299] 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.

[0300] In this invention, the server includes means for analyzing emotional information entered by a user and identifying an emotional classification; means for creating an original musical work based on the emotional classification; and means for storing and sharing the musical work on a digital recording medium. This enables users to embody their emotional experiences as original music and share it with society.

[0301] "Emotional information" refers to data input to express the emotions that a user has towards a specific moment or event.

[0302] "Analysis" refers to the process of analyzing the content based on the information provided by the user and deriving specific conclusions or results.

[0303] "Emotion classification" refers to the act of classifying emotional information into specific categories based on the analysis results.

[0304] "Original music work" refers to music that is specially generated based on the emotion classification of the user and reflects the user's personality and experiences.

[0305] "Digital recording medium" refers to a medium that can electronically store information and is used to store the generated music work.

[0306] "Sharing" refers to the act of sharing the generated content with others and is carried out through social or information transmission technologies.

[0307] The system for implementing this invention has a series of processes for analyzing the emotional information and image data input by the user, generating an original music work based on it, storing it in a digital recording medium, and sharing it. This system is mainly composed of a server, a terminal, and related software.

[0308] The user uses the terminal to input text information expressing specific emotions and related image data. In the terminal, these data are collected and sent to the server. At this time, it is common for the terminal to use an Internet connection and pass through cloud-based data storage.

[0309] The server analyzes the received data using natural language processing libraries developed in Python (e.g., NLTK and Transformers). This analysis categorizes emotional information into specific emotion classifications, preparing it for subsequent data processing. Furthermore, it uses TensorFlow and PyTorch to analyze image data and integrate it with text data to improve the accuracy of emotion identification.

[0310] Next, the server generates lyrics using a generative AI model. Here, the OpenAI API is applied, and lyrics are created with appropriate themes and vocabulary based on sentiment classification. Subsequently, the music generation module utilizes librosa and scikit-learn to create melodies and harmonies that match the user's emotions.

[0311] The generated musical works are stored on digital recording media, and users can play and share them on their devices. This sharing is typically done through information transmission technologies such as social networking services (SNS).

[0312] As a concrete example, consider a scenario where a user inputs a photo of a sunrise taken during a trip and provides emotional information such as "awe-inspiring." Based on this input, the system generates lyrics and music that reflect the user's feelings. The generated music, with its refreshing morning melody, is themed around the inspiring start of a day.

[0313] As an example of a prompt statement,

[0314] "Let's turn your emotions into music! Add a photo you've taken and tell us what you're feeling in one word (e.g., 'joy'). We'll create an original musical piece for you."

[0315] This provides instructions to the user, encouraging them to quickly input emotional information.

[0316] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0317] Step 1:

[0318] The user inputs emotional information and image data using a device. A prompt appears on the device screen for inputting emotions, where the user uploads emotional information in text format and the captured image. The input data is then prepared by the device to be sent to a cloud server.

[0319] Step 2:

[0320] The server receives data sent from the terminal. The server analyzes the received sentiment information using natural language processing techniques. Using the Python NLTK library, text data is processed with a sentiment classification algorithm and classified into specific sentiment categories. The input is string data, and the output is a sentiment category label.

[0321] Step 3:

[0322] The server analyzes image data using TensorFlow and identifies emotional elements extracted from the images. This process is performed by an image recognition model, and its consistency with the emotional categories obtained from text analysis is verified. The input is image data, and the output is additional emotional feature information.

[0323] Step 4:

[0324] The server generates original lyrics based on emotion categories. The lyrics are automatically generated using an AI model utilizing the OpenAI API. This process involves selecting themes and words that match the emotions. The input is the emotion category labels, and the output is the generated lyric text.

[0325] Step 5:

[0326] The server creates music to match the generated lyrics. It utilizes librosa and scikit-learn to build a music generation module, synthesizing melodies and harmonies that correspond to emotions. The input is the generated lyrics text, and the output is in music file format.

[0327] Step 6:

[0328] The server saves the final musical work to a digital recording medium. This music is uploaded to the user's digital storage, and access is managed. The user can play this music through their device. The input is music data, and the output is the saved music file.

[0329] Step 7:

[0330] Users share their generated music works with others through information transmission technology. Sharing takes place via social media or email, and users do so using their device's sharing options. The input is a saved music file, and the output is a music file placed in a state where other users can access it.

[0331] 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.

[0332] This invention relates to a system equipped with an emotion engine that analyzes emotional information, image data, and voice input entered by a user to accurately recognize emotions. The user uses a terminal to input emotions felt at a specific moment as text, voice, or a photograph. All input data is transmitted from the terminal to the server.

[0333] Upon receiving this information, the server initiates a series of emotion recognition processes. Using an emotion engine, it extracts emotion categories from the input text using natural language processing techniques. Simultaneously, it analyzes facial expressions from image data and detects emotion signals from voice input. This enables multifaceted emotion recognition.

[0334] The recognized emotion information is used in the process of generating appropriate lyrics to match the user's intended emotions. The server uses an AI model to automatically generate original lyrics that are most relevant and reflect the user's emotions, based on the identified emotion categories. This lyric generation process is crucial for more deeply expressing the user's emotional experience.

[0335] Next, the server uses the generated lyric information to operate the music generation engine, creating a melody and accompaniment that matches the emotions. Furthermore, it can adjust the musical style based on the user's musical genre preferences. This results in a musical experience that resonates with the individual's emotions.

[0336] The generated lyrics and music are saved in a user-exclusive digital album, allowing the user to relive the experience at any time. This process offers a new experience that enables users to record and reflect on special emotional moments in their lives.

[0337] As a concrete example, consider a scenario where a user wants to record the fun and excitement they felt during a trip with friends. In this case, the user takes photos, inputs phrases like "the best trip" or "an unforgettable moment," and possibly adds cheerful voice comments. The server uses this multimodal data to perform a complex sentiment analysis and generates lyrics and music that express those emotions. Finally, these creations are saved in a digital album, allowing the user to relive those special moments through music.

[0338] The following describes the processing flow.

[0339] Step 1:

[0340] The user uses the device to input text, audio, or photos that express their emotions. Once input is complete, they press the send button to prepare these data for the next processing stage.

[0341] Step 2:

[0342] The terminal packages the input data (text, audio, images) and sends it to the server using a secure data communication protocol. This process ensures that information is transmitted to the server without external interference.

[0343] Step 3:

[0344] The server passes the received text data to a natural language processing engine for sentiment analysis. It extracts sentiment categories (e.g., joy, surprise, sadness, etc.) from the text to identify the main emotions the user is feeling.

[0345] Step 4:

[0346] The server also performs sentiment analysis on audio data. Using a speech recognition engine, it analyzes emotional signals from voice tone and word choice, and integrates them with the results from the text data.

[0347] Step 5:

[0348] The server uses image data to analyze the user's facial expressions. Using advanced computer vision technology, it extracts emotions from the facial expressions in the image and integrates them with the previously obtained emotional information.

[0349] Step 6:

[0350] The server uses an AI model to generate original lyrics based on integrated emotional information. These lyrics are designed to match specific emotional categories and accurately reflect the user's feelings.

[0351] Step 7:

[0352] The server activates a music generation engine based on the generated lyrics, creating music that best suits the emotions. Furthermore, it selects a music genre that reflects the user's preferences and fine-tunes the details of the music.

[0353] Step 8:

[0354] The server saves the completed lyrics and music data to the user's personal digital album. This allows the user to relive those special emotional moments through music at any time.

[0355] Step 9:

[0356] The device provides the user with the option to play the generated music. The user can use this option to listen to the music again and re-experience the emotions.

[0357] (Example 2)

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

[0359] The problem lies in the lack of methods to integrate input data consisting of multiple formats, such as emotional information, images, and audio, to analyze the user's multifaceted emotional state and provide a music experience based on that analysis. In particular, the process of expressing individual emotions and generating music appropriate to them is cumbersome, and there is a challenge in customizing music to match the user's preferences.

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

[0361] In this invention, the server includes means for analyzing emotional information input through a device used by the user and extracting an emotional classification; means for generating a unique sentence based on the emotional classification; and means for creating a sound that matches the sentence. This enables emotional analysis integrating multiple data formats, as well as the automatic generation and storage of music tailored to the user's individual emotional experience.

[0362] "Device" refers to an electronic device used by users to input emotional information such as text, images, and audio.

[0363] "Emotional information" refers to data that expresses emotions such as joy, sadness, or surprise that a user felt at a specific moment, in one of three forms: text, images, or audio.

[0364] "Emotion classification" refers to the emotional categories extracted from input emotional information through natural language processing, image analysis, and voice analysis.

[0365] "Methods for generating text" refers to the process of automatically creating lyrics or text that reflect emotions using AI technology based on emotion classification.

[0366] "Means of creating sound" refers to the music generation process that creates melodies and accompaniments that fit the generated sentences.

[0367] A "digital storage device" refers to a recording medium that saves generated music and lyrics for each user, allowing them to be played back later.

[0368] "Song format" refers to different methods of musical expression depending on the genre and style of music, and in particular includes musical characteristics that can be adjusted according to the user's preferences.

[0369] "Facial expression analysis" refers to a technology that analyzes the features of a user's face from image data and identifies their emotional state.

[0370] "Audio signals" refer to the patterns and characteristics of speech necessary to detect intonation and emotion from the audio data input by the user.

[0371] This invention is a system that analyzes emotional information input from a user's device and generates music based on that information. Users can input emotionally related text, images, and audio through their device. All of this input data is transmitted to a server in digital format. The device can be an electronic device such as a smartphone or tablet.

[0372] Upon receiving data, the server first analyzes the emotional information using techniques such as natural language processing, image processing, and speech analysis. For natural language processing, software libraries for emotion analysis, such as open-source NLP toolkits, are used. Image data is analyzed using image analysis algorithms to determine facial expressions, and speech recognition software is used to analyze speech signals from audio data.

[0373] Based on the analyzed emotion data, the server uses a generative AI model to generate original lyrics. In this process, the AI ​​creates words and phrases according to the emotion category. The generated lyrics are fed into a music generation engine, which creates music by appropriately combining melody and accompaniment. Here, an AI-based composition system is used, adjusting the genre and style to match the user's musical preferences.

[0374] As a concrete example, consider a scenario where a user wants to record the enjoyment they felt while traveling with friends. The user takes fun photos with their smartphone, enters text such as "the best moment," and possibly records their joyful voice. When this data is sent to a server, the server comprehensively analyzes the emotions from these various data points and generates lyrics and a melody based on the results.

[0375] An example of a prompt message might be, "Please create lyrics that express the joyful feelings you have while traveling." Through this system, users can continuously record and re-experience their individual, emotion-based musical experiences.

[0376] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0377] Step 1:

[0378] Users input emotional information using a device. This input data can be in the form of text, images, audio, or a combination of these. For example, a user might take a photo of a moment they feel "happy" and record a short phrase or comment expressing that emotion. This input data is then converted into a digital format by the device. As a result, multiple emotional data formats are generated and ready to be sent to the server.

[0379] Step 2:

[0380] The server analyzes the digital data received from the terminal. Specifically, text data is analyzed using natural language processing techniques to extract emotion classifications from the input words. Image data is analyzed using image processing algorithms to determine emotions from facial features. Audio data is analyzed using speech analysis software to determine intonation from the audio signal and infer emotions. The output of this step is an integrated emotion profile derived from text, images, and audio.

[0381] Step 3:

[0382] The server generates original lyrics using a generative AI model based on an integrated emotional profile. This process creates words and phrases related to emotional categories. Specifically, the AI ​​model selects positive words to express "joy" and forms creative sentences based on prompt sentences. The generated lyrics are the output of this step.

[0383] Step 4:

[0384] Based on the generated lyrics, the server uses a music generation engine to create a melody and accompaniment. Here, the user's musical genre preferences are taken into consideration, and the song format is adjusted. For example, a pop song with a cheerful atmosphere might be generated. The output of this step is fully original music that matches the user's emotional experience.

[0385] Step 5:

[0386] Finally, the server saves the generated lyrics and music to the user's personal digital storage device. This allows the user to relive the musical experience at any time. The saved data includes the generated lyrics and music files.

[0387] (Application Example 2)

[0388] 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."

[0389] In modern times, there is a growing need for deeper self-expression by recording emotional moments experienced in daily life and expressing and sharing them as music. However, there is a lack of effective systems for comprehensively analyzing emotions and automatically generating empathetic music or poetry based on those analyses. The challenge lies in easily creating original musical experiences that appropriately reflect users' emotions, resonate with individual experiences, and then easily share them through digital media.

[0390] 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.

[0391] In this invention, the server includes means for analyzing emotional information entered by the user and identifying multifaceted emotional categories, means for generating original verses and sounds, and means for storing the generated verses and sounds on a recording medium dedicated to the user and making them shareable via digital media. As a result, the user can have their emotional moments analyzed with high accuracy, express themselves richly through original music, and form emotional connections with others through sharing and re-experiencing.

[0392] "Emotional information" refers to audio, text, and image data that users input to express the emotions they felt at a particular moment.

[0393] An "emotional category" is a classification of emotions identified based on analyzed emotional information.

[0394] "Verse" refers to original lyrics or poetic writing generated based on emotional categories.

[0395] "Acoustics" refers to music or a combination of sounds created to fit the generated verse.

[0396] A "recording medium" is a digital data storage device for saving the generated verse and sound.

[0397] "Multifaceted analysis" is a method that recognizes emotions with greater accuracy by integrating and analyzing audio, text, and images.

[0398] "Digital media" refers to electronic communication platforms used by users to share generated music, poetry, and other forms of expression with others.

[0399] "Preferences" refer to a user's personal tastes regarding music genres or sound formats they particularly enjoy.

[0400] The system implementing this invention includes a terminal for the user to input emotional information and a server for emotion analysis and managing the generated content. The user uses a terminal such as a smartphone or computer glasses to input the emotions they feel at a particular moment as voice, text, or images. This data is transmitted from the terminal to the server.

[0401] The server uses the "speech_recognition" library for speech processing, converting speech into text data. This makes it possible to extract emotions from the speech data. Next, the "transformers" library is used to analyze the emotions in the text data, identifying multifaceted emotional categories. At the same time, the "emotion_recognition_module" is used to analyze the emotions in image data, analyzing facial expressions and extracting emotional information from the images.

[0402] The server integrates these analysis results to determine an emotion category and instructs the generative AI model to generate a verse that fits the emotion category as a "prompt sentence." This generation process uses generative AI models such as "GPT-2" to create creative verses that reflect the user's emotions. Furthermore, it manipulates a music generation engine to generate sounds that match the generated verses, adjusting them based on the user's preferences and different sound formats.

[0403] The verses and sounds generated during this process are saved to a user-specific digital storage medium. Users can then share these results with others through digital media. Furthermore, by using prompts such as "Create lyrics that match the joy and excitement you felt while traveling," users can generate creative content tailored to specific emotions. This allows users to re-experience their emotional experiences as music.

[0404] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0405] Step 1:

[0406] The user uses a device to input emotions they feel at a specific moment as voice, text, and images. The input data is sent from the device to the server. The input in this step consists of the user's voice, text, and image data, which are used for subsequent processing.

[0407] Step 2:

[0408] The server uses the "speech_recognition" library to convert audio data into text. It analyzes the audio file and generates a string using speech recognition technology. The output is the converted text data.

[0409] Step 3:

[0410] The server uses a natural language processing engine to identify sentiment categories for both the converted text and the text directly entered by the user. Specifically, the "transformers" library performs sentiment analysis and extracts sentiment labels such as positive, negative, and neutral from the text. The output of this step is the identified sentiment category.

[0411] Step 4:

[0412] The server uses the "emotion_recognition_module" with image data to analyze the user's facial expressions. Through this analysis, it obtains emotion data extracted from the image. The output of this step is emotion information based on the image.

[0413] Step 5:

[0414] The server integrates the emotion data obtained from steps 3 and 4 and determines a synthetic emotion category. Based on this synthetic emotion information, it inputs the emotion category into the generative AI model and generates a prompt sentence. The output is a prompt sentence formatted for the generative AI model.

[0415] Step 6:

[0416] The server uses a prompt to generate a verse using a generative AI model (e.g., "GPT-2"). The AI ​​model creates creative text based on the provided sentiment categories. The output of this step is the generated verse.

[0417] Step 7:

[0418] The server operates a music generation engine to produce sounds that match the generated verse. It creates melodies and accompaniments based on user preferences and context, and adjusts the musical style. The output of this step is the generated sound data.

[0419] Step 8:

[0420] The server saves the generated verses and sounds to a user-specific digital storage medium. The user can later play back this content and share it with others via digital media. The output of this step is the saved media file.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] [Third Embodiment]

[0425] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0426] 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.

[0427] 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).

[0428] 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.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] 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".

[0437] This invention relates to a system that generates original music using emotional information and image data entered by the user. The user first enters emotions related to specific moments or events in their daily life in text format via a terminal. They can also add image data capturing those moments as needed.

[0438] These input data are sent from the terminal to the server. The server uses natural language processing technology to analyze the emotional information and classify the input text into specific emotional categories. This analysis accurately identifies the emotions the user was experiencing at that moment. Furthermore, image data is also used in the analysis to improve the accuracy of emotion identification.

[0439] Next, the server generates original lyrics based on the identified emotions. This lyric generation process selects appropriate themes and vocabulary to match the emotions, creating text that best expresses the user's feelings. This lyric generation is a core process for richly recreating specific moments that the user wants to remember.

[0440] Furthermore, the server generates music based on these lyrics. The music generation process considers melodies, harmonies, and rhythms that match the emotional category, musically expressing the user's emotions. It's also possible to adjust the generated music to different genres according to the user's preferences.

[0441] The generated lyrics and music are stored in a user-specific digital album. This allows the user to play the generated content anytime and relive those special emotions. The device presents the user with playback options for the generated music, providing a personalized music experience.

[0442] As a concrete example, consider a scenario where a user wants to record an emotional moment from their graduation ceremony. In this case, the user inputs "graduation ceremony" and a photo, and describes their emotions as "gratitude" or "pride." The server analyzes this information, assigns a specific emotion category, and generates corresponding lyrics. Then, it generates an uplifting melody or moving accompaniment to match the emotion, and saves it as a final musical work.

[0443] This invention provides a novel method that allows users to save important moments in their lives as music and relive them emotionally.

[0444] The following describes the processing flow.

[0445] Step 1:

[0446] The user inputs emotional information as text from their device and selects relevant photo data as needed. They then press the send button to prepare to send this data to the server.

[0447] Step 2:

[0448] The terminal packages the text and image data entered by the user and sends it to the server using an encrypted communication protocol.

[0449] Step 3:

[0450] The server passes the received emotional text data to a natural language processing engine, which analyzes the user's emotions. This identifies emotional categories such as joy, sadness, and surprise from the text.

[0451] Step 4:

[0452] The server uses an AI generative model to generate original lyrics based on identified emotion categories. The AI ​​generative model selects themes and appropriate vocabulary corresponding to the emotion and creates lyrics that express that emotion.

[0453] Step 5:

[0454] The server activates a music generation engine based on the generated lyrics. This engine designs melodies, harmonies, and rhythms that match the emotion category, expressing the emotion in musical form.

[0455] Step 6:

[0456] The server saves the generated music data to the user's personalized digital album and updates the album chronologically. This allows users to organize special moments in their lives as music.

[0457] Step 7:

[0458] The server sends the generated lyrics and music data to the terminal, making them accessible to the user.

[0459] Step 8:

[0460] The device provides the user with playback options for the generated music, allowing them to play the music whenever they choose and relive special moments.

[0461] (Example 1)

[0462] 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."

[0463] Traditionally, systems that analyze user emotions and generate creative works based on them have had limitations in terms of the accuracy of emotion recognition and the quality of the generated content. Furthermore, ensuring the security of emotional information transmission and data conversion, and generating optimal content tailored to diverse user preferences, have been challenges.

[0464] 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.

[0465] In this invention, the server includes means for analyzing emotional information entered by the user and identifying an emotional category, means for generating an original string based on the emotional category, and means for generating audio that matches the string. This enables high-precision analysis of the user's emotions and the generation of high-quality content that matches those emotions.

[0466] A "user" is an entity that utilizes this system, inputting emotional information and additional data to request the generation of original content.

[0467] "Emotional information" refers to information about a user's emotional state or events, which they input as text.

[0468] An "emotional category" is a concept that indicates a specific group of emotions to which analyzed emotional information is classified.

[0469] "Strings of characters" refer to linguistic expressions generated based on emotional information, and take the form of lyrics or poetry.

[0470] "Sound" refers to music or audio information generated in a format that matches a string of characters.

[0471] "Recording medium" refers to the digital storage or physical media on which the generated content is stored.

[0472] "Natural language processing technology" refers to the technology that uses computers to understand and manipulate human language.

[0473] "Image analysis technology" refers to computational methods for extracting information from digital images.

[0474] A "generative model" refers to an algorithmic framework for automatically generating new content based on input data.

[0475] This invention is a system that allows users to record specific moments in their daily lives and generate original audio content that reflects their emotions. Users first use a terminal to input emotional information in text format. They can also upload image data capturing the moment if necessary. This data is transmitted from the terminal to a server. To ensure security during data transmission, encryption technologies such as HTTP are used.

[0476] The server efficiently analyzes emotional information using natural language processing techniques and identifies emotional categories. This analysis utilizes toolkits such as natural language processing APIs. Furthermore, image analysis techniques are used to analyze image data and improve the accuracy of emotional analysis. A general-purpose library is used as a digital image processing framework for the analysis.

[0477] After identifying an emotion category, the server uses a generative model to generate original strings based on that emotion. Various generative AI algorithms are used for this generation. Furthermore, in the process of generating speech based on the generated strings, a speech generation model is used to select a tone and structure that matches the emotion.

[0478] The generated text and audio are saved to a user-specific storage medium, which the user can then play back through their device to enjoy a personalized experience. For example, if a user wants to record a fun moment with their family, they can input text such as "Today was so much fun. I'm so happy to be with my family" along with an image capturing that scene, and audio content that captures that precious family memory will be generated.

[0479] Examples of prompts for a generative AI model are as follows:

[0480] "Text: Today was really fun. I'm happy to spend time with my family. Emotion: Joy, happiness. Please generate text and audio based on this emotion."

[0481] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0482] Step 1:

[0483] Users use their devices to input emotional information about specific moments in their daily lives in text format, and sometimes attach image data. The input information is prepared as emotional information in text format and as visual data in image format. This input data is then prepared for transmission to the server using an information transmission protocol.

[0484] Step 2:

[0485] The device sends emotional information and image data received from the user to the server. The data sent includes the input text and images. The data is transmitted using a secure communication protocol (e.g., HTTPS), and the server is prepared for analysis.

[0486] Step 3:

[0487] The server analyzes the received text data using natural language processing tools to identify sentiment categories. In this process, text data is used as input, and the identified sentiment categories are provided as output. For example, sentiments are classified as positive, negative, neutral, etc., based on keywords extracted from the text.

[0488] Step 4:

[0489] The server analyzes the transmitted image data using image analysis technology. Here, image data is taken as input, and selected information to reinforce emotions is extracted as output. Specifically, objects and scenes within the image are recognized, and this is integrated with the results of text analysis to make the emotional judgment more accurate.

[0490] Step 5:

[0491] The server uses a generative AI model to generate original strings using already identified emotion categories. The input here is the emotion category, and the output is the newly generated string. This process selects themes and vocabulary appropriate to the emotion.

[0492] Step 6:

[0493] The server uses a speech generation model to create music based on the generated string. The input is the generated string, and the output is audio data. In speech generation, the tone, melody, and harmony that match the emotion are considered, and music that reflects the user's feelings is created.

[0494] Step 7:

[0495] The server saves the generated text and audio to a user-specific storage medium. The saved data consists of text and audio. This allows the user to later play back the generated content through their device and relive the emotions they felt at the time.

[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] There is a lack of technological means to reproduce and record users' emotional experiences in a deeper, more personalized way. Furthermore, there is a need to provide generated content in a form that can be shared not only individually but also socially.

[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 analyzing emotional information entered by a user and identifying an emotional classification; means for creating an original musical work based on the emotional classification; and means for storing and sharing the musical work on a digital recording medium. This enables users to embody their emotional experiences as original music and share it with society.

[0501] "Emotional information" refers to data entered by a user to express the emotions they feel in response to a particular moment or event.

[0502] "Analysis" is the process of analyzing information provided by users and deriving specific conclusions or results from that information.

[0503] "Emotional classification" is the act of classifying emotional information into specific categories based on analysis results.

[0504] An "original musical work" is music that is specially generated based on the user's emotional classification and reflects the user's personality and experiences.

[0505] A "digital recording medium" is a medium that can store information electronically and is used to store generated musical works.

[0506] "Sharing" is the act of sharing generated content with others, whether through social means or information transmission technologies.

[0507] The system for carrying out this invention has a series of processes for analyzing emotional information and image data entered by a user, generating original musical works based on that information, and saving and sharing them on a digital recording medium. This system mainly consists of a server, a terminal, and related software.

[0508] Users input text information expressing specific emotions and associated image data using their devices. This data is collected on the device and sent to a server. Typically, this process utilizes an internet connection and cloud-based data storage.

[0509] The server analyzes the received data using natural language processing libraries developed in Python (e.g., NLTK and Transformers). This analysis categorizes emotional information into specific emotion classifications, preparing it for subsequent data processing. Furthermore, it uses TensorFlow and PyTorch to analyze image data and integrate it with text data to improve the accuracy of emotion identification.

[0510] Next, the server generates lyrics using a generative AI model. Here, the OpenAI API is applied, and lyrics are created with appropriate themes and vocabulary based on sentiment classification. Subsequently, the music generation module utilizes librosa and scikit-learn to create melodies and harmonies that match the user's emotions.

[0511] The generated musical works are stored on digital recording media, and users can play and share them on their devices. This sharing is typically done through information transmission technologies such as social networking services (SNS).

[0512] As a concrete example, consider a scenario where a user inputs a photo of a sunrise taken during a trip and provides emotional information such as "awe-inspiring." Based on this input, the system generates lyrics and music that reflect the user's feelings. The generated music, with its refreshing morning melody, is themed around the inspiring start of a day.

[0513] As an example of a prompt statement,

[0514] "Let's turn your emotions into music! Add a photo you've taken and tell us what you're feeling in one word (e.g., 'joy'). We'll create an original musical piece for you."

[0515] This provides instructions to the user, encouraging them to quickly input emotional information.

[0516] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0517] Step 1:

[0518] The user inputs emotional information and image data using a device. A prompt appears on the device screen for inputting emotions, where the user uploads emotional information in text format and the captured image. The input data is then prepared by the device to be sent to a cloud server.

[0519] Step 2:

[0520] The server receives data sent from the terminal. The server analyzes the received sentiment information using natural language processing techniques. Using the Python NLTK library, text data is processed with a sentiment classification algorithm and classified into specific sentiment categories. The input is string data, and the output is a sentiment category label.

[0521] Step 3:

[0522] The server analyzes image data using TensorFlow and identifies emotional elements extracted from the images. This process is performed by an image recognition model, and its consistency with the emotional categories obtained from text analysis is verified. The input is image data, and the output is additional emotional feature information.

[0523] Step 4:

[0524] The server generates original lyrics based on emotion categories. The lyrics are automatically generated using an AI model utilizing the OpenAI API. This process involves selecting themes and words that match the emotions. The input is the emotion category labels, and the output is the generated lyric text.

[0525] Step 5:

[0526] The server creates music to match the generated lyrics. It utilizes librosa and scikit-learn to build a music generation module, synthesizing melodies and harmonies that correspond to emotions. The input is the generated lyrics text, and the output is in music file format.

[0527] Step 6:

[0528] The server saves the final musical work to a digital recording medium. This music is uploaded to the user's digital storage, and access is managed. The user can play this music through their device. The input is music data, and the output is the saved music file.

[0529] Step 7:

[0530] Users share their generated music works with others through information transmission technology. Sharing takes place via social media or email, and users do so using their device's sharing options. The input is a saved music file, and the output is a music file placed in a state where other users can access it.

[0531] 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.

[0532] This invention relates to a system equipped with an emotion engine that analyzes emotional information, image data, and voice input entered by a user to accurately recognize emotions. The user uses a terminal to input emotions felt at a specific moment as text, voice, or a photograph. All input data is transmitted from the terminal to the server.

[0533] Upon receiving this information, the server initiates a series of emotion recognition processes. Using an emotion engine, it extracts emotion categories from the input text using natural language processing techniques. Simultaneously, it analyzes facial expressions from image data and detects emotion signals from voice input. This enables multifaceted emotion recognition.

[0534] The recognized emotion information is used in the process of generating appropriate lyrics to match the user's intended emotions. The server uses an AI model to automatically generate original lyrics that are most relevant and reflect the user's emotions, based on the identified emotion categories. This lyric generation process is crucial for more deeply expressing the user's emotional experience.

[0535] Next, the server uses the generated lyric information to operate the music generation engine, creating a melody and accompaniment that matches the emotions. Furthermore, it can adjust the musical style based on the user's musical genre preferences. This results in a musical experience that resonates with the individual's emotions.

[0536] The generated lyrics and music are saved in a user-exclusive digital album, allowing the user to relive the experience at any time. This process offers a new experience that enables users to record and reflect on special emotional moments in their lives.

[0537] As a concrete example, consider a scenario where a user wants to record the fun and excitement they felt during a trip with friends. In this case, the user takes photos, inputs phrases like "the best trip" or "an unforgettable moment," and possibly adds cheerful voice comments. The server uses this multimodal data to perform a complex sentiment analysis and generates lyrics and music that express those emotions. Finally, these creations are saved in a digital album, allowing the user to relive those special moments through music.

[0538] The following describes the processing flow.

[0539] Step 1:

[0540] The user uses the device to input text, audio, or photos that express their emotions. Once input is complete, they press the send button to prepare these data for the next processing stage.

[0541] Step 2:

[0542] The terminal packages the input data (text, audio, images) and sends it to the server using a secure data communication protocol. This process ensures that information is transmitted to the server without external interference.

[0543] Step 3:

[0544] The server passes the received text data to a natural language processing engine for sentiment analysis. It extracts sentiment categories (e.g., joy, surprise, sadness, etc.) from the text to identify the main emotions the user is feeling.

[0545] Step 4:

[0546] The server also performs sentiment analysis on audio data. Using a speech recognition engine, it analyzes emotional signals from voice tone and word choice, and integrates them with the results from the text data.

[0547] Step 5:

[0548] The server uses image data to analyze the user's facial expressions. Using advanced computer vision technology, it extracts emotions from the facial expressions in the image and integrates them with the previously obtained emotional information.

[0549] Step 6:

[0550] The server uses an AI model to generate original lyrics based on integrated emotional information. These lyrics are designed to match specific emotional categories and accurately reflect the user's feelings.

[0551] Step 7:

[0552] The server activates a music generation engine based on the generated lyrics, creating music that best suits the emotions. Furthermore, it selects a music genre that reflects the user's preferences and fine-tunes the details of the music.

[0553] Step 8:

[0554] The server saves the completed lyrics and music data to the user's personal digital album. This allows the user to relive those special emotional moments through music at any time.

[0555] Step 9:

[0556] The device provides the user with the option to play the generated music. The user can use this option to listen to the music again and re-experience the emotions.

[0557] (Example 2)

[0558] 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."

[0559] The problem lies in the lack of methods to integrate input data consisting of multiple formats, such as emotional information, images, and audio, to analyze the user's multifaceted emotional state and provide a music experience based on that analysis. In particular, the process of expressing individual emotions and generating music appropriate to them is cumbersome, and there is a challenge in customizing music to match the user's preferences.

[0560] 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.

[0561] In this invention, the server includes means for analyzing emotional information input through a device used by the user and extracting an emotional classification; means for generating a unique sentence based on the emotional classification; and means for creating a sound that matches the sentence. This enables emotional analysis integrating multiple data formats, as well as the automatic generation and storage of music tailored to the user's individual emotional experience.

[0562] "Device" refers to an electronic device used by users to input emotional information such as text, images, and audio.

[0563] "Emotional information" refers to data that expresses emotions such as joy, sadness, or surprise that a user felt at a specific moment, in one of three forms: text, images, or audio.

[0564] "Emotion classification" refers to the emotional categories extracted from input emotional information through natural language processing, image analysis, and voice analysis.

[0565] "Methods for generating text" refers to the process of automatically creating lyrics or text that reflect emotions using AI technology based on emotion classification.

[0566] "Means of creating sound" refers to the music generation process that creates melodies and accompaniments that fit the generated sentences.

[0567] A "digital storage device" refers to a recording medium that saves generated music and lyrics for each user, allowing them to be played back later.

[0568] "Song format" refers to different methods of musical expression depending on the genre and style of music, and in particular includes musical characteristics that can be adjusted according to the user's preferences.

[0569] "Facial expression analysis" refers to a technology that analyzes the features of a user's face from image data and identifies their emotional state.

[0570] "Audio signals" refer to the patterns and characteristics of speech necessary to detect intonation and emotion from the audio data input by the user.

[0571] This invention is a system that analyzes emotional information input from a user's device and generates music based on that information. Users can input emotionally related text, images, and audio through their device. All of this input data is transmitted to a server in digital format. The device can be an electronic device such as a smartphone or tablet.

[0572] Upon receiving data, the server first analyzes the emotional information using techniques such as natural language processing, image processing, and speech analysis. For natural language processing, software libraries for emotion analysis, such as open-source NLP toolkits, are used. Image data is analyzed using image analysis algorithms to determine facial expressions, and speech recognition software is used to analyze speech signals from audio data.

[0573] Based on the analyzed emotion data, the server uses a generative AI model to generate original lyrics. In this process, the AI ​​creates words and phrases according to the emotion category. The generated lyrics are fed into a music generation engine, which creates music by appropriately combining melody and accompaniment. Here, an AI-based composition system is used, adjusting the genre and style to match the user's musical preferences.

[0574] As a concrete example, consider a scenario where a user wants to record the enjoyment they felt while traveling with friends. The user takes fun photos with their smartphone, enters text such as "the best moment," and possibly records their joyful voice. When this data is sent to a server, the server comprehensively analyzes the emotions from these various data points and generates lyrics and a melody based on the results.

[0575] An example of a prompt message might be, "Please create lyrics that express the joyful feelings you have while traveling." Through this system, users can continuously record and re-experience their individual, emotion-based musical experiences.

[0576] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0577] Step 1:

[0578] Users input emotional information using a device. This input data can be in the form of text, images, audio, or a combination of these. For example, a user might take a photo of a moment they feel "happy" and record a short phrase or comment expressing that emotion. This input data is then converted into a digital format by the device. As a result, multiple emotional data formats are generated and ready to be sent to the server.

[0579] Step 2:

[0580] The server analyzes the digital data received from the terminal. Specifically, text data is analyzed using natural language processing techniques to extract emotion classifications from the input words. Image data is analyzed using image processing algorithms to determine emotions from facial features. Audio data is analyzed using speech analysis software to determine intonation from the audio signal and infer emotions. The output of this step is an integrated emotion profile derived from text, images, and audio.

[0581] Step 3:

[0582] The server generates original lyrics using a generative AI model based on an integrated emotional profile. This process creates words and phrases related to emotional categories. Specifically, the AI ​​model selects positive words to express "joy" and forms creative sentences based on prompt sentences. The generated lyrics are the output of this step.

[0583] Step 4:

[0584] Based on the generated lyrics, the server uses a music generation engine to create a melody and accompaniment. Here, the user's musical genre preferences are taken into consideration, and the song format is adjusted. For example, a pop song with a cheerful atmosphere might be generated. The output of this step is fully original music that matches the user's emotional experience.

[0585] Step 5:

[0586] Finally, the server saves the generated lyrics and music to the user's personal digital storage device. This allows the user to relive the musical experience at any time. The saved data includes the generated lyrics and music files.

[0587] (Application Example 2)

[0588] 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."

[0589] In modern times, there is a growing need for deeper self-expression by recording emotional moments experienced in daily life and expressing and sharing them as music. However, there is a lack of effective systems for comprehensively analyzing emotions and automatically generating empathetic music or poetry based on those analyses. The challenge lies in easily creating original musical experiences that appropriately reflect users' emotions, resonate with individual experiences, and then easily share them through digital media.

[0590] 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.

[0591] In this invention, the server includes means for analyzing emotional information entered by the user and identifying multifaceted emotional categories, means for generating original verses and sounds, and means for storing the generated verses and sounds on a recording medium dedicated to the user and making them shareable via digital media. As a result, the user can have their emotional moments analyzed with high accuracy, express themselves richly through original music, and form emotional connections with others through sharing and re-experiencing.

[0592] "Emotional information" refers to audio, text, and image data that users input to express the emotions they felt at a particular moment.

[0593] An "emotional category" is a classification of emotions identified based on analyzed emotional information.

[0594] "Verse" refers to original lyrics or poetic writing generated based on emotional categories.

[0595] "Acoustics" refers to music or a combination of sounds created to fit the generated verse.

[0596] A "recording medium" is a digital data storage device for saving the generated verse and sound.

[0597] "Multifaceted analysis" is a method that recognizes emotions with greater accuracy by integrating and analyzing audio, text, and images.

[0598] "Digital media" refers to electronic communication platforms used by users to share generated music, poetry, and other forms of expression with others.

[0599] "Preferences" refer to a user's personal tastes regarding music genres or sound formats they particularly enjoy.

[0600] The system implementing this invention includes a terminal for the user to input emotional information and a server for emotion analysis and managing the generated content. The user uses a terminal such as a smartphone or computer glasses to input the emotions they feel at a particular moment as voice, text, or images. This data is transmitted from the terminal to the server.

[0601] The server uses the "speech_recognition" library for speech processing, converting speech into text data. This makes it possible to extract emotions from the speech data. Next, the "transformers" library is used to analyze the emotions in the text data, identifying multifaceted emotional categories. At the same time, the "emotion_recognition_module" is used to analyze the emotions in image data, analyzing facial expressions and extracting emotional information from the images.

[0602] The server integrates these analysis results to determine an emotion category and instructs the generative AI model to generate a verse that fits the emotion category as a "prompt sentence." This generation process uses generative AI models such as "GPT-2" to create creative verses that reflect the user's emotions. Furthermore, it manipulates a music generation engine to generate sounds that match the generated verses, adjusting them based on the user's preferences and different sound formats.

[0603] The verses and sounds generated during this process are saved to a user-specific digital storage medium. Users can then share these results with others through digital media. Furthermore, by using prompts such as "Create lyrics that match the joy and excitement you felt while traveling," users can generate creative content tailored to specific emotions. This allows users to re-experience their emotional experiences as music.

[0604] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0605] Step 1:

[0606] The user uses a device to input emotions they feel at a specific moment as voice, text, and images. The input data is sent from the device to the server. The input in this step consists of the user's voice, text, and image data, which are used for subsequent processing.

[0607] Step 2:

[0608] The server uses the "speech_recognition" library to convert audio data into text. It analyzes the audio file and generates a string using speech recognition technology. The output is the converted text data.

[0609] Step 3:

[0610] The server uses a natural language processing engine to identify sentiment categories for both the converted text and the text directly entered by the user. Specifically, the "transformers" library performs sentiment analysis and extracts sentiment labels such as positive, negative, and neutral from the text. The output of this step is the identified sentiment category.

[0611] Step 4:

[0612] The server uses the "emotion_recognition_module" with image data to analyze the user's facial expressions. Through this analysis, it obtains emotion data extracted from the image. The output of this step is emotion information based on the image.

[0613] Step 5:

[0614] The server integrates the emotion data obtained from steps 3 and 4 and determines a synthetic emotion category. Based on this synthetic emotion information, it inputs the emotion category into the generative AI model and generates a prompt sentence. The output is a prompt sentence formatted for the generative AI model.

[0615] Step 6:

[0616] The server uses a prompt to generate a verse using a generative AI model (e.g., "GPT-2"). The AI ​​model creates creative text based on the provided sentiment categories. The output of this step is the generated verse.

[0617] Step 7:

[0618] The server operates a music generation engine to produce sounds that match the generated verse. It creates melodies and accompaniments based on user preferences and context, and adjusts the musical style. The output of this step is the generated sound data.

[0619] Step 8:

[0620] The server saves the generated verses and sounds to a user-specific digital storage medium. The user can later play back this content and share it with others via digital media. The output of this step is the saved media file.

[0621] 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.

[0622] 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.

[0623] 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.

[0624] [Fourth Embodiment]

[0625] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0626] 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.

[0627] 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).

[0628] 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.

[0629] 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.

[0630] 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).

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] 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.

[0637] 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".

[0638] This invention relates to a system that generates original music using emotional information and image data entered by the user. The user first enters emotions related to specific moments or events in their daily life in text format via a terminal. They can also add image data capturing those moments as needed.

[0639] These input data are sent from the terminal to the server. The server uses natural language processing technology to analyze the emotional information and classify the input text into specific emotional categories. This analysis accurately identifies the emotions the user was experiencing at that moment. Furthermore, image data is also used in the analysis to improve the accuracy of emotion identification.

[0640] Next, the server generates original lyrics based on the identified emotions. This lyric generation process selects appropriate themes and vocabulary to match the emotions, creating text that best expresses the user's feelings. This lyric generation is a core process for richly recreating specific moments that the user wants to remember.

[0641] Furthermore, the server generates music based on these lyrics. The music generation process considers melodies, harmonies, and rhythms that match the emotional category, musically expressing the user's emotions. It's also possible to adjust the generated music to different genres according to the user's preferences.

[0642] The generated lyrics and music are stored in a user-specific digital album. This allows the user to play the generated content anytime and relive those special emotions. The device presents the user with playback options for the generated music, providing a personalized music experience.

[0643] As a concrete example, consider a scenario where a user wants to record an emotional moment from their graduation ceremony. In this case, the user inputs "graduation ceremony" and a photo, and describes their emotions as "gratitude" or "pride." The server analyzes this information, assigns a specific emotion category, and generates corresponding lyrics. Then, it generates an uplifting melody or moving accompaniment to match the emotion, and saves it as a final musical work.

[0644] This invention provides a novel method that allows users to save important moments in their lives as music and relive them emotionally.

[0645] The following describes the processing flow.

[0646] Step 1:

[0647] The user inputs emotional information as text from their device and selects relevant photo data as needed. They then press the send button to prepare to send this data to the server.

[0648] Step 2:

[0649] The terminal packages the text and image data entered by the user and sends it to the server using an encrypted communication protocol.

[0650] Step 3:

[0651] The server passes the received emotional text data to a natural language processing engine, which analyzes the user's emotions. This identifies emotional categories such as joy, sadness, and surprise from the text.

[0652] Step 4:

[0653] The server uses an AI generative model to generate original lyrics based on identified emotion categories. The AI ​​generative model selects themes and appropriate vocabulary corresponding to the emotion and creates lyrics that express that emotion.

[0654] Step 5:

[0655] The server activates a music generation engine based on the generated lyrics. This engine designs melodies, harmonies, and rhythms that match the emotion category, expressing the emotion in musical form.

[0656] Step 6:

[0657] The server saves the generated music data to the user's personalized digital album and updates the album chronologically. This allows users to organize special moments in their lives as music.

[0658] Step 7:

[0659] The server sends the generated lyrics and music data to the terminal, making them accessible to the user.

[0660] Step 8:

[0661] The device provides the user with playback options for the generated music, allowing them to play the music whenever they choose and relive special moments.

[0662] (Example 1)

[0663] 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".

[0664] Traditionally, systems that analyze user emotions and generate creative works based on them have had limitations in terms of the accuracy of emotion recognition and the quality of the generated content. Furthermore, ensuring the security of emotional information transmission and data conversion, and generating optimal content tailored to diverse user preferences, have been challenges.

[0665] 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.

[0666] In this invention, the server includes means for analyzing emotional information entered by the user and identifying an emotional category, means for generating an original string based on the emotional category, and means for generating audio that matches the string. This enables high-precision analysis of the user's emotions and the generation of high-quality content that matches those emotions.

[0667] A "user" is an entity that utilizes this system, inputting emotional information and additional data to request the generation of original content.

[0668] "Emotional information" refers to information about a user's emotional state or events, which they input as text.

[0669] An "emotional category" is a concept that indicates a specific group of emotions to which analyzed emotional information is classified.

[0670] "Strings of characters" refer to linguistic expressions generated based on emotional information, and take the form of lyrics or poetry.

[0671] "Sound" refers to music or audio information generated in a format that matches a string of characters.

[0672] "Recording medium" refers to the digital storage or physical media on which the generated content is stored.

[0673] "Natural language processing technology" refers to the technology that uses computers to understand and manipulate human language.

[0674] "Image analysis technology" refers to computational methods for extracting information from digital images.

[0675] A "generative model" refers to an algorithmic framework for automatically generating new content based on input data.

[0676] This invention is a system that allows users to record specific moments in their daily lives and generate original audio content that reflects their emotions. Users first use a terminal to input emotional information in text format. They can also upload image data capturing the moment if necessary. This data is transmitted from the terminal to a server. To ensure security during data transmission, encryption technologies such as HTTP are used.

[0677] The server efficiently analyzes emotional information using natural language processing techniques and identifies emotional categories. This analysis utilizes toolkits such as natural language processing APIs. Furthermore, image analysis techniques are used to analyze image data and improve the accuracy of emotional analysis. A general-purpose library is used as a digital image processing framework for the analysis.

[0678] After identifying an emotion category, the server uses a generative model to generate original strings based on that emotion. Various generative AI algorithms are used for this generation. Furthermore, in the process of generating speech based on the generated strings, a speech generation model is used to select a tone and structure that matches the emotion.

[0679] The generated text and audio are saved to a user-specific storage medium, which the user can then play back through their device to enjoy a personalized experience. For example, if a user wants to record a fun moment with their family, they can input text such as "Today was so much fun. I'm so happy to be with my family" along with an image capturing that scene, and audio content that captures that precious family memory will be generated.

[0680] Examples of prompts for a generative AI model are as follows:

[0681] "Text: Today was really fun. I'm happy to spend time with my family. Emotion: Joy, happiness. Please generate text and audio based on this emotion."

[0682] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0683] Step 1:

[0684] Users use their devices to input emotional information about specific moments in their daily lives in text format, and sometimes attach image data. The input information is prepared as emotional information in text format and as visual data in image format. This input data is then prepared for transmission to the server using an information transmission protocol.

[0685] Step 2:

[0686] The device sends emotional information and image data received from the user to the server. The data sent includes the input text and images. The data is transmitted using a secure communication protocol (e.g., HTTPS), and the server is prepared for analysis.

[0687] Step 3:

[0688] The server analyzes the received text data using natural language processing tools to identify sentiment categories. In this process, text data is used as input, and the identified sentiment categories are provided as output. For example, sentiments are classified as positive, negative, neutral, etc., based on keywords extracted from the text.

[0689] Step 4:

[0690] The server analyzes the transmitted image data using image analysis technology. Here, image data is taken as input, and selected information to reinforce emotions is extracted as output. Specifically, objects and scenes within the image are recognized, and this is integrated with the results of text analysis to make the emotional judgment more accurate.

[0691] Step 5:

[0692] The server uses a generative AI model to generate original strings using already identified emotion categories. The input here is the emotion category, and the output is the newly generated string. This process selects themes and vocabulary appropriate to the emotion.

[0693] Step 6:

[0694] The server uses a speech generation model to create music based on the generated string. The input is the generated string, and the output is audio data. In speech generation, the tone, melody, and harmony that match the emotion are considered, and music that reflects the user's feelings is created.

[0695] Step 7:

[0696] The server saves the generated text and audio to a user-specific storage medium. The saved data consists of text and audio. This allows the user to later play back the generated content through their device and relive the emotions they felt at the time.

[0697] (Application Example 1)

[0698] 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".

[0699] There is a lack of technological means to reproduce and record users' emotional experiences in a deeper, more personalized way. Furthermore, there is a need to provide generated content in a form that can be shared not only individually but also socially.

[0700] 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.

[0701] In this invention, the server includes means for analyzing emotional information entered by a user and identifying an emotional classification; means for creating an original musical work based on the emotional classification; and means for storing and sharing the musical work on a digital recording medium. This enables users to embody their emotional experiences as original music and share it with society.

[0702] "Emotional information" refers to data entered by a user to express the emotions they feel in response to a particular moment or event.

[0703] "Analysis" is the process of analyzing information provided by users and deriving specific conclusions or results from that information.

[0704] "Emotional classification" is the act of classifying emotional information into specific categories based on analysis results.

[0705] An "original musical work" is music that is specially generated based on the user's emotional classification and reflects the user's personality and experiences.

[0706] A "digital recording medium" is a medium that can store information electronically and is used to store generated musical works.

[0707] "Sharing" is the act of sharing generated content with others, whether through social means or information transmission technologies.

[0708] The system for carrying out this invention has a series of processes for analyzing emotional information and image data entered by a user, generating original musical works based on that information, and saving and sharing them on a digital recording medium. This system mainly consists of a server, a terminal, and related software.

[0709] Users input text information expressing specific emotions and associated image data using their devices. This data is collected on the device and sent to a server. Typically, this process utilizes an internet connection and cloud-based data storage.

[0710] The server analyzes the received data using natural language processing libraries developed in Python (e.g., NLTK and Transformers). This analysis categorizes emotional information into specific emotion classifications, preparing it for subsequent data processing. Furthermore, it uses TensorFlow and PyTorch to analyze image data and integrate it with text data to improve the accuracy of emotion identification.

[0711] Next, the server generates lyrics using a generative AI model. Here, the OpenAI API is applied, and lyrics are created with appropriate themes and vocabulary based on sentiment classification. Subsequently, the music generation module utilizes librosa and scikit-learn to create melodies and harmonies that match the user's emotions.

[0712] The generated musical works are stored on digital recording media, and users can play and share them on their devices. This sharing is typically done through information transmission technologies such as social networking services (SNS).

[0713] As a concrete example, consider a scenario where a user inputs a photo of a sunrise taken during a trip and provides emotional information such as "awe-inspiring." Based on this input, the system generates lyrics and music that reflect the user's feelings. The generated music, with its refreshing morning melody, is themed around the inspiring start of a day.

[0714] As an example of a prompt statement,

[0715] "Let's turn your emotions into music! Add a photo you've taken and tell us what you're feeling in one word (e.g., 'joy'). We'll create an original musical piece for you."

[0716] This provides instructions to the user, encouraging them to quickly input emotional information.

[0717] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0718] Step 1:

[0719] The user inputs emotional information and image data using a device. A prompt appears on the device screen for inputting emotions, where the user uploads emotional information in text format and the captured image. The input data is then prepared by the device to be sent to a cloud server.

[0720] Step 2:

[0721] The server receives data sent from the terminal. The server analyzes the received sentiment information using natural language processing techniques. Using the Python NLTK library, text data is processed with a sentiment classification algorithm and classified into specific sentiment categories. The input is string data, and the output is a sentiment category label.

[0722] Step 3:

[0723] The server analyzes image data using TensorFlow and identifies emotional elements extracted from the images. This process is performed by an image recognition model, and its consistency with the emotional categories obtained from text analysis is verified. The input is image data, and the output is additional emotional feature information.

[0724] Step 4:

[0725] The server generates original lyrics based on emotion categories. The lyrics are automatically generated using an AI model utilizing the OpenAI API. This process involves selecting themes and words that match the emotions. The input is the emotion category labels, and the output is the generated lyric text.

[0726] Step 5:

[0727] The server creates music to match the generated lyrics. It utilizes librosa and scikit-learn to build a music generation module, synthesizing melodies and harmonies that correspond to emotions. The input is the generated lyrics text, and the output is in music file format.

[0728] Step 6:

[0729] The server saves the final musical work to a digital recording medium. This music is uploaded to the user's digital storage, and access is managed. The user can play this music through their device. The input is music data, and the output is the saved music file.

[0730] Step 7:

[0731] Users share their generated music works with others through information transmission technology. Sharing takes place via social media or email, and users do so using their device's sharing options. The input is a saved music file, and the output is a music file placed in a state where other users can access it.

[0732] 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.

[0733] This invention relates to a system equipped with an emotion engine that analyzes emotional information, image data, and voice input entered by a user to accurately recognize emotions. The user uses a terminal to input emotions felt at a specific moment as text, voice, or a photograph. All input data is transmitted from the terminal to the server.

[0734] Upon receiving this information, the server initiates a series of emotion recognition processes. Using an emotion engine, it extracts emotion categories from the input text using natural language processing techniques. Simultaneously, it analyzes facial expressions from image data and detects emotion signals from voice input. This enables multifaceted emotion recognition.

[0735] The recognized emotion information is used in the process of generating appropriate lyrics to match the user's intended emotions. The server uses an AI model to automatically generate original lyrics that are most relevant and reflect the user's emotions, based on the identified emotion categories. This lyric generation process is crucial for more deeply expressing the user's emotional experience.

[0736] Next, the server uses the generated lyric information to operate the music generation engine, creating a melody and accompaniment that matches the emotions. Furthermore, it can adjust the musical style based on the user's musical genre preferences. This results in a musical experience that resonates with the individual's emotions.

[0737] The generated lyrics and music are saved in a user-exclusive digital album, allowing the user to relive the experience at any time. This process offers a new experience that enables users to record and reflect on special emotional moments in their lives.

[0738] As a concrete example, consider a scenario where a user wants to record the fun and excitement they felt during a trip with friends. In this case, the user takes photos, inputs phrases like "the best trip" or "an unforgettable moment," and possibly adds cheerful voice comments. The server uses this multimodal data to perform a complex sentiment analysis and generates lyrics and music that express those emotions. Finally, these creations are saved in a digital album, allowing the user to relive those special moments through music.

[0739] The following describes the processing flow.

[0740] Step 1:

[0741] The user uses the device to input text, audio, or photos that express their emotions. Once input is complete, they press the send button to prepare these data for the next processing stage.

[0742] Step 2:

[0743] The terminal packages the input data (text, audio, images) and sends it to the server using a secure data communication protocol. This process ensures that information is transmitted to the server without external interference.

[0744] Step 3:

[0745] The server passes the received text data to a natural language processing engine for sentiment analysis. It extracts sentiment categories (e.g., joy, surprise, sadness, etc.) from the text to identify the main emotions the user is feeling.

[0746] Step 4:

[0747] The server also performs sentiment analysis on audio data. Using a speech recognition engine, it analyzes emotional signals from voice tone and word choice, and integrates them with the results from the text data.

[0748] Step 5:

[0749] The server uses image data to analyze the user's facial expressions. Using advanced computer vision technology, it extracts emotions from the facial expressions in the image and integrates them with the previously obtained emotional information.

[0750] Step 6:

[0751] The server uses an AI model to generate original lyrics based on integrated emotional information. These lyrics are designed to match specific emotional categories and accurately reflect the user's feelings.

[0752] Step 7:

[0753] The server activates a music generation engine based on the generated lyrics, creating music that best suits the emotions. Furthermore, it selects a music genre that reflects the user's preferences and fine-tunes the details of the music.

[0754] Step 8:

[0755] The server saves the completed lyrics and music data to the user's personal digital album. This allows the user to relive those special emotional moments through music at any time.

[0756] Step 9:

[0757] The device provides the user with the option to play the generated music. The user can use this option to listen to the music again and re-experience the emotions.

[0758] (Example 2)

[0759] 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".

[0760] The problem lies in the lack of methods to integrate input data consisting of multiple formats, such as emotional information, images, and audio, to analyze the user's multifaceted emotional state and provide a music experience based on that analysis. In particular, the process of expressing individual emotions and generating music appropriate to them is cumbersome, and there is a challenge in customizing music to match the user's preferences.

[0761] 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.

[0762] In this invention, the server includes means for analyzing emotional information input through a device used by the user and extracting an emotional classification; means for generating a unique sentence based on the emotional classification; and means for creating a sound that matches the sentence. This enables emotional analysis integrating multiple data formats, as well as the automatic generation and storage of music tailored to the user's individual emotional experience.

[0763] "Device" refers to an electronic device used by users to input emotional information such as text, images, and audio.

[0764] "Emotional information" refers to data that expresses emotions such as joy, sadness, or surprise that a user felt at a specific moment, in one of three forms: text, images, or audio.

[0765] "Emotion classification" refers to the emotional categories extracted from input emotional information through natural language processing, image analysis, and voice analysis.

[0766] "Methods for generating text" refers to the process of automatically creating lyrics or text that reflect emotions using AI technology based on emotion classification.

[0767] "Means of creating sound" refers to the music generation process that creates melodies and accompaniments that fit the generated sentences.

[0768] A "digital storage device" refers to a recording medium that saves generated music and lyrics for each user, allowing them to be played back later.

[0769] "Song format" refers to different methods of musical expression depending on the genre and style of music, and in particular includes musical characteristics that can be adjusted according to the user's preferences.

[0770] "Facial expression analysis" refers to a technology that analyzes the features of a user's face from image data and identifies their emotional state.

[0771] "Audio signals" refer to the patterns and characteristics of speech necessary to detect intonation and emotion from the audio data input by the user.

[0772] This invention is a system that analyzes emotional information input from a user's device and generates music based on that information. Users can input emotionally related text, images, and audio through their device. All of this input data is transmitted to a server in digital format. The device can be an electronic device such as a smartphone or tablet.

[0773] Upon receiving data, the server first analyzes the emotional information using techniques such as natural language processing, image processing, and speech analysis. For natural language processing, software libraries for emotion analysis, such as open-source NLP toolkits, are used. Image data is analyzed using image analysis algorithms to determine facial expressions, and speech recognition software is used to analyze speech signals from audio data.

[0774] Based on the analyzed emotion data, the server uses a generative AI model to generate original lyrics. In this process, the AI ​​creates words and phrases according to the emotion category. The generated lyrics are fed into a music generation engine, which creates music by appropriately combining melody and accompaniment. Here, an AI-based composition system is used, adjusting the genre and style to match the user's musical preferences.

[0775] As a concrete example, consider a scenario where a user wants to record the enjoyment they felt while traveling with friends. The user takes fun photos with their smartphone, enters text such as "the best moment," and possibly records their joyful voice. When this data is sent to a server, the server comprehensively analyzes the emotions from these various data points and generates lyrics and a melody based on the results.

[0776] An example of a prompt message might be, "Please create lyrics that express the joyful feelings you have while traveling." Through this system, users can continuously record and re-experience their individual, emotion-based musical experiences.

[0777] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0778] Step 1:

[0779] Users input emotional information using a device. This input data can be in the form of text, images, audio, or a combination of these. For example, a user might take a photo of a moment they feel "happy" and record a short phrase or comment expressing that emotion. This input data is then converted into a digital format by the device. As a result, multiple emotional data formats are generated and ready to be sent to the server.

[0780] Step 2:

[0781] The server analyzes the digital data received from the terminal. Specifically, text data is analyzed using natural language processing techniques to extract emotion classifications from the input words. Image data is analyzed using image processing algorithms to determine emotions from facial features. Audio data is analyzed using speech analysis software to determine intonation from the audio signal and infer emotions. The output of this step is an integrated emotion profile derived from text, images, and audio.

[0782] Step 3:

[0783] The server generates original lyrics using a generative AI model based on an integrated emotional profile. This process creates words and phrases related to emotional categories. Specifically, the AI ​​model selects positive words to express "joy" and forms creative sentences based on prompt sentences. The generated lyrics are the output of this step.

[0784] Step 4:

[0785] Based on the generated lyrics, the server uses a music generation engine to create a melody and accompaniment. Here, the user's musical genre preferences are taken into consideration, and the song format is adjusted. For example, a pop song with a cheerful atmosphere might be generated. The output of this step is fully original music that matches the user's emotional experience.

[0786] Step 5:

[0787] Finally, the server saves the generated lyrics and music to the user's personal digital storage device. This allows the user to relive the musical experience at any time. The saved data includes the generated lyrics and music files.

[0788] (Application Example 2)

[0789] 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".

[0790] In modern times, there is a growing need for deeper self-expression by recording emotional moments experienced in daily life and expressing and sharing them as music. However, there is a lack of effective systems for comprehensively analyzing emotions and automatically generating empathetic music or poetry based on those analyses. The challenge lies in easily creating original musical experiences that appropriately reflect users' emotions, resonate with individual experiences, and then easily share them through digital media.

[0791] 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.

[0792] In this invention, the server includes means for analyzing emotional information entered by the user and identifying multifaceted emotional categories, means for generating original verses and sounds, and means for storing the generated verses and sounds on a recording medium dedicated to the user and making them shareable via digital media. As a result, the user can have their emotional moments analyzed with high accuracy, express themselves richly through original music, and form emotional connections with others through sharing and re-experiencing.

[0793] "Emotional information" refers to audio, text, and image data that users input to express the emotions they felt at a particular moment.

[0794] An "emotional category" is a classification of emotions identified based on analyzed emotional information.

[0795] "Verse" refers to original lyrics or poetic writing generated based on emotional categories.

[0796] "Acoustics" refers to music or a combination of sounds created to fit the generated verse.

[0797] A "recording medium" is a digital data storage device for saving the generated verse and sound.

[0798] "Multifaceted analysis" is a method that recognizes emotions with greater accuracy by integrating and analyzing audio, text, and images.

[0799] "Digital media" refers to electronic communication platforms used by users to share generated music, poetry, and other forms of expression with others.

[0800] "Preferences" refer to a user's personal tastes regarding music genres or sound formats they particularly enjoy.

[0801] The system implementing this invention includes a terminal for the user to input emotional information and a server for emotion analysis and managing the generated content. The user uses a terminal such as a smartphone or computer glasses to input the emotions they feel at a particular moment as voice, text, or images. This data is transmitted from the terminal to the server.

[0802] The server uses the "speech_recognition" library for speech processing, converting speech into text data. This makes it possible to extract emotions from the speech data. Next, the "transformers" library is used to analyze the emotions in the text data, identifying multifaceted emotional categories. At the same time, the "emotion_recognition_module" is used to analyze the emotions in image data, analyzing facial expressions and extracting emotional information from the images.

[0803] The server integrates these analysis results to determine an emotion category and instructs the generative AI model to generate a verse that fits the emotion category as a "prompt sentence." This generation process uses generative AI models such as "GPT-2" to create creative verses that reflect the user's emotions. Furthermore, it manipulates a music generation engine to generate sounds that match the generated verses, adjusting them based on the user's preferences and different sound formats.

[0804] The verses and sounds generated during this process are saved to a user-specific digital storage medium. Users can then share these results with others through digital media. Furthermore, by using prompts such as "Create lyrics that match the joy and excitement you felt while traveling," users can generate creative content tailored to specific emotions. This allows users to re-experience their emotional experiences as music.

[0805] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0806] Step 1:

[0807] The user uses a device to input emotions they feel at a specific moment as voice, text, and images. The input data is sent from the device to the server. The input in this step consists of the user's voice, text, and image data, which are used for subsequent processing.

[0808] Step 2:

[0809] The server uses the "speech_recognition" library to convert audio data into text. It analyzes the audio file and generates a string using speech recognition technology. The output is the converted text data.

[0810] Step 3:

[0811] The server uses a natural language processing engine to identify sentiment categories for both the converted text and the text directly entered by the user. Specifically, the "transformers" library performs sentiment analysis and extracts sentiment labels such as positive, negative, and neutral from the text. The output of this step is the identified sentiment category.

[0812] Step 4:

[0813] The server uses the "emotion_recognition_module" with image data to analyze the user's facial expressions. Through this analysis, it obtains emotion data extracted from the image. The output of this step is emotion information based on the image.

[0814] Step 5:

[0815] The server integrates the emotion data obtained from steps 3 and 4 and determines a synthetic emotion category. Based on this synthetic emotion information, it inputs the emotion category into the generative AI model and generates a prompt sentence. The output is a prompt sentence formatted for the generative AI model.

[0816] Step 6:

[0817] The server uses a prompt to generate a verse using a generative AI model (e.g., "GPT-2"). The AI ​​model creates creative text based on the provided sentiment categories. The output of this step is the generated verse.

[0818] Step 7:

[0819] The server operates a music generation engine to produce sounds that match the generated verse. It creates melodies and accompaniments based on user preferences and context, and adjusts the musical style. The output of this step is the generated sound data.

[0820] Step 8:

[0821] The server saves the generated verses and sounds to a user-specific digital storage medium. The user can later play back this content and share it with others via digital media. The output of this step is the saved media file.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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."

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0843] The following is further disclosed regarding the embodiments described above.

[0844] (Claim 1)

[0845] A means of analyzing emotional information entered by the user and identifying the emotional category,

[0846] A means for generating original lyrics based on the aforementioned emotion categories,

[0847] A means for generating music that matches the aforementioned lyrics,

[0848] Means for storing the generated lyrics and music on a recording medium dedicated to the user,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, comprising means for improving the accuracy of sentiment analysis based on image data input by the user.

[0852] (Claim 3)

[0853] The system according to claim 1, further comprising means for adjusting the generated music into different music genres based on the user's preferences.

[0854] "Example 1"

[0855] (Claim 1)

[0856] A means of analyzing emotional information entered by the user and identifying the emotional category,

[0857] means for generating an original string based on the aforementioned emotion category,

[0858] means for generating audio that matches the aforementioned string,

[0859] Means for saving the generated string and audio to a recording medium dedicated to the user,

[0860] A means of digitizing emotional information and transmitting it securely,

[0861] A method for analyzing the characteristics of emotions using natural language processing technology,

[0862] A means of extending additional information using image analysis technology,

[0863] A means of creating content using generative models,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, comprising means for improving the accuracy of sentiment analysis based on image data input by the user.

[0867] (Claim 3)

[0868] The system according to claim 1, further comprising means for adjusting the generated audio to different music genres based on the user's preferences.

[0869] "Application Example 1"

[0870] (Claim 1)

[0871] A means of analyzing emotional information entered by the user and identifying the emotional classification,

[0872] A means of creating original musical works based on the aforementioned emotional classification,

[0873] A means for saving and sharing the aforementioned musical works on a digital recording medium,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, comprising means for improving the accuracy of sentiment analysis based on image data input by the user.

[0877] (Claim 3)

[0878] The system according to claim 1, comprising means for sharing the generated music with others using information transmission technology.

[0879] "Example 2 of combining an emotion engine"

[0880] (Claim 1)

[0881] A means for analyzing emotional information input through a device used by a user and extracting emotional classifications,

[0882] means for generating a unique sentence based on the aforementioned emotion classification,

[0883] Means for creating a sound that conforms to the above sentence,

[0884] Means for storing the generated text and sound in a user-dedicated digital storage device,

[0885] A means for adjusting the musical format based on the user's preferences when generating the aforementioned sound,

[0886] When the aforementioned emotional information includes text, images, and audio, means for integrating the data obtained from each mode,

[0887] A means for recognizing emotions by analyzing facial expressions from the aforementioned image data,

[0888] A means for analyzing audio signals from the aforementioned audio and recognizing emotions,

[0889] A system that includes this.

[0890] (Claim 2)

[0891] The system according to claim 1, comprising means for integrating multiple formats of data received from the aforementioned device to improve the accuracy of sentiment analysis.

[0892] (Claim 3)

[0893] The system according to claim 1, comprising means that enable a user to re-experience past emotional moments through the generated digital recording.

[0894] "Application example 2 when combining with an emotional engine"

[0895] (Claim 1)

[0896] A means of analyzing emotional information entered by the user and identifying the emotional category,

[0897] A means for generating original verse based on the aforementioned emotional categories,

[0898] means for generating sounds that are appropriate to the aforementioned verse,

[0899] Means for storing the generated verse and sound on a recording medium dedicated to the user,

[0900] A means to improve the accuracy of emotion recognition by performing multifaceted analysis using voice, text, and images as emotional input,

[0901] A means of making the generated verses and sounds shareable through digital media,

[0902] A system that includes this.

[0903] (Claim 2)

[0904] The system according to claim 1, further comprising means for improving the accuracy of sentiment analysis based on image data and audio data entered by the user.

[0905] (Claim 3)

[0906] The system according to claim 1, further comprising means for adjusting the generated sound into different sound formats based on user preferences and usage environment, thereby promoting sharing and re-experiencing. [Explanation of Symbols]

[0907] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of analyzing emotional information entered by the user and identifying the emotional classification, A means of creating original musical works based on the aforementioned emotional classification, A means for saving and sharing the aforementioned musical works on a digital recording medium, A system that includes this.

2. The system according to claim 1, comprising means for improving the accuracy of emotion analysis based on image data input by the user.

3. The system according to claim 1, comprising means for sharing the generated music with others using information transmission technology.

Images