system

Abstract

We provide the system. [Solution] A speech recognition means that converts input audio data into digital waveform data, An analysis means for extracting a melody profile from converted digital waveform data, A natural language processing method that analyzes input text data and extracts emotions and themes, A music generation method that automatically generates music based on the analyzed results, A video generation method that generates video based on the generated music to create a music video, Moderation measures to check for inappropriate content and copyright infringement in the generated content, 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] There are problems that music production requires specialized knowledge and experience, and it is difficult for beginners and amateurs to enhance their own ideas to professional quality. Also, sharing the generated music and video content requires a great deal of time and effort to support multiple platforms. Furthermore, it is also an important issue to avoid the possibility that the completed content contains inappropriate content and the risk of infringing copyright.

Means for Solving the Problems

[0005] This invention includes a speech recognition means that converts user-inputted audio data into digital waveform data and extracts a melody profile therefrom. It also provides a natural language processing means that analyzes user-inputted text data to extract emotions and themes. Based on these analysis results, it includes a music generation means that automatically generates a song, and a video generation means that generates video based on the generated song to create a music video. Furthermore, it guarantees the quality and safety of the content by using a moderation means that checks the generated content for inappropriate content and the risk of copyright infringement. In addition, it reduces the burden on the user by automatically uploading the generated music video and song to an online platform. In this system, machine learning models are utilized in the analysis and generation processes to improve accuracy and efficiency.

[0006] "Speech recognition means" refers to a technology or device for analyzing input speech data and extracting musical features such as digital waveform data and melody profiles.

[0007] "Analysis means" refers to a technology or device for processing input data and extracting meaningful information or features from it.

[0008] "Natural language processing means" refers to technologies or devices that analyze input text data to understand and extract the structure, meaning, sentiment, theme, etc., of the text.

[0009] "Music generation means" refers to a technology or device for creating new music based on analyzed audio or text data.

[0010] "Video generation means" refers to technology or apparatus for creating visual content based on music or other digital data, and for generating music videos and the like.

[0011] "Moderation measures" refer to technologies or devices used to verify that generated content does not contain inappropriate material or infringe on copyright.

[0012] A "machine learning model" is an algorithm or system that learns patterns from large amounts of data and uses those patterns to analyze or predict new data. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0014] 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.

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

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the 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.

[0019] 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).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention provides a system that enables users to automatically create and widely share entirely new songs and music videos by providing simple humming or lyric ideas. This system is implemented using terminals, servers, and a network connecting them.

[0035] User input process

[0036] The user records humming using the device and inputs lyric ideas as text. The device collects this data and sends it to a server over the network.

[0037] Data Analysis Process

[0038] The server converts the transmitted audio data into digital waveform data using speech recognition. Analysis is then performed on this converted data to extract a melody profile. Text data is also analyzed using natural language processing to extract its emotions and themes. This organizes the fundamental information that determines the atmosphere and constituent elements of the music.

[0039] Music production and video production

[0040] The server integrates the analyzed audio and text data and automatically generates music through a music generation system. This process combines melody, rhythm, and harmony to transform the user's ideas into a concrete musical work. Based on the generated music, visual content is created using a video generation system, and a music video is compiled. At this stage, scene composition can be tailored to the rhythm and atmosphere of the music.

[0041] Content moderation and sharing

[0042] The generated songs and music videos undergo moderation to check for inappropriate content and copyright infringement. If no issues are found, the content is automatically uploaded to online platforms and immediately made available on various platforms specified by the user, such as social media and video sharing services.

[0043] Specific example

[0044] For example, if a user records themselves humming a tune with the theme "A joyful summer day" and inputs related lyrics, the server will generate an upbeat song based on that intention and create a music video combining it with images of a blue sky and a beach. In this way, users can obtain professional-quality works without any expertise in music or video production.

[0045] In this way, the invention lowers the barrier to music production and provides an environment where anyone can easily share music and video works with the world.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The user records humming using the device's microphone and inputs lyric ideas in text format. The device saves this data along with session information.

[0049] Step 2:

[0050] The device sends the stored audio and text data to the server. During this process, the data format may be standardized and compressed.

[0051] Step 3:

[0052] The server starts the speech recognition engine and converts the received audio data into digital waveform data. Next, the analysis module extracts the melody profile and analyzes musical characteristics such as tempo and pitch.

[0053] Step 4:

[0054] The server uses a natural language processing engine to analyze the received text data. From the analysis results, it extracts the emotional tone and themes of the lyrics and generates metadata to determine the direction of the song.

[0055] Step 5:

[0056] The server integrates the results of speech and text analysis and creates music using an AI-powered music generation module. This module designs the melody structure, harmony, and rhythm patterns to complete a professional-quality song.

[0057] Step 6:

[0058] The server activates a video generation engine based on the generated music and edits the music video. It constructs video scenes in accordance with the rhythm and theme of the music and adds visual effects.

[0059] Step 7:

[0060] The server runs a moderation engine on the generated songs and music videos to check for inappropriate content. It also checks for copyright infringement risks.

[0061] Step 8:

[0062] If there are no problems, the server will automatically upload the completed content to the specified online platform. At this time, metadata such as titles and tags will also be automatically generated as needed.

[0063] (Example 1)

[0064] 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."

[0065] Traditionally, music and video production required specialized knowledge and skills, which was a barrier for the average user. Furthermore, verifying copyright and content appropriateness when sharing music and videos online was a time-consuming process. Thus, there was a need for a system that would allow ordinary people to easily create high-quality music and video works and share them widely with confidence.

[0066] 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.

[0067] In this invention, the server includes speech recognition means for converting input audio information into digital waveform information, analysis means for extracting melodic characteristics from the converted digital waveform information, and natural language processing means for analyzing input text information and extracting emotions and themes. This makes it possible for ordinary users, even without specialized knowledge of music or video, to easily create professional-quality music and video works and share them securely on an online platform.

[0068] "Audio information" refers to raw audio data before it is converted into a digital format.

[0069] "Digital waveform information" refers to audio information that has been digitized and converted into a format that can be processed by a computer.

[0070] "Speech recognition means" refers to technology or equipment for converting speech information into digital waveform information.

[0071] "Melody characteristics" refer to information that describes the basic pitch, rhythm, and other musical elements of a musical work.

[0072] "Analysis means" refers to technology or equipment for analyzing digital waveform information or text information and extracting necessary features and information.

[0073] "Textual information" refers to text data entered by users, particularly information related to lyrics and themes.

[0074] "Natural language processing means" refers to technologies or devices that analyze textual information and extract emotions and themes.

[0075] A "musical work" refers to a piece of music that is automatically generated based on the characteristics of sound and melody.

[0076] "Video generation means" refers to a technology or device that automatically creates video based on a generated musical work.

[0077] A "music video work" refers to a work that combines a generated musical piece with corresponding video content.

[0078] "Content review means" refers to technology or equipment that checks generated information for inappropriate content or copyright infringement.

[0079] "Network infrastructure" refers to the internet and similar data communication infrastructure, which provides a platform for sharing content online.

[0080] This invention is a system that allows users to automatically generate and share music and video content. The user uses a terminal to first record a hum, and then provides related lyric ideas as text input. The terminal collects the input audio information and converts it into digital waveform information using speech recognition software. The terminal's application implements interfaces for digital audio and text manipulation.

[0081] The server uses a special speech analysis engine to analyze the received digital waveform information and extract melodic characteristics. It also uses a natural language processing module to analyze the input text information and identify emotions and themes. To perform these processes, the server is equipped with machine learning models and databases.

[0082] Based on the analyzed data, the server automatically generates musical works using a generative AI model. Depending on the characteristics of these musical works, video generation software is used to create video footage, which is then edited into a musical video. The video generation utilizes scene selection algorithms that correspond to the rhythm and atmosphere of the music.

[0083] Subsequently, the generated music videos and songs are evaluated by moderation software to ensure their appropriateness and the absence of copyright issues. If no problems are found at this stage, the server automatically uploads the music video works to various online platforms via the network infrastructure.

[0084] As a concrete example, a user might record themselves humming a tune with the theme "A joyful summer day" and input lyrics that fit that theme. This data is sent to a server, where it is analyzed and automatically generated. A bright and cheerful song is then created, and accompanying images of a blue sky or beach are incorporated into the music video.

[0085] An example of a prompt message is, "Analyze a melody and lyrics that evoke a feeling of joy on a summer day, and generate music and visuals that match them." This is a message that instructs the AI ​​on what kind of work it should produce.

[0086] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0087] Step 1:

[0088] The user records humming using the device. The device has a recording application installed that collects audio information input through the microphone. In this process, the user's humming is converted from analog format to digital audio format and saved.

[0089] Step 2:

[0090] The user provides lyric ideas as text input on their device. This text information is managed by the device's application and prepared as a data packet along with the audio information. This data packet is then ready to be transmitted over the network.

[0091] Step 3:

[0092] The terminal transmits prepared voice and text information to the server over the network. To maintain data integrity and confidentiality, the data is transmitted using encryption technology. The input here is an encrypted data packet, and the output is the data as it reaches the server.

[0093] Step 4:

[0094] The server processes the received audio information using speech recognition software and converts it into digital waveform information. This process extracts melodic characteristics such as rhythm and pitch from the input analog audio. The output is a digital melody profile.

[0095] Step 5:

[0096] The server uses a natural language processing module to analyze the input text information. Based on the prompt, it identifies the sentiment and subject of the text and generates related structured data. The input for this analysis is character information, and the output is the sentiment and subject information of the text.

[0097] Step 6:

[0098] The server uses a generative AI model to automatically generate musical works based on the analysis results of speech and text. The input is melodic characteristics and emotional / thematic information from the text, and the output is the completed musical work. At this stage, melody, rhythm, and harmony are integrated.

[0099] Step 7:

[0100] The server runs video generation software to produce videos based on musical works. Scenes that match the rhythm and atmosphere of the music are selected and edited. The input is the completed musical work, and the output is a music video.

[0101] Step 8:

[0102] The server reviews the generated content using moderation software, checking for inappropriate content and copyright infringement. The input is music video works, and the output is the verified content.

[0103] Step 9:

[0104] The server uploads music and video works to various online platforms. Here, the content is published to the social networking services and video sharing services specified by the user. The input consists of verified music and video works, and the output is widely publicized.

[0105] (Application Example 1)

[0106] 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."

[0107] In recent years, there has been a growing demand for individuals to easily create and widely share music and video content. However, this often requires specialized knowledge and skills, making it difficult for individual users to easily bring their ideas to life. There is a need to solve this problem and provide a system that allows anyone to easily enjoy creative activities.

[0108] 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.

[0109] In this invention, the server includes an audio processing means for converting input audio information into digital waveform information, an analysis means for extracting a melody profile from the converted digital waveform information, and a means for automatically generating music and video with a nostalgic atmosphere from user input audio and text. This makes it possible for users to easily create and share professional-quality music and video content based on their personal ideas, even without specialized knowledge.

[0110] "Audio processing means" refers to technology for converting input audio information into digital waveform information.

[0111] "Analysis means" refers to a method for extracting a melody profile from converted digital waveform information.

[0112] "Natural language processing methods" are techniques for analyzing input text information and extracting the emotions and themes contained within it.

[0113] "Music generation means" refers to technology that automatically generates music based on the results of analysis.

[0114] "Video generation means" refers to techniques for creating visual media based on generated music.

[0115] "Moderation measures" are mechanisms for checking generated information to ensure it does not contain inappropriate content or infringe on copyright.

[0116] An "online information dissemination platform" is an internet platform that can automatically transmit and share generated visual media and music.

[0117] A "machine learning model" is an algorithm used in analysis and generation processes to learn patterns from data and apply that knowledge to improve results.

[0118] A "nostalgic atmosphere" refers to a theme or emotion that evokes a feeling of recalling the past based on user input.

[0119] To implement this invention, a server, a terminal, and a network environment connecting them are required. The user records voice and inputs text information using the terminal. The voice information collected by the terminal is transmitted to the server via the network.

[0120] The server uses a speech recognition library (e.g., Google® Cloud Speech-to-Text) as an acoustic processing tool to convert speech information into digital waveform information. Then, dedicated software, acting as an analysis tool, extracts a melody profile from the digital waveform information.

[0121] Furthermore, a natural language processing library (e.g., spaCy) is used to analyze text information entered by the user, extracting emotions and themes from it. Based on the extracted melody profile and the information obtained from the text, music is automatically generated using tools such as Magenta as a music generation tool.

[0122] Based on the generated music, visual media is created using video generation software (e.g., FFmpeg). This results in a music video with a nostalgic atmosphere. The generated visual media and music are checked by moderation tools to ensure there is no inappropriate content or copyright infringement. After verification, the video and music are automatically sent to online information dissemination platforms (e.g., YouTube® or Instagram).

[0123] For example, if a user records themselves humming a tune with the theme "a nostalgic sunset" and inputs related lyrics, the server can generate a song that evokes a sense of nostalgia and create a music video combining it with footage of a sunset. An example of a prompt to the generation AI model would be, "Based on the user's humming and lyrics, generate a song with a nostalgic atmosphere and automatically create a video to match it."

[0124] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0125] Step 1:

[0126] The user records audio using a device and inputs text information. The input information consists of an audio file of the user humming and lyrics in text format. This audio file is prepared to be converted into waveform data for use in subsequent processes.

[0127] Step 2:

[0128] The terminal transmits recorded audio information to the server via the network. The input here is an audio file, and the output is the state after it has been transferred to the server. This process allows the audio data to be stored on a central server for more advanced analysis.

[0129] Step 3:

[0130] The server uses a speech recognition library as an acoustic processing tool to convert audio information into digital waveform information. The input is an audio file, and the output is digital waveform data. This conversion processes the audio data into a format that is easy to analyze.

[0131] Step 4:

[0132] The server uses analysis tools to extract melodic profiles from digital waveform data. The input is the converted digital waveform data, and the output is the melodic profile. This analysis extracts the characteristics of the sound, which then serves as a guide for subsequent music generation.

[0133] Step 5:

[0134] The server analyzes text information using natural language processing to extract emotions and themes. The input is text data provided by the user, and the output is data related to emotions and themes. Through this analysis, the mood and direction of the song are determined.

[0135] Step 6:

[0136] The server uses music generation technology to automatically generate music based on extracted melody profiles and themes and emotions derived from text. The input consists of melody profile, emotion, and theme data, while the output is a newly generated song. This generation process combines various musical elements to create unique compositions.

[0137] Step 7:

[0138] The server creates visual media based on music generated using video generation methods. The input is the generated music, and the output is a music video with a nostalgic atmosphere. This process integrates music and visual information to create compelling content.

[0139] Step 8:

[0140] The server uses moderation mechanisms to check the video and music for inappropriate content and copyright infringement. The input is the generated music video and music, and the output is the content after verification. This verification process ensures that the content being distributed is appropriate.

[0141] Step 9:

[0142] The server automatically transmits verified visual media and music to the online information dissemination platform. The input is verified music videos, and the output is publicly available on the online platform. This final step allows users to widely publish and share their work.

[0143] 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.

[0144] This invention provides a system that delivers more personalized content by recognizing the user's emotional state and dynamically generating music and video accordingly. This system consists of a network system including terminals, servers, and an emotion engine.

[0145] User input process

[0146] The user records humming using the device and inputs the lyrics. In addition, the device uses an emotion engine to infer the user's current emotions. This includes using the user's voice tone, facial recognition, or other physiological signals. The device sends this data to the system's server.

[0147] Data Analysis Process

[0148] The server converts the received audio data into digital waveform data using speech recognition and extracts the melody profile. It also analyzes the emotion and theme of the lyrics using natural language processing. Simultaneously, emotion data obtained from the emotion engine is also used, and these analysis results are integrated.

[0149] Content generation

[0150] The music generation system combines analyzed audio, lyrics, and emotional data to create a song. The emotional data, in particular, influences the melody, rhythm, and harmonic structure of the song. The video generation system designs the visuals to match the emotional tone of the generated song and produces a music video.

[0151] Content moderation and sharing

[0152] The generated music and videos are reviewed by moderation mechanisms to ensure they do not contain inappropriate content or infringe on copyrights. If there are no issues, the server automatically uploads the content to online platforms and makes it available on the various platforms selected by the user.

[0153] Specific example

[0154] For example, if a user records themselves humming a tune with the theme of "a hopeful dawn," and the user's emotion engine detects "joy," the server will automatically generate a positive and refreshing melody based on this information. The video generation system will then create a video combining bright dawn scenery and dynamic scenes. In this way, music and visuals that fit the user's emotions are provided, resulting in more emotionally resonant content. This invention allows users to easily share musical works that reflect their own emotions with the world.

[0155] The following describes the processing flow.

[0156] Step 1:

[0157] The user records humming using the device's microphone and inputs lyric ideas in text format. Simultaneously, the device collects emotional data to infer emotions from the user's face and voice.

[0158] Step 2:

[0159] The device packages recorded audio data, entered text data, and emotion data, and sends them to the server via the network.

[0160] Step 3:

[0161] The server uses speech recognition to convert the received audio data into digital waveform data and extracts a melody profile. This process allows the user's humming to be used as the basis for the music.

[0162] Step 4:

[0163] The server uses natural language processing to analyze text data and extract the themes and emotions of the lyrics. The analyzed information is then used as a factor in determining the structure of the song.

[0164] Step 5:

[0165] The server uses data from the emotion engine to evaluate the user's emotional state and integrates this with other analysis results. This integrated information is used to define the mood and emotional characteristics of the music.

[0166] Step 6:

[0167] The music generation method creates a song by considering the melody profile, the theme of the lyrics, and the user's emotions, and adjusts each element of the song to have a consistent emotional tone.

[0168] Step 7:

[0169] The video generation method creates a music video based on the emotions and themes of the generated song. The scene composition and color adjustments of the video reflect the user's emotional data.

[0170] Step 8:

[0171] The generated content is checked for inappropriate material and copyright infringement using moderation mechanisms. After this check, the server automatically uploads and publishes the content to the designated online platform.

[0172] (Example 2)

[0173] 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".

[0174] In a world with numerous entertainment systems, there is a growing demand for dynamic and personalized content that resonates with users' emotions. Traditional systems struggle to generate music and visuals that accurately reflect user emotions, and they lack the means to publish the created content without the risk of inappropriate content or copyright infringement. As a result, individual users' experiences are often limited.

[0175] 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.

[0176] In this invention, the server includes speech recognition means for converting input audio information into digital waveform data, analysis means for extracting melody data from the converted digital waveform data, and natural language processing means for analyzing input character data. This enables the generation of music and videos that reflect the user's emotions, as well as appropriate verification and publication of the content.

[0177] "Speech recognition means" refers to technology for converting speech information into digital waveform data.

[0178] "Analysis method" refers to a technique for extracting melody data from digital waveform data.

[0179] "Natural language processing means" refers to technologies that analyze text data and extract emotions and themes.

[0180] "Music generation means" refers to technology that automatically generates music based on analyzed data.

[0181] "Video generation means" refers to a technology that generates visual data based on a generated song to create a music video.

[0182] "Emotion measurement means" refers to technology that measures a user's emotional state and integrates that data within a system.

[0183] "Moderation methods" are technologies that inspect generated content to identify inappropriate material or copyright infringement.

[0184] This invention is a system that generates personalized music and video content based on the user's emotions. The system includes a terminal, a server, and an emotion engine.

[0185] The user records humming using the device. The device provides a lyric input function and also features an emotion engine to detect emotions from the user's voice tone and facial expressions. This engine analyzes the voice and facial expressions, and, if necessary, acquires physiological data from a heart rate sensor and other sources.

[0186] The terminal sends the acquired data to the server. The server uses speech recognition software to convert the speech information into digital waveform data. Here, common speech recognition technology is utilized. The server then performs analysis to extract a melody profile from the digital waveform data. Next, natural language processing technology is used to analyze the emotion and theme of the lyrics entered by the user. This determines how the information entered by the user should be reflected in the music.

[0187] In music generation, the server utilizes a learning model. This allows it to generate music based on the analyzed data. Emotional data is a factor that influences the rhythm, melody, and harmonic structure of the music.

[0188] In video generation, technology for generating visual data is used to automatically create music videos that match the music. The video generation method designs the video using visual materials that match the emotional tone of the generated music.

[0189] A concrete example of this invention is when a user records humming a tune with the theme of "a hopeful dawn," and the emotion engine detects "joy." In this case, the server automatically generates a positive and refreshing melody, and the video generation means provides a music video combining a bright dawn scene with a dynamic scene.

[0190] As an example of a prompt, the system can be instructed as follows: "If the user's emotion is 'joy,' generate a bright and refreshing song and a video that matches it." This makes it possible to efficiently generate content that resonates with the user's emotions.

[0191] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0192] Step 1:

[0193] The user records humming using the device and inputs the lyrics. The device captures the audio using its microphone and retrieves the lyrics as text through the input interface. The device also analyzes facial expressions and voice tone using its camera and sensors, and uses an emotion engine to infer the user's current emotion. The input data includes audio data, text data, and emotion data.

[0194] Step 2:

[0195] The terminal sends the acquired voice data, text data, and sentiment data to the server. Encryption technology is used to ensure the security of this communication. The server receives this data and prepares it for use in the next processing step. The output is all the input data passed to the server.

[0196] Step 3:

[0197] The server converts the received audio data into digital waveform data using speech recognition. Specifically, a speech recognition algorithm analyzes the audio data and converts it into text data. Next, it analyzes the waveform data to extract the melody profile. The input is audio data, and the output is the melody profile.

[0198] Step 4:

[0199] The server analyzes text data using natural language processing techniques to extract emotions and themes. In doing so, it processes the text data using sentiment analysis algorithms to understand the user's intent and themes. The input is the text data of the lyrics, and the output is the emotional data and themes of the text.

[0200] Step 5:

[0201] The server integrates analyzed melody profiles, themes from text, and emotion data, and automatically generates music using music generation methods. A generation AI model is used, and this data is reflected in the melody and rhythm of the music. The input is the melody profile and integrated emotion / theme data, and the output is the generated music.

[0202] Step 6:

[0203] The server uses a video generation system to create a visual music video based on the generated music. This is achieved by a video generation AI model that constructs visual materials according to the emotional tone of the music. The input is the generated music, and the output is a music video.

[0204] Step 7:

[0205] The generated music and video are verified by moderation mechanisms on the server. Content moderation technology is used to ensure there is no inappropriate content or copyright issues. Once this is confirmed, the generated material proceeds to the next process. The output is verified content.

[0206] (Application Example 2)

[0207] 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".

[0208] Personalizing in-home entertainment experiences and providing music and video content tailored to users' emotions in real time has been difficult with conventional technologies. In particular, there is a need for an efficient system that can effectively recognize emotional states and dynamically generate content accordingly.

[0209] 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.

[0210] In this invention, the server includes a speech recognition means for converting input audio data into digital waveform data, an analysis means for extracting a melody profile from the converted digital waveform data, and a means for integrating music and video generated in response to the user's real-time emotions into the home system. This enables the real-time provision of personalized music and video that matches the user's emotions within the home.

[0211] "Speech recognition means" refers to a technology that processes input speech data into digital waveform data.

[0212] The "analysis method" is a technique for extracting a melody profile from the converted digital waveform data.

[0213] "Natural language processing means" refers to technologies that analyze input text data and extract emotions and themes.

[0214] "Music generation means" refers to technology that automatically generates music based on the results of analysis.

[0215] "Video generation means" refers to a technology that generates video based on a generated song to create a music video.

[0216] "Methods for integration into home systems" refers to technologies that integrate generated music and video into a system that provides them within the home in response to the user's real-time emotions.

[0217] A "machine learning model" is an algorithm that learns from empirical data during the analysis and generation process to perform predictions, classifications, and generation.

[0218] This invention describes a home entertainment system that generates and delivers music and video that reflects the user's emotions in real time. The main processes consist of input from the user's terminal, analysis and generation on a server, and delivery on the home system.

[0219] The user uses a terminal to input humming or text data and sends it to the server. The terminal is equipped with a microphone for speech recognition and a camera for real-time emotion detection. The voice data is converted into digital waveform data by a speech recognition system. The data is analyzed to extract a melody profile, and the emotion and theme are analyzed by a natural language processing system.

[0220] The server automatically generates music using music generation equipment based on the analyzed data, and then creates a music video using video generation equipment. This process utilizes machine learning models based on empirical data to refine the generation process.

[0221] The home system provides music and visuals generated in response to the user's real-time emotions. For example, if a user feels refreshed and hums a cheerful tune, the system will generate music with a refreshing melody and visuals of a sunny park as the background, and immediately display them on the screen.

[0222] Here is an example of a prompt message for this invention: "Take the humming the user sings in a refreshed mood as input, and generate content that combines an upbeat melody with a visual of a park on a sunny day."

[0223] This system allows users to easily enjoy personalized entertainment tailored to their emotional state.

[0224] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0225] Step 1:

[0226] The user records humming using the device and inputs the lyrics as text data. The input audio data is captured via the device's microphone, and the text data is provided by the user through the device's interface. The device sends this data to the server for the next processing step.

[0227] Step 2:

[0228] The server converts the received audio data into digital waveform data using speech recognition. This conversion process extracts audio patterns from the recorded humming and converts them into an analyzable digital format. The output data is digital waveform data ready for extracting a melody profile.

[0229] Step 3:

[0230] The server extracts a melody profile from the digital waveform data converted using an analysis tool. Here, the melody phrases and rhythms are identified. The extracted melody profile becomes the input for the next music generation process.

[0231] Step 4:

[0232] The server uses natural language processing to analyze the received text data and extract emotions and themes. This process identifies emotional expressions and keywords contained within the text and extracts elements that influence the overall theme of the song. The extracted emotional data and theme information are then supplied to the music generation process.

[0233] Step 5:

[0234] The server uses music generation tools to automatically generate music by combining analyzed melody profiles and emotion data. During the generation process, the server adjusts musical attributes such as melody and rhythm according to the emotion elements. The generated music is then used in the next video generation step.

[0235] Step 6:

[0236] The server uses video generation tools to create visuals that match the emotional tone and theme of the generated song, completing the music video. In this process, the server selects relevant video footage and seamlessly integrates it with the song for a visually appealing presentation. The output is a custom video for the user's entertainment experience.

[0237] Step 7:

[0238] Based on emotion prediction data transmitted from the device, the home system proposes generated music and video that matches the user's real-time emotions. The home system then instantly delivers the content via a display and sound system, allowing the user to enjoy it.

[0239] 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.

[0240] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0241] 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.

[0242] [Second Embodiment]

[0243] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0244] 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.

[0245] 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).

[0246] 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.

[0247] 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.

[0248] 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).

[0249] 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.

[0250] 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.

[0251] 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.

[0252] 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.

[0253] 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.

[0254] 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".

[0255] This invention provides a system that enables users to automatically create and widely share entirely new songs and music videos by providing simple humming or lyric ideas. This system is implemented using terminals, servers, and a network connecting them.

[0256] User input process

[0257] The user records humming using the device and inputs lyric ideas as text. The device collects this data and sends it to a server over the network.

[0258] Data Analysis Process

[0259] The server converts the transmitted audio data into digital waveform data using speech recognition. Analysis is then performed on this converted data to extract a melody profile. Text data is also analyzed using natural language processing to extract its emotions and themes. This organizes the fundamental information that determines the atmosphere and constituent elements of the music.

[0260] Music production and video production

[0261] The server integrates the analyzed audio and text data and automatically generates music through a music generation system. This process combines melody, rhythm, and harmony to transform the user's ideas into a concrete musical work. Based on the generated music, visual content is created using a video generation system, and a music video is compiled. At this stage, scene composition can be tailored to the rhythm and atmosphere of the music.

[0262] Content moderation and sharing

[0263] The generated songs and music videos undergo moderation to check for inappropriate content and copyright infringement. If no issues are found, the content is automatically uploaded to online platforms and immediately made available on various platforms specified by the user, such as social media and video sharing services.

[0264] Specific example

[0265] For example, if a user records themselves humming a tune with the theme "A joyful summer day" and inputs related lyrics, the server will generate an upbeat song based on that intention and create a music video combining it with images of a blue sky and a beach. In this way, users can obtain professional-quality works without any expertise in music or video production.

[0266] In this way, the invention lowers the barrier to music production and provides an environment where anyone can easily share music and video works with the world.

[0267] The following describes the processing flow.

[0268] Step 1:

[0269] The user records humming using the device's microphone and inputs lyric ideas in text format. The device saves this data along with session information.

[0270] Step 2:

[0271] The device sends the stored audio and text data to the server. During this process, the data format may be standardized and compressed.

[0272] Step 3:

[0273] The server starts the speech recognition engine and converts the received audio data into digital waveform data. Next, the analysis module extracts the melody profile and analyzes musical characteristics such as tempo and pitch.

[0274] Step 4:

[0275] The server uses a natural language processing engine to analyze the received text data. From the analysis results, it extracts the emotional tone and themes of the lyrics and generates metadata to determine the direction of the song.

[0276] Step 5:

[0277] The server integrates the results of speech and text analysis and creates music using an AI-powered music generation module. This module designs the melody structure, harmony, and rhythm patterns to complete a professional-quality song.

[0278] Step 6:

[0279] The server starts a video generation engine based on the generated music and edits the music video. It constructs video scenes according to the rhythm and theme of the music and adds visual effects.

[0280] Step 7:

[0281] The server operates a moderation engine on the generated music and music video to check whether there is any inappropriate content. At the same time, the risk of copyright infringement is also checked.

[0282] Step 8:

[0283] If there is no problem, the server automatically uploads the completed content to the specified online platform. At this time, metadata such as titles and tags are also automatically generated as needed.

[0284] (Example 1)

[0285] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0286] Conventionally, the production of music and videos requires specialized knowledge and technology, which has been a barrier for ordinary users. Furthermore, it has been time-consuming to check the copyright and content appropriateness when sharing music and videos online. Thus, there has been a demand for a system that allows ordinary people to easily create high-quality music and video works and safely share them widely.

[0287] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0288] In this invention, the server includes speech recognition means for converting input audio information into digital waveform information, analysis means for extracting melodic characteristics from the converted digital waveform information, and natural language processing means for analyzing input text information and extracting emotions and themes. This makes it possible for ordinary users, even without specialized knowledge of music or video, to easily create professional-quality music and video works and share them securely on an online platform.

[0289] "Audio information" refers to raw audio data before it is converted into a digital format.

[0290] "Digital waveform information" refers to audio information that has been digitized and converted into a format that can be processed by a computer.

[0291] "Speech recognition means" refers to technology or equipment for converting speech information into digital waveform information.

[0292] "Melody characteristics" refer to information that describes the basic pitch, rhythm, and other musical elements of a musical work.

[0293] "Analysis means" refers to technology or equipment for analyzing digital waveform information or text information and extracting necessary features and information.

[0294] "Textual information" refers to text data entered by users, particularly information related to lyrics and themes.

[0295] "Natural language processing means" refers to technologies or devices that analyze textual information and extract emotions and themes.

[0296] A "musical work" refers to a piece of music that is automatically generated based on the characteristics of sound and melody.

[0297] "Video generation means" refers to a technology or device that automatically creates video based on a generated musical work.

[0298] A "music video work" refers to a work that combines a generated musical piece with corresponding video content.

[0299] "Content review means" refers to technology or equipment that checks generated information for inappropriate content or copyright infringement.

[0300] "Network infrastructure" refers to the internet and similar data communication infrastructure, which provides a platform for sharing content online.

[0301] This invention is a system that allows users to automatically generate and share music and video content. The user uses a terminal to first record a hum, and then provides related lyric ideas as text input. The terminal collects the input audio information and converts it into digital waveform information using speech recognition software. The terminal's application implements interfaces for digital audio and text manipulation.

[0302] The server uses a special speech analysis engine to analyze the received digital waveform information and extract melodic characteristics. It also uses a natural language processing module to analyze the input text information and identify emotions and themes. To perform these processes, the server is equipped with machine learning models and databases.

[0303] Based on the analyzed data, the server automatically generates musical works using a generative AI model. Depending on the characteristics of these musical works, video generation software is used to create video footage, which is then edited into a musical video. The video generation utilizes scene selection algorithms that correspond to the rhythm and atmosphere of the music.

[0304] Subsequently, the generated music videos and songs are evaluated by moderation software to ensure their appropriateness and the absence of copyright issues. If no problems are found at this stage, the server automatically uploads the music video works to various online platforms via the network infrastructure.

[0305] As a specific example, there is a case where a user records a humming with the theme of "a summer day full of joy" and inputs a lyric phrase along with that theme. When this data is sent to the server and analysis and automatic generation are performed, a bright and lively piece of music will be generated, and images of a blue sky and a seaside will be incorporated into the music video work accordingly.

[0306] An example of a prompt sentence is "Analyze the melody and lyrics that convey joy on a summer day and generate a music and video work that matches them." This is the statement that instructs the AI on what kind of work to generate.

[0307] The flow of the specific process in Example 1 will be described using FIG. 11.

[0308] Step 1:

[0309] The user uses the terminal to record a humming. An application for recording is installed on the terminal, which collects the voice information input through the microphone. In this process, the user's humming is converted from an analog format to a digital audio format and saved.

[0310] Step 2:

[0311] The user provides an idea for the lyrics as text input on the terminal. This text information is managed by the application on the terminal and is prepared as a data packet together with the voice information. This data packet is ready to be sent via the network.

[0312] Step 3:

[0313] The terminal sends the prepared voice information and text information to the server through the network. To maintain the integrity and confidentiality of the data, the data is transferred using encryption technology. The input here is an encrypted data packet, and the output is the data in the state that reaches the server.

[0314] Step 4:

[0315] The server processes the received audio information using speech recognition software and converts it into digital waveform information. This process extracts melodic characteristics such as rhythm and pitch from the input analog audio. The output is a digital melody profile.

[0316] Step 5:

[0317] The server uses a natural language processing module to analyze the input text information. Based on the prompt, it identifies the sentiment and subject of the text and generates related structured data. The input for this analysis is character information, and the output is the sentiment and subject information of the text.

[0318] Step 6:

[0319] The server uses a generative AI model to automatically generate musical works based on the analysis results of speech and text. The input is melodic characteristics and emotional / thematic information from the text, and the output is the completed musical work. At this stage, melody, rhythm, and harmony are integrated.

[0320] Step 7:

[0321] The server runs video generation software to produce videos based on musical works. Scenes that match the rhythm and atmosphere of the music are selected and edited. The input is the completed musical work, and the output is a music video.

[0322] Step 8:

[0323] The server reviews the generated content using moderation software, checking for inappropriate content and copyright infringement. The input is music video works, and the output is the verified content.

[0324] Step 9:

[0325] The server uploads music and video works to various online platforms. Here, the content is published to the social networking services and video sharing services specified by the user. The input consists of verified music and video works, and the output is widely publicized.

[0326] (Application Example 1)

[0327] 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."

[0328] In recent years, there has been a growing demand for individuals to easily create and widely share music and video content. However, this often requires specialized knowledge and skills, making it difficult for individual users to easily bring their ideas to life. There is a need to solve this problem and provide a system that allows anyone to easily enjoy creative activities.

[0329] 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.

[0330] In this invention, the server includes an audio processing means for converting input audio information into digital waveform information, an analysis means for extracting a melody profile from the converted digital waveform information, and a means for automatically generating music and video with a nostalgic atmosphere from user input audio and text. This makes it possible for users to easily create and share professional-quality music and video content based on their personal ideas, even without specialized knowledge.

[0331] "Audio processing means" refers to technology for converting input audio information into digital waveform information.

[0332] "Analysis means" refers to a method for extracting a melody profile from converted digital waveform information.

[0333] "Natural language processing methods" are techniques for analyzing input text information and extracting the emotions and themes contained within it.

[0334] "Music generation means" refers to technology that automatically generates music based on the results of analysis.

[0335] "Video generation means" refers to techniques for creating visual media based on generated music.

[0336] "Moderation measures" are mechanisms for checking generated information to ensure it does not contain inappropriate content or infringe on copyright.

[0337] An "online information dissemination platform" is an internet platform that can automatically transmit and share generated visual media and music.

[0338] A "machine learning model" is an algorithm used in analysis and generation processes to learn patterns from data and apply that knowledge to improve results.

[0339] A "nostalgic atmosphere" refers to a theme or emotion that evokes a feeling of recalling the past based on user input.

[0340] To implement this invention, a server, a terminal, and a network environment connecting them are required. The user records voice and inputs text information using the terminal. The voice information collected by the terminal is transmitted to the server via the network.

[0341] The server uses a speech recognition library (e.g., Google Cloud Speech-to-Text) as an acoustic processing tool to convert speech information into digital waveform information. Then, dedicated software, acting as an analysis tool, extracts a melody profile from the digital waveform information.

[0342] Furthermore, a natural language processing library (e.g., spaCy) is used to analyze text information entered by the user, extracting emotions and themes from it. Based on the extracted melody profile and the information obtained from the text, music is automatically generated using tools such as Magenta as a music generation tool.

[0343] Based on the generated music, visual media is created using video generation software (e.g., FFmpeg). This results in a music video with a nostalgic atmosphere. The generated visual media and music are checked by moderation tools to ensure there is no inappropriate content or copyright infringement. After verification, the video and music are automatically sent to online information dissemination platforms (e.g., YouTube and Instagram).

[0344] For example, if a user records themselves humming a tune with the theme "a nostalgic sunset" and inputs related lyrics, the server can generate a song that evokes a sense of nostalgia and create a music video combining it with footage of a sunset. An example of a prompt to the generation AI model would be, "Based on the user's humming and lyrics, generate a song with a nostalgic atmosphere and automatically create a video to match it."

[0345] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0346] Step 1:

[0347] The user records audio using a device and inputs text information. The input information consists of an audio file of the user humming and lyrics in text format. This audio file is prepared to be converted into waveform data for use in subsequent processes.

[0348] Step 2:

[0349] The terminal transmits recorded audio information to the server via the network. The input here is an audio file, and the output is the state after it has been transferred to the server. This process allows the audio data to be stored on a central server for more advanced analysis.

[0350] Step 3:

[0351] The server uses a speech recognition library as an acoustic processing tool to convert audio information into digital waveform information. The input is an audio file, and the output is digital waveform data. This conversion processes the audio data into a format that is easy to analyze.

[0352] Step 4:

[0353] The server uses analysis tools to extract melodic profiles from digital waveform data. The input is the converted digital waveform data, and the output is the melodic profile. This analysis extracts the characteristics of the sound, which then serves as a guide for subsequent music generation.

[0354] Step 5:

[0355] The server analyzes text information using natural language processing to extract emotions and themes. The input is text data provided by the user, and the output is data related to emotions and themes. Through this analysis, the mood and direction of the song are determined.

[0356] Step 6:

[0357] The server uses music generation technology to automatically generate music based on extracted melody profiles and themes and emotions derived from text. The input consists of melody profile, emotion, and theme data, while the output is a newly generated song. This generation process combines various musical elements to create unique compositions.

[0358] Step 7:

[0359] The server creates visual media based on music generated using video generation methods. The input is the generated music, and the output is a music video with a nostalgic atmosphere. This process integrates music and visual information to create compelling content.

[0360] Step 8:

[0361] The server uses moderation mechanisms to check the video and music for inappropriate content and copyright infringement. The input is the generated music video and music, and the output is the content after verification. This verification process ensures that the content being distributed is appropriate.

[0362] Step 9:

[0363] The server automatically transmits verified visual media and music to the online information dissemination platform. The input is verified music videos, and the output is publicly available on the online platform. This final step allows users to widely publish and share their work.

[0364] 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.

[0365] This invention provides a system that delivers more personalized content by recognizing the user's emotional state and dynamically generating music and video accordingly. This system consists of a network system including terminals, servers, and an emotion engine.

[0366] User input process

[0367] The user records humming using the device and inputs the lyrics. In addition, the device uses an emotion engine to infer the user's current emotions. This includes using the user's voice tone, facial recognition, or other physiological signals. The device sends this data to the system's server.

[0368] Data Analysis Process

[0369] The server converts the received audio data into digital waveform data using speech recognition and extracts the melody profile. It also analyzes the emotion and theme of the lyrics using natural language processing. Simultaneously, emotion data obtained from the emotion engine is also used, and these analysis results are integrated.

[0370] Content generation

[0371] The music generation system combines analyzed audio, lyrics, and emotional data to create a song. The emotional data, in particular, influences the melody, rhythm, and harmonic structure of the song. The video generation system designs the visuals to match the emotional tone of the generated song and produces a music video.

[0372] Content moderation and sharing

[0373] The generated music and videos are reviewed by moderation mechanisms to ensure they do not contain inappropriate content or infringe on copyrights. If there are no issues, the server automatically uploads the content to online platforms and makes it available on the various platforms selected by the user.

[0374] Specific example

[0375] For example, if a user records themselves humming a tune with the theme of "a hopeful dawn," and the user's emotion engine detects "joy," the server will automatically generate a positive and refreshing melody based on this information. The video generation system will then create a video combining bright dawn scenery and dynamic scenes. In this way, music and visuals that fit the user's emotions are provided, resulting in more emotionally resonant content. This invention allows users to easily share musical works that reflect their own emotions with the world.

[0376] The following describes the processing flow.

[0377] Step 1:

[0378] The user records humming using the device's microphone and inputs lyric ideas in text format. Simultaneously, the device collects emotional data to infer emotions from the user's face and voice.

[0379] Step 2:

[0380] The device packages recorded audio data, entered text data, and emotion data, and sends them to the server via the network.

[0381] Step 3:

[0382] The server uses speech recognition to convert the received audio data into digital waveform data and extracts a melody profile. This process allows the user's humming to be used as the basis for the music.

[0383] Step 4:

[0384] The server uses natural language processing to analyze text data and extract the themes and emotions of the lyrics. The analyzed information is then used as a factor in determining the structure of the song.

[0385] Step 5:

[0386] The server uses data from the emotion engine to evaluate the user's emotional state and integrates this with other analysis results. This integrated information is used to define the mood and emotional characteristics of the music.

[0387] Step 6:

[0388] The music generation method creates a song by considering the melody profile, the theme of the lyrics, and the user's emotions, and adjusts each element of the song to have a consistent emotional tone.

[0389] Step 7:

[0390] The video generation method creates a music video based on the emotions and themes of the generated song. The scene composition and color adjustments of the video reflect the user's emotional data.

[0391] Step 8:

[0392] The generated content is checked for inappropriate material and copyright infringement using moderation mechanisms. After this check, the server automatically uploads and publishes the content to the designated online platform.

[0393] (Example 2)

[0394] 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".

[0395] In a world with numerous entertainment systems, there is a growing demand for dynamic and personalized content that resonates with users' emotions. Traditional systems struggle to generate music and visuals that accurately reflect user emotions, and they lack the means to publish the created content without the risk of inappropriate content or copyright infringement. As a result, individual users' experiences are often limited.

[0396] 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.

[0397] In this invention, the server includes speech recognition means for converting input audio information into digital waveform data, analysis means for extracting melody data from the converted digital waveform data, and natural language processing means for analyzing input character data. This enables the generation of music and videos that reflect the user's emotions, as well as appropriate verification and publication of the content.

[0398] "Speech recognition means" refers to technology for converting speech information into digital waveform data.

[0399] "Analysis method" refers to a technique for extracting melody data from digital waveform data.

[0400] "Natural language processing means" refers to technologies that analyze text data and extract emotions and themes.

[0401] "Music generation means" refers to technology that automatically generates music based on analyzed data.

[0402] "Video generation means" refers to a technology that generates visual data based on a generated song to create a music video.

[0403] "Emotion measurement means" refers to technology that measures a user's emotional state and integrates that data within a system.

[0404] "Moderation methods" are technologies that inspect generated content to identify inappropriate material or copyright infringement.

[0405] This invention is a system that generates personalized music and video content based on the user's emotions. The system includes a terminal, a server, and an emotion engine.

[0406] The user records humming using the device. The device provides a lyric input function and also features an emotion engine to detect emotions from the user's voice tone and facial expressions. This engine analyzes the voice and facial expressions, and, if necessary, acquires physiological data from a heart rate sensor and other sources.

[0407] The terminal sends the acquired data to the server. The server uses speech recognition software to convert the speech information into digital waveform data. Here, common speech recognition technology is utilized. The server then performs analysis to extract a melody profile from the digital waveform data. Next, natural language processing technology is used to analyze the emotion and theme of the lyrics entered by the user. This determines how the information entered by the user should be reflected in the music.

[0408] In music generation, the server utilizes a learning model. This allows it to generate music based on the analyzed data. Emotional data is a factor that influences the rhythm, melody, and harmonic structure of the music.

[0409] In video generation, technology for generating visual data is used to automatically create music videos that match the music. The video generation method designs the video using visual materials that match the emotional tone of the generated music.

[0410] A concrete example of this invention is when a user records humming a tune with the theme of "a hopeful dawn," and the emotion engine detects "joy." In this case, the server automatically generates a positive and refreshing melody, and the video generation means provides a music video combining a bright dawn scene with a dynamic scene.

[0411] As an example of a prompt, the system can be instructed as follows: "If the user's emotion is 'joy,' generate a bright and refreshing song and a video that matches it." This makes it possible to efficiently generate content that resonates with the user's emotions.

[0412] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0413] Step 1:

[0414] The user records humming using the device and inputs the lyrics. The device captures the audio using its microphone and retrieves the lyrics as text through the input interface. The device also analyzes facial expressions and voice tone using its camera and sensors, and uses an emotion engine to infer the user's current emotion. The input data includes audio data, text data, and emotion data.

[0415] Step 2:

[0416] The terminal sends the acquired voice data, text data, and sentiment data to the server. Encryption technology is used to ensure the security of this communication. The server receives this data and prepares it for use in the next processing step. The output is all the input data passed to the server.

[0417] Step 3:

[0418] The server converts the received audio data into digital waveform data using speech recognition. Specifically, a speech recognition algorithm analyzes the audio data and converts it into text data. Next, it analyzes the waveform data to extract the melody profile. The input is audio data, and the output is the melody profile.

[0419] Step 4:

[0420] The server analyzes text data using natural language processing techniques to extract emotions and themes. In doing so, it processes the text data using sentiment analysis algorithms to understand the user's intent and themes. The input is the text data of the lyrics, and the output is the emotional data and themes of the text.

[0421] Step 5:

[0422] The server integrates analyzed melody profiles, themes from text, and emotion data, and automatically generates music using music generation methods. A generation AI model is used, and this data is reflected in the melody and rhythm of the music. The input is the melody profile and integrated emotion / theme data, and the output is the generated music.

[0423] Step 6:

[0424] The server uses a video generation system to create a visual music video based on the generated music. This is achieved by a video generation AI model that constructs visual materials according to the emotional tone of the music. The input is the generated music, and the output is a music video.

[0425] Step 7:

[0426] The generated music and video are verified by moderation mechanisms on the server. Content moderation technology is used to ensure there is no inappropriate content or copyright issues. Once this is confirmed, the generated material proceeds to the next process. The output is verified content.

[0427] (Application Example 2)

[0428] 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."

[0429] Personalizing in-home entertainment experiences and providing music and video content tailored to users' emotions in real time has been difficult with conventional technologies. In particular, there is a need for an efficient system that can effectively recognize emotional states and dynamically generate content accordingly.

[0430] 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.

[0431] In this invention, the server includes a speech recognition means for converting input audio data into digital waveform data, an analysis means for extracting a melody profile from the converted digital waveform data, and a means for integrating music and video generated in response to the user's real-time emotions into the home system. This enables the real-time provision of personalized music and video that matches the user's emotions within the home.

[0432] "Speech recognition means" refers to a technology that processes input speech data into digital waveform data.

[0433] The "analysis method" is a technique for extracting a melody profile from the converted digital waveform data.

[0434] "Natural language processing means" refers to technologies that analyze input text data and extract emotions and themes.

[0435] "Music generation means" refers to technology that automatically generates music based on the results of analysis.

[0436] "Video generation means" refers to a technology that generates video based on a generated song to create a music video.

[0437] "Methods for integration into home systems" refers to technologies that integrate generated music and video into a system that provides them within the home in response to the user's real-time emotions.

[0438] A "machine learning model" is an algorithm that learns from empirical data during the analysis and generation process to perform predictions, classifications, and generation.

[0439] This invention describes a home entertainment system that generates and delivers music and video that reflects the user's emotions in real time. The main processes consist of input from the user's terminal, analysis and generation on a server, and delivery on the home system.

[0440] The user uses a terminal to input humming or text data and sends it to the server. The terminal is equipped with a microphone for speech recognition and a camera for real-time emotion detection. The voice data is converted into digital waveform data by a speech recognition system. The data is analyzed to extract a melody profile, and the emotion and theme are analyzed by a natural language processing system.

[0441] The server automatically generates music using music generation equipment based on the analyzed data, and then creates a music video using video generation equipment. This process utilizes machine learning models based on empirical data to refine the generation process.

[0442] The home system provides music and visuals generated in response to the user's real-time emotions. For example, if a user feels refreshed and hums a cheerful tune, the system will generate music with a refreshing melody and visuals of a sunny park as the background, and immediately display them on the screen.

[0443] Here is an example of a prompt message for this invention: "Take the humming the user sings in a refreshed mood as input, and generate content that combines an upbeat melody with a visual of a park on a sunny day."

[0444] This system allows users to easily enjoy personalized entertainment tailored to their emotional state.

[0445] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0446] Step 1:

[0447] The user records humming using the device and inputs the lyrics as text data. The input audio data is captured via the device's microphone, and the text data is provided by the user through the device's interface. The device sends this data to the server for the next processing step.

[0448] Step 2:

[0449] The server converts the received audio data into digital waveform data using speech recognition. This conversion process extracts audio patterns from the recorded humming and converts them into an analyzable digital format. The output data is digital waveform data ready for extracting a melody profile.

[0450] Step 3:

[0451] The server extracts a melody profile from the digital waveform data converted using an analysis tool. Here, the melody phrases and rhythms are identified. The extracted melody profile becomes the input for the next music generation process.

[0452] Step 4:

[0453] The server uses natural language processing to analyze the received text data and extract emotions and themes. This process identifies emotional expressions and keywords contained within the text and extracts elements that influence the overall theme of the song. The extracted emotional data and theme information are then supplied to the music generation process.

[0454] Step 5:

[0455] The server uses music generation tools to automatically generate music by combining analyzed melody profiles and emotion data. During the generation process, the server adjusts musical attributes such as melody and rhythm according to the emotion elements. The generated music is then used in the next video generation step.

[0456] Step 6:

[0457] The server uses video generation tools to create visuals that match the emotional tone and theme of the generated song, completing the music video. In this process, the server selects relevant video footage and seamlessly integrates it with the song for a visually appealing presentation. The output is a custom video for the user's entertainment experience.

[0458] Step 7:

[0459] Based on emotion prediction data transmitted from the device, the home system proposes generated music and video that matches the user's real-time emotions. The home system then instantly delivers the content via a display and sound system, allowing the user to enjoy it.

[0460] 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.

[0461] 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.

[0462] 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.

[0463] [Third Embodiment]

[0464] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0465] 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.

[0466] 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).

[0467] 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.

[0468] 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.

[0469] 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).

[0470] 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.

[0471] 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.

[0472] 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.

[0473] 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.

[0474] 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.

[0475] 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".

[0476] This invention provides a system that enables users to automatically create and widely share entirely new songs and music videos by providing simple humming or lyric ideas. This system is implemented using terminals, servers, and a network connecting them.

[0477] User input process

[0478] The user records humming using the device and inputs lyric ideas as text. The device collects this data and sends it to a server over the network.

[0479] Data Analysis Process

[0480] The server converts the transmitted audio data into digital waveform data using speech recognition. Analysis is then performed on this converted data to extract a melody profile. Text data is also analyzed using natural language processing to extract its emotions and themes. This organizes the fundamental information that determines the atmosphere and constituent elements of the music.

[0481] Music production and video production

[0482] The server integrates the analyzed audio and text data and automatically generates music through a music generation system. This process combines melody, rhythm, and harmony to transform the user's ideas into a concrete musical work. Based on the generated music, visual content is created using a video generation system, and a music video is compiled. At this stage, scene composition can be tailored to the rhythm and atmosphere of the music.

[0483] Content moderation and sharing

[0484] The generated songs and music videos undergo moderation to check for inappropriate content and copyright infringement. If no issues are found, the content is automatically uploaded to online platforms and immediately made available on various platforms specified by the user, such as social media and video sharing services.

[0485] Specific example

[0486] For example, if a user records themselves humming a tune with the theme "A joyful summer day" and inputs related lyrics, the server will generate an upbeat song based on that intention and create a music video combining it with images of a blue sky and a beach. In this way, users can obtain professional-quality works without any expertise in music or video production.

[0487] In this way, the invention lowers the barrier to music production and provides an environment where anyone can easily share music and video works with the world.

[0488] The following describes the processing flow.

[0489] Step 1:

[0490] The user records humming using the device's microphone and inputs lyric ideas in text format. The device saves this data along with session information.

[0491] Step 2:

[0492] The device sends the stored audio and text data to the server. During this process, the data format may be standardized and compressed.

[0493] Step 3:

[0494] The server starts the speech recognition engine and converts the received audio data into digital waveform data. Next, the analysis module extracts the melody profile and analyzes musical characteristics such as tempo and pitch.

[0495] Step 4:

[0496] The server uses a natural language processing engine to analyze the received text data. From the analysis results, it extracts the emotional tone and themes of the lyrics and generates metadata to determine the direction of the song.

[0497] Step 5:

[0498] The server integrates the results of speech and text analysis and creates music using an AI-powered music generation module. This module designs the melody structure, harmony, and rhythm patterns to complete a professional-quality song.

[0499] Step 6:

[0500] The server activates a video generation engine based on the generated music and edits the music video. It constructs video scenes in accordance with the rhythm and theme of the music and adds visual effects.

[0501] Step 7:

[0502] The server runs a moderation engine on the generated songs and music videos to check for inappropriate content. It also checks for copyright infringement risks.

[0503] Step 8:

[0504] If there are no problems, the server will automatically upload the completed content to the specified online platform. At this time, metadata such as titles and tags will also be automatically generated as needed.

[0505] (Example 1)

[0506] 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."

[0507] Traditionally, music and video production required specialized knowledge and skills, which was a barrier for the average user. Furthermore, verifying copyright and content appropriateness when sharing music and videos online was a time-consuming process. Thus, there was a need for a system that would allow ordinary people to easily create high-quality music and video works and share them widely with confidence.

[0508] 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.

[0509] In this invention, the server includes speech recognition means for converting input audio information into digital waveform information, analysis means for extracting melodic characteristics from the converted digital waveform information, and natural language processing means for analyzing input text information and extracting emotions and themes. This makes it possible for ordinary users, even without specialized knowledge of music or video, to easily create professional-quality music and video works and share them securely on an online platform.

[0510] "Audio information" refers to raw audio data before it is converted into a digital format.

[0511] "Digital waveform information" refers to audio information that has been digitized and converted into a format that can be processed by a computer.

[0512] "Speech recognition means" refers to technology or equipment for converting speech information into digital waveform information.

[0513] "Melody characteristics" refer to information that describes the basic pitch, rhythm, and other musical elements of a musical work.

[0514] "Analysis means" refers to technology or equipment for analyzing digital waveform information or text information and extracting necessary features and information.

[0515] "Textual information" refers to text data entered by users, particularly information related to lyrics and themes.

[0516] "Natural language processing means" refers to technologies or devices that analyze textual information and extract emotions and themes.

[0517] A "musical work" refers to a piece of music that is automatically generated based on the characteristics of sound and melody.

[0518] "Video generation means" refers to a technology or device that automatically creates video based on a generated musical work.

[0519] A "music video work" refers to a work that combines a generated musical piece with corresponding video content.

[0520] "Content review means" refers to technology or equipment that checks generated information for inappropriate content or copyright infringement.

[0521] "Network infrastructure" refers to the internet and similar data communication infrastructure, which provides a platform for sharing content online.

[0522] This invention is a system that allows users to automatically generate and share music and video content. The user uses a terminal to first record a hum, and then provides related lyric ideas as text input. The terminal collects the input audio information and converts it into digital waveform information using speech recognition software. The terminal's application implements interfaces for digital audio and text manipulation.

[0523] The server uses a special speech analysis engine to analyze the received digital waveform information and extract melodic characteristics. It also uses a natural language processing module to analyze the input text information and identify emotions and themes. To perform these processes, the server is equipped with machine learning models and databases.

[0524] Based on the analyzed data, the server automatically generates musical works using a generative AI model. Depending on the characteristics of these musical works, video generation software is used to create video footage, which is then edited into a musical video. The video generation utilizes scene selection algorithms that correspond to the rhythm and atmosphere of the music.

[0525] Subsequently, the generated music videos and songs are evaluated by moderation software to ensure their appropriateness and the absence of copyright issues. If no problems are found at this stage, the server automatically uploads the music video works to various online platforms via the network infrastructure.

[0526] As a concrete example, a user might record themselves humming a tune with the theme "A joyful summer day" and input lyrics that fit that theme. This data is sent to a server, where it is analyzed and automatically generated. A bright and cheerful song is then created, and accompanying images of a blue sky or beach are incorporated into the music video.

[0527] An example of a prompt message is, "Analyze a melody and lyrics that evoke a feeling of joy on a summer day, and generate music and visuals that match them." This is a message that instructs the AI ​​on what kind of work it should produce.

[0528] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0529] Step 1:

[0530] The user records humming using the device. The device has a recording application installed that collects audio information input through the microphone. In this process, the user's humming is converted from analog format to digital audio format and saved.

[0531] Step 2:

[0532] The user provides lyric ideas as text input on their device. This text information is managed by the device's application and prepared as a data packet along with the audio information. This data packet is then ready to be transmitted over the network.

[0533] Step 3:

[0534] The terminal transmits prepared voice and text information to the server over the network. To maintain data integrity and confidentiality, the data is transmitted using encryption technology. The input here is an encrypted data packet, and the output is the data as it reaches the server.

[0535] Step 4:

[0536] The server processes the received audio information using speech recognition software and converts it into digital waveform information. This process extracts melodic characteristics such as rhythm and pitch from the input analog audio. The output is a digital melody profile.

[0537] Step 5:

[0538] The server uses a natural language processing module to analyze the input text information. Based on the prompt, it identifies the sentiment and subject of the text and generates related structured data. The input for this analysis is character information, and the output is the sentiment and subject information of the text.

[0539] Step 6:

[0540] The server uses a generative AI model to automatically generate musical works based on the analysis results of speech and text. The input is melodic characteristics and emotional / thematic information from the text, and the output is the completed musical work. At this stage, melody, rhythm, and harmony are integrated.

[0541] Step 7:

[0542] The server runs video generation software to produce videos based on musical works. Scenes that match the rhythm and atmosphere of the music are selected and edited. The input is the completed musical work, and the output is a music video.

[0543] Step 8:

[0544] The server reviews the generated content using moderation software, checking for inappropriate content and copyright infringement. The input is music video works, and the output is the verified content.

[0545] Step 9:

[0546] The server uploads music and video works to various online platforms. Here, the content is published to the social networking services and video sharing services specified by the user. The input consists of verified music and video works, and the output is widely publicized.

[0547] (Application Example 1)

[0548] 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."

[0549] In recent years, there has been a growing demand for individuals to easily create and widely share music and video content. However, this often requires specialized knowledge and skills, making it difficult for individual users to easily bring their ideas to life. There is a need to solve this problem and provide a system that allows anyone to easily enjoy creative activities.

[0550] 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.

[0551] In this invention, the server includes an audio processing means for converting input audio information into digital waveform information, an analysis means for extracting a melody profile from the converted digital waveform information, and a means for automatically generating music and video with a nostalgic atmosphere from user input audio and text. This makes it possible for users to easily create and share professional-quality music and video content based on their personal ideas, even without specialized knowledge.

[0552] "Audio processing means" refers to technology for converting input audio information into digital waveform information.

[0553] "Analysis means" refers to a method for extracting a melody profile from converted digital waveform information.

[0554] "Natural language processing methods" are techniques for analyzing input text information and extracting the emotions and themes contained within it.

[0555] "Music generation means" refers to technology that automatically generates music based on the results of analysis.

[0556] "Video generation means" refers to techniques for creating visual media based on generated music.

[0557] "Moderation measures" are mechanisms for checking generated information to ensure it does not contain inappropriate content or infringe on copyright.

[0558] An "online information dissemination platform" is an internet platform that can automatically transmit and share generated visual media and music.

[0559] A "machine learning model" is an algorithm used in analysis and generation processes to learn patterns from data and apply that knowledge to improve results.

[0560] A "nostalgic atmosphere" refers to a theme or emotion that evokes a feeling of recalling the past based on user input.

[0561] To implement this invention, a server, a terminal, and a network environment connecting them are required. The user records voice and inputs text information using the terminal. The voice information collected by the terminal is transmitted to the server via the network.

[0562] The server uses a speech recognition library (e.g., Google Cloud Speech-to-Text) as an acoustic processing tool to convert speech information into digital waveform information. Then, dedicated software, acting as an analysis tool, extracts a melody profile from the digital waveform information.

[0563] Furthermore, a natural language processing library (e.g., spaCy) is used to analyze text information entered by the user, extracting emotions and themes from it. Based on the extracted melody profile and the information obtained from the text, music is automatically generated using tools such as Magenta as a music generation tool.

[0564] Based on the generated music, visual media is created using video generation software (e.g., FFmpeg). This results in a music video with a nostalgic atmosphere. The generated visual media and music are checked by moderation tools to ensure there is no inappropriate content or copyright infringement. After verification, the video and music are automatically sent to online information dissemination platforms (e.g., YouTube and Instagram).

[0565] For example, if a user records themselves humming a tune with the theme "a nostalgic sunset" and inputs related lyrics, the server can generate a song that evokes a sense of nostalgia and create a music video combining it with footage of a sunset. An example of a prompt to the generation AI model would be, "Based on the user's humming and lyrics, generate a song with a nostalgic atmosphere and automatically create a video to match it."

[0566] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0567] Step 1:

[0568] The user records audio using a device and inputs text information. The input information consists of an audio file of the user humming and lyrics in text format. This audio file is prepared to be converted into waveform data for use in subsequent processes.

[0569] Step 2:

[0570] The terminal transmits recorded audio information to the server via the network. The input here is an audio file, and the output is the state after it has been transferred to the server. This process allows the audio data to be stored on a central server for more advanced analysis.

[0571] Step 3:

[0572] The server uses a speech recognition library as an acoustic processing tool to convert audio information into digital waveform information. The input is an audio file, and the output is digital waveform data. This conversion processes the audio data into a format that is easy to analyze.

[0573] Step 4:

[0574] The server uses analysis tools to extract melodic profiles from digital waveform data. The input is the converted digital waveform data, and the output is the melodic profile. This analysis extracts the characteristics of the sound, which then serves as a guide for subsequent music generation.

[0575] Step 5:

[0576] The server analyzes text information using natural language processing to extract emotions and themes. The input is text data provided by the user, and the output is data related to emotions and themes. Through this analysis, the mood and direction of the song are determined.

[0577] Step 6:

[0578] The server uses music generation technology to automatically generate music based on extracted melody profiles and themes and emotions derived from text. The input consists of melody profile, emotion, and theme data, while the output is a newly generated song. This generation process combines various musical elements to create unique compositions.

[0579] Step 7:

[0580] The server creates visual media based on music generated using video generation methods. The input is the generated music, and the output is a music video with a nostalgic atmosphere. This process integrates music and visual information to create compelling content.

[0581] Step 8:

[0582] The server uses moderation mechanisms to check the video and music for inappropriate content and copyright infringement. The input is the generated music video and music, and the output is the content after verification. This verification process ensures that the content being distributed is appropriate.

[0583] Step 9:

[0584] The server automatically transmits verified visual media and music to the online information dissemination platform. The input is verified music videos, and the output is publicly available on the online platform. This final step allows users to widely publish and share their work.

[0585] 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.

[0586] This invention provides a system that delivers more personalized content by recognizing the user's emotional state and dynamically generating music and video accordingly. This system consists of a network system including terminals, servers, and an emotion engine.

[0587] User input process

[0588] The user records humming using the device and inputs the lyrics. In addition, the device uses an emotion engine to infer the user's current emotions. This includes using the user's voice tone, facial recognition, or other physiological signals. The device sends this data to the system's server.

[0589] Data Analysis Process

[0590] The server converts the received audio data into digital waveform data using speech recognition and extracts the melody profile. It also analyzes the emotion and theme of the lyrics using natural language processing. Simultaneously, emotion data obtained from the emotion engine is also used, and these analysis results are integrated.

[0591] Content generation

[0592] The music generation system combines analyzed audio, lyrics, and emotional data to create a song. The emotional data, in particular, influences the melody, rhythm, and harmonic structure of the song. The video generation system designs the visuals to match the emotional tone of the generated song and produces a music video.

[0593] Content moderation and sharing

[0594] The generated music and videos are reviewed by moderation mechanisms to ensure they do not contain inappropriate content or infringe on copyrights. If there are no issues, the server automatically uploads the content to online platforms and makes it available on the various platforms selected by the user.

[0595] Specific example

[0596] For example, if a user records themselves humming a tune with the theme of "a hopeful dawn," and the user's emotion engine detects "joy," the server will automatically generate a positive and refreshing melody based on this information. The video generation system will then create a video combining bright dawn scenery and dynamic scenes. In this way, music and visuals that fit the user's emotions are provided, resulting in more emotionally resonant content. This invention allows users to easily share musical works that reflect their own emotions with the world.

[0597] The following describes the processing flow.

[0598] Step 1:

[0599] The user records humming using the device's microphone and inputs lyric ideas in text format. Simultaneously, the device collects emotional data to infer emotions from the user's face and voice.

[0600] Step 2:

[0601] The device packages recorded audio data, entered text data, and emotion data, and sends them to the server via the network.

[0602] Step 3:

[0603] The server uses speech recognition to convert the received audio data into digital waveform data and extracts a melody profile. This process allows the user's humming to be used as the basis for the music.

[0604] Step 4:

[0605] The server uses natural language processing to analyze text data and extract the themes and emotions of the lyrics. The analyzed information is then used as a factor in determining the structure of the song.

[0606] Step 5:

[0607] The server uses data from the emotion engine to evaluate the user's emotional state and integrates this with other analysis results. This integrated information is used to define the mood and emotional characteristics of the music.

[0608] Step 6:

[0609] The music generation method creates a song by considering the melody profile, the theme of the lyrics, and the user's emotions, and adjusts each element of the song to have a consistent emotional tone.

[0610] Step 7:

[0611] The video generation method creates a music video based on the emotions and themes of the generated song. The scene composition and color adjustments of the video reflect the user's emotional data.

[0612] Step 8:

[0613] The generated content is checked for inappropriate material and copyright infringement using moderation mechanisms. After this check, the server automatically uploads and publishes the content to the designated online platform.

[0614] (Example 2)

[0615] 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."

[0616] In a world with numerous entertainment systems, there is a growing demand for dynamic and personalized content that resonates with users' emotions. Traditional systems struggle to generate music and visuals that accurately reflect user emotions, and they lack the means to publish the created content without the risk of inappropriate content or copyright infringement. As a result, individual users' experiences are often limited.

[0617] 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.

[0618] In this invention, the server includes speech recognition means for converting input audio information into digital waveform data, analysis means for extracting melody data from the converted digital waveform data, and natural language processing means for analyzing input character data. This enables the generation of music and videos that reflect the user's emotions, as well as appropriate verification and publication of the content.

[0619] "Speech recognition means" refers to technology for converting speech information into digital waveform data.

[0620] "Analysis method" refers to a technique for extracting melody data from digital waveform data.

[0621] "Natural language processing means" refers to technologies that analyze text data and extract emotions and themes.

[0622] "Music generation means" refers to technology that automatically generates music based on analyzed data.

[0623] "Video generation means" refers to a technology that generates visual data based on a generated song to create a music video.

[0624] "Emotion measurement means" refers to technology that measures a user's emotional state and integrates that data within a system.

[0625] "Moderation methods" are technologies that inspect generated content to identify inappropriate material or copyright infringement.

[0626] This invention is a system that generates personalized music and video content based on the user's emotions. The system includes a terminal, a server, and an emotion engine.

[0627] The user records humming using the device. The device provides a lyric input function and also features an emotion engine to detect emotions from the user's voice tone and facial expressions. This engine analyzes the voice and facial expressions, and, if necessary, acquires physiological data from a heart rate sensor and other sources.

[0628] The terminal sends the acquired data to the server. The server uses speech recognition software to convert the speech information into digital waveform data. Here, common speech recognition technology is utilized. The server then performs analysis to extract a melody profile from the digital waveform data. Next, natural language processing technology is used to analyze the emotion and theme of the lyrics entered by the user. This determines how the information entered by the user should be reflected in the music.

[0629] In music generation, the server utilizes a learning model. This allows it to generate music based on the analyzed data. Emotional data is a factor that influences the rhythm, melody, and harmonic structure of the music.

[0630] In video generation, technology for generating visual data is used to automatically create music videos that match the music. The video generation method designs the video using visual materials that match the emotional tone of the generated music.

[0631] A concrete example of this invention is when a user records humming a tune with the theme of "a hopeful dawn," and the emotion engine detects "joy." In this case, the server automatically generates a positive and refreshing melody, and the video generation means provides a music video combining a bright dawn scene with a dynamic scene.

[0632] As an example of a prompt, the system can be instructed as follows: "If the user's emotion is 'joy,' generate a bright and refreshing song and a video that matches it." This makes it possible to efficiently generate content that resonates with the user's emotions.

[0633] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0634] Step 1:

[0635] The user records humming using the device and inputs the lyrics. The device captures the audio using its microphone and retrieves the lyrics as text through the input interface. The device also analyzes facial expressions and voice tone using its camera and sensors, and uses an emotion engine to infer the user's current emotion. The input data includes audio data, text data, and emotion data.

[0636] Step 2:

[0637] The terminal sends the acquired voice data, text data, and sentiment data to the server. Encryption technology is used to ensure the security of this communication. The server receives this data and prepares it for use in the next processing step. The output is all the input data passed to the server.

[0638] Step 3:

[0639] The server converts the received audio data into digital waveform data using speech recognition. Specifically, a speech recognition algorithm analyzes the audio data and converts it into text data. Next, it analyzes the waveform data to extract the melody profile. The input is audio data, and the output is the melody profile.

[0640] Step 4:

[0641] The server analyzes text data using natural language processing techniques to extract emotions and themes. In doing so, it processes the text data using sentiment analysis algorithms to understand the user's intent and themes. The input is the text data of the lyrics, and the output is the emotional data and themes of the text.

[0642] Step 5:

[0643] The server integrates analyzed melody profiles, themes from text, and emotion data, and automatically generates music using music generation methods. A generation AI model is used, and this data is reflected in the melody and rhythm of the music. The input is the melody profile and integrated emotion / theme data, and the output is the generated music.

[0644] Step 6:

[0645] The server uses a video generation system to create a visual music video based on the generated music. This is achieved by a video generation AI model that constructs visual materials according to the emotional tone of the music. The input is the generated music, and the output is a music video.

[0646] Step 7:

[0647] The generated music and video are verified by moderation mechanisms on the server. Content moderation technology is used to ensure there is no inappropriate content or copyright issues. Once this is confirmed, the generated material proceeds to the next process. The output is verified content.

[0648] (Application Example 2)

[0649] 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."

[0650] Personalizing in-home entertainment experiences and providing music and video content tailored to users' emotions in real time has been difficult with conventional technologies. In particular, there is a need for an efficient system that can effectively recognize emotional states and dynamically generate content accordingly.

[0651] 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.

[0652] In this invention, the server includes a speech recognition means for converting input audio data into digital waveform data, an analysis means for extracting a melody profile from the converted digital waveform data, and a means for integrating music and video generated in response to the user's real-time emotions into the home system. This enables the real-time provision of personalized music and video that matches the user's emotions within the home.

[0653] "Speech recognition means" refers to a technology that processes input speech data into digital waveform data.

[0654] The "analysis method" is a technique for extracting a melody profile from the converted digital waveform data.

[0655] "Natural language processing means" refers to technologies that analyze input text data and extract emotions and themes.

[0656] "Music generation means" refers to technology that automatically generates music based on the results of analysis.

[0657] "Video generation means" refers to a technology that generates video based on a generated song to create a music video.

[0658] "Methods for integration into home systems" refers to technologies that integrate generated music and video into a system that provides them within the home in response to the user's real-time emotions.

[0659] A "machine learning model" is an algorithm that learns from empirical data during the analysis and generation process to perform predictions, classifications, and generation.

[0660] This invention describes a home entertainment system that generates and delivers music and video that reflects the user's emotions in real time. The main processes consist of input from the user's terminal, analysis and generation on a server, and delivery on the home system.

[0661] The user uses a terminal to input humming or text data and sends it to the server. The terminal is equipped with a microphone for speech recognition and a camera for real-time emotion detection. The voice data is converted into digital waveform data by a speech recognition system. The data is analyzed to extract a melody profile, and the emotion and theme are analyzed by a natural language processing system.

[0662] The server automatically generates music using music generation equipment based on the analyzed data, and then creates a music video using video generation equipment. This process utilizes machine learning models based on empirical data to refine the generation process.

[0663] The home system provides music and visuals generated in response to the user's real-time emotions. For example, if a user feels refreshed and hums a cheerful tune, the system will generate music with a refreshing melody and visuals of a sunny park as the background, and immediately display them on the screen.

[0664] Here is an example of a prompt message for this invention: "Take the humming the user sings in a refreshed mood as input, and generate content that combines an upbeat melody with a visual of a park on a sunny day."

[0665] This system allows users to easily enjoy personalized entertainment tailored to their emotional state.

[0666] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0667] Step 1:

[0668] The user records humming using the device and inputs the lyrics as text data. The input audio data is captured via the device's microphone, and the text data is provided by the user through the device's interface. The device sends this data to the server for the next processing step.

[0669] Step 2:

[0670] The server converts the received audio data into digital waveform data using speech recognition. This conversion process extracts audio patterns from the recorded humming and converts them into an analyzable digital format. The output data is digital waveform data ready for extracting a melody profile.

[0671] Step 3:

[0672] The server extracts a melody profile from the digital waveform data converted using an analysis tool. Here, the melody phrases and rhythms are identified. The extracted melody profile becomes the input for the next music generation process.

[0673] Step 4:

[0674] The server uses natural language processing to analyze the received text data and extract emotions and themes. This process identifies emotional expressions and keywords contained within the text and extracts elements that influence the overall theme of the song. The extracted emotional data and theme information are then supplied to the music generation process.

[0675] Step 5:

[0676] The server uses music generation tools to automatically generate music by combining analyzed melody profiles and emotion data. During the generation process, the server adjusts musical attributes such as melody and rhythm according to the emotion elements. The generated music is then used in the next video generation step.

[0677] Step 6:

[0678] The server uses video generation tools to create visuals that match the emotional tone and theme of the generated song, completing the music video. In this process, the server selects relevant video footage and seamlessly integrates it with the song for a visually appealing presentation. The output is a custom video for the user's entertainment experience.

[0679] Step 7:

[0680] Based on emotion prediction data transmitted from the device, the home system proposes generated music and video that matches the user's real-time emotions. The home system then instantly delivers the content via a display and sound system, allowing the user to enjoy it.

[0681] 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.

[0682] 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.

[0683] 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.

[0684] [Fourth Embodiment]

[0685] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0686] 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.

[0687] 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).

[0688] 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.

[0689] 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.

[0690] 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).

[0691] 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.

[0692] 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.

[0693] 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.

[0694] 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.

[0695] 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.

[0696] 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.

[0697] 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".

[0698] This invention provides a system that enables users to automatically create and widely share entirely new songs and music videos by providing simple humming or lyric ideas. This system is implemented using terminals, servers, and a network connecting them.

[0699] User input process

[0700] The user records humming using the device and inputs lyric ideas as text. The device collects this data and sends it to a server over the network.

[0701] Data Analysis Process

[0702] The server converts the transmitted audio data into digital waveform data using speech recognition. Analysis is then performed on this converted data to extract a melody profile. Text data is also analyzed using natural language processing to extract its emotions and themes. This organizes the fundamental information that determines the atmosphere and constituent elements of the music.

[0703] Music production and video production

[0704] The server integrates the analyzed audio and text data and automatically generates music through a music generation system. This process combines melody, rhythm, and harmony to transform the user's ideas into a concrete musical work. Based on the generated music, visual content is created using a video generation system, and a music video is compiled. At this stage, scene composition can be tailored to the rhythm and atmosphere of the music.

[0705] Content moderation and sharing

[0706] The generated songs and music videos undergo moderation to check for inappropriate content and copyright infringement. If no issues are found, the content is automatically uploaded to online platforms and immediately made available on various platforms specified by the user, such as social media and video sharing services.

[0707] Specific example

[0708] For example, if a user records themselves humming a tune with the theme "A joyful summer day" and inputs related lyrics, the server will generate an upbeat song based on that intention and create a music video combining it with images of a blue sky and a beach. In this way, users can obtain professional-quality works without any expertise in music or video production.

[0709] In this way, the invention lowers the barrier to music production and provides an environment where anyone can easily share music and video works with the world.

[0710] The following describes the processing flow.

[0711] Step 1:

[0712] The user records humming using the device's microphone and inputs lyric ideas in text format. The device saves this data along with session information.

[0713] Step 2:

[0714] The device sends the stored audio and text data to the server. During this process, the data format may be standardized and compressed.

[0715] Step 3:

[0716] The server starts the speech recognition engine and converts the received audio data into digital waveform data. Next, the analysis module extracts the melody profile and analyzes musical characteristics such as tempo and pitch.

[0717] Step 4:

[0718] The server uses a natural language processing engine to analyze the received text data. From the analysis results, it extracts the emotional tone and themes of the lyrics and generates metadata to determine the direction of the song.

[0719] Step 5:

[0720] The server integrates the results of speech and text analysis and creates music using an AI-powered music generation module. This module designs the melody structure, harmony, and rhythm patterns to complete a professional-quality song.

[0721] Step 6:

[0722] The server activates a video generation engine based on the generated music and edits the music video. It constructs video scenes in accordance with the rhythm and theme of the music and adds visual effects.

[0723] Step 7:

[0724] The server runs a moderation engine on the generated songs and music videos to check for inappropriate content. It also checks for copyright infringement risks.

[0725] Step 8:

[0726] If there are no problems, the server will automatically upload the completed content to the specified online platform. At this time, metadata such as titles and tags will also be automatically generated as needed.

[0727] (Example 1)

[0728] 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".

[0729] Traditionally, music and video production required specialized knowledge and skills, which was a barrier for the average user. Furthermore, verifying copyright and content appropriateness when sharing music and videos online was a time-consuming process. Thus, there was a need for a system that would allow ordinary people to easily create high-quality music and video works and share them widely with confidence.

[0730] 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.

[0731] In this invention, the server includes speech recognition means for converting input audio information into digital waveform information, analysis means for extracting melodic characteristics from the converted digital waveform information, and natural language processing means for analyzing input text information and extracting emotions and themes. This makes it possible for ordinary users, even without specialized knowledge of music or video, to easily create professional-quality music and video works and share them securely on an online platform.

[0732] "Audio information" refers to raw audio data before it is converted into a digital format.

[0733] "Digital waveform information" refers to audio information that has been digitized and converted into a format that can be processed by a computer.

[0734] "Speech recognition means" refers to technology or equipment for converting speech information into digital waveform information.

[0735] "Melody characteristics" refer to information that describes the basic pitch, rhythm, and other musical elements of a musical work.

[0736] "Analysis means" refers to technology or equipment for analyzing digital waveform information or text information and extracting necessary features and information.

[0737] "Textual information" refers to text data entered by users, particularly information related to lyrics and themes.

[0738] "Natural language processing means" refers to technologies or devices that analyze textual information and extract emotions and themes.

[0739] A "musical work" refers to a piece of music that is automatically generated based on the characteristics of sound and melody.

[0740] "Video generation means" refers to a technology or device that automatically creates video based on a generated musical work.

[0741] A "music video work" refers to a work that combines a generated musical piece with corresponding video content.

[0742] "Content review means" refers to technology or equipment that checks generated information for inappropriate content or copyright infringement.

[0743] "Network infrastructure" refers to the internet and similar data communication infrastructure, which provides a platform for sharing content online.

[0744] This invention is a system that allows users to automatically generate and share music and video content. The user uses a terminal to first record a hum, and then provides related lyric ideas as text input. The terminal collects the input audio information and converts it into digital waveform information using speech recognition software. The terminal's application implements interfaces for digital audio and text manipulation.

[0745] The server uses a special speech analysis engine to analyze the received digital waveform information and extract melodic characteristics. It also uses a natural language processing module to analyze the input text information and identify emotions and themes. To perform these processes, the server is equipped with machine learning models and databases.

[0746] Based on the analyzed data, the server automatically generates musical works using a generative AI model. Depending on the characteristics of these musical works, video generation software is used to create video footage, which is then edited into a musical video. The video generation utilizes scene selection algorithms that correspond to the rhythm and atmosphere of the music.

[0747] Subsequently, the generated music videos and songs are evaluated by moderation software to ensure their appropriateness and the absence of copyright issues. If no problems are found at this stage, the server automatically uploads the music video works to various online platforms via the network infrastructure.

[0748] As a concrete example, a user might record themselves humming a tune with the theme "A joyful summer day" and input lyrics that fit that theme. This data is sent to a server, where it is analyzed and automatically generated. A bright and cheerful song is then created, and accompanying images of a blue sky or beach are incorporated into the music video.

[0749] An example of a prompt message is, "Analyze a melody and lyrics that evoke a feeling of joy on a summer day, and generate music and visuals that match them." This is a message that instructs the AI ​​on what kind of work it should produce.

[0750] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0751] Step 1:

[0752] The user records humming using the device. The device has a recording application installed that collects audio information input through the microphone. In this process, the user's humming is converted from analog format to digital audio format and saved.

[0753] Step 2:

[0754] The user provides lyric ideas as text input on their device. This text information is managed by the device's application and prepared as a data packet along with the audio information. This data packet is then ready to be transmitted over the network.

[0755] Step 3:

[0756] The terminal transmits prepared voice and text information to the server over the network. To maintain data integrity and confidentiality, the data is transmitted using encryption technology. The input here is an encrypted data packet, and the output is the data as it reaches the server.

[0757] Step 4:

[0758] The server processes the received audio information using speech recognition software and converts it into digital waveform information. This process extracts melodic characteristics such as rhythm and pitch from the input analog audio. The output is a digital melody profile.

[0759] Step 5:

[0760] The server uses a natural language processing module to analyze the input text information. Based on the prompt, it identifies the sentiment and subject of the text and generates related structured data. The input for this analysis is character information, and the output is the sentiment and subject information of the text.

[0761] Step 6:

[0762] The server uses a generative AI model to automatically generate musical works based on the analysis results of speech and text. The input is melodic characteristics and emotional / thematic information from the text, and the output is the completed musical work. At this stage, melody, rhythm, and harmony are integrated.

[0763] Step 7:

[0764] The server runs video generation software to produce videos based on musical works. Scenes that match the rhythm and atmosphere of the music are selected and edited. The input is the completed musical work, and the output is a music video.

[0765] Step 8:

[0766] The server reviews the generated content using moderation software, checking for inappropriate content and copyright infringement. The input is music video works, and the output is the verified content.

[0767] Step 9:

[0768] The server uploads music and video works to various online platforms. Here, the content is published to the social networking services and video sharing services specified by the user. The input consists of verified music and video works, and the output is widely publicized.

[0769] (Application Example 1)

[0770] 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".

[0771] In recent years, there has been a growing demand for individuals to easily create and widely share music and video content. However, this often requires specialized knowledge and skills, making it difficult for individual users to easily bring their ideas to life. There is a need to solve this problem and provide a system that allows anyone to easily enjoy creative activities.

[0772] 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.

[0773] In this invention, the server includes an audio processing means for converting input audio information into digital waveform information, an analysis means for extracting a melody profile from the converted digital waveform information, and a means for automatically generating music and video with a nostalgic atmosphere from user input audio and text. This makes it possible for users to easily create and share professional-quality music and video content based on their personal ideas, even without specialized knowledge.

[0774] "Audio processing means" refers to technology for converting input audio information into digital waveform information.

[0775] "Analysis means" refers to a method for extracting a melody profile from converted digital waveform information.

[0776] "Natural language processing methods" are techniques for analyzing input text information and extracting the emotions and themes contained within it.

[0777] "Music generation means" refers to technology that automatically generates music based on the results of analysis.

[0778] "Video generation means" refers to techniques for creating visual media based on generated music.

[0779] "Moderation measures" are mechanisms for checking generated information to ensure it does not contain inappropriate content or infringe on copyright.

[0780] An "online information dissemination platform" is an internet platform that can automatically transmit and share generated visual media and music.

[0781] A "machine learning model" is an algorithm used in analysis and generation processes to learn patterns from data and apply that knowledge to improve results.

[0782] A "nostalgic atmosphere" refers to a theme or emotion that evokes a feeling of recalling the past based on user input.

[0783] To implement this invention, a server, a terminal, and a network environment connecting them are required. The user records voice and inputs text information using the terminal. The voice information collected by the terminal is transmitted to the server via the network.

[0784] The server uses a speech recognition library (e.g., Google Cloud Speech-to-Text) as an acoustic processing tool to convert speech information into digital waveform information. Then, dedicated software, acting as an analysis tool, extracts a melody profile from the digital waveform information.

[0785] Furthermore, a natural language processing library (e.g., spaCy) is used to analyze text information entered by the user, extracting emotions and themes from it. Based on the extracted melody profile and the information obtained from the text, music is automatically generated using tools such as Magenta as a music generation tool.

[0786] Based on the generated music, visual media is created using video generation software (e.g., FFmpeg). This results in a music video with a nostalgic atmosphere. The generated visual media and music are checked by moderation tools to ensure there is no inappropriate content or copyright infringement. After verification, the video and music are automatically sent to online information dissemination platforms (e.g., YouTube and Instagram).

[0787] For example, if a user records themselves humming a tune with the theme "a nostalgic sunset" and inputs related lyrics, the server can generate a song that evokes a sense of nostalgia and create a music video combining it with footage of a sunset. An example of a prompt to the generation AI model would be, "Based on the user's humming and lyrics, generate a song with a nostalgic atmosphere and automatically create a video to match it."

[0788] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0789] Step 1:

[0790] The user records audio using a device and inputs text information. The input information consists of an audio file of the user humming and lyrics in text format. This audio file is prepared to be converted into waveform data for use in subsequent processes.

[0791] Step 2:

[0792] The terminal transmits recorded audio information to the server via the network. The input here is an audio file, and the output is the state after it has been transferred to the server. This process allows the audio data to be stored on a central server for more advanced analysis.

[0793] Step 3:

[0794] The server uses a speech recognition library as an acoustic processing tool to convert audio information into digital waveform information. The input is an audio file, and the output is digital waveform data. This conversion processes the audio data into a format that is easy to analyze.

[0795] Step 4:

[0796] The server uses analysis tools to extract melodic profiles from digital waveform data. The input is the converted digital waveform data, and the output is the melodic profile. This analysis extracts the characteristics of the sound, which then serves as a guide for subsequent music generation.

[0797] Step 5:

[0798] The server analyzes text information using natural language processing to extract emotions and themes. The input is text data provided by the user, and the output is data related to emotions and themes. Through this analysis, the mood and direction of the song are determined.

[0799] Step 6:

[0800] The server uses music generation technology to automatically generate music based on extracted melody profiles and themes and emotions derived from text. The input consists of melody profile, emotion, and theme data, while the output is a newly generated song. This generation process combines various musical elements to create unique compositions.

[0801] Step 7:

[0802] The server creates visual media based on music generated using video generation methods. The input is the generated music, and the output is a music video with a nostalgic atmosphere. This process integrates music and visual information to create compelling content.

[0803] Step 8:

[0804] The server uses moderation mechanisms to check the video and music for inappropriate content and copyright infringement. The input is the generated music video and music, and the output is the content after verification. This verification process ensures that the content being distributed is appropriate.

[0805] Step 9:

[0806] The server automatically transmits verified visual media and music to the online information dissemination platform. The input is verified music videos, and the output is publicly available on the online platform. This final step allows users to widely publish and share their work.

[0807] 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.

[0808] This invention provides a system that delivers more personalized content by recognizing the user's emotional state and dynamically generating music and video accordingly. This system consists of a network system including terminals, servers, and an emotion engine.

[0809] User input process

[0810] The user records humming using the device and inputs the lyrics. In addition, the device uses an emotion engine to infer the user's current emotions. This includes using the user's voice tone, facial recognition, or other physiological signals. The device sends this data to the system's server.

[0811] Data Analysis Process

[0812] The server converts the received audio data into digital waveform data using speech recognition and extracts the melody profile. It also analyzes the emotion and theme of the lyrics using natural language processing. Simultaneously, emotion data obtained from the emotion engine is also used, and these analysis results are integrated.

[0813] Content generation

[0814] The music generation system combines analyzed audio, lyrics, and emotional data to create a song. The emotional data, in particular, influences the melody, rhythm, and harmonic structure of the song. The video generation system designs the visuals to match the emotional tone of the generated song and produces a music video.

[0815] Content moderation and sharing

[0816] The generated music and videos are reviewed by moderation mechanisms to ensure they do not contain inappropriate content or infringe on copyrights. If there are no issues, the server automatically uploads the content to online platforms and makes it available on the various platforms selected by the user.

[0817] Specific example

[0818] For example, if a user records themselves humming a tune with the theme of "a hopeful dawn," and the user's emotion engine detects "joy," the server will automatically generate a positive and refreshing melody based on this information. The video generation system will then create a video combining bright dawn scenery and dynamic scenes. In this way, music and visuals that fit the user's emotions are provided, resulting in more emotionally resonant content. This invention allows users to easily share musical works that reflect their own emotions with the world.

[0819] The following describes the processing flow.

[0820] Step 1:

[0821] The user records humming using the device's microphone and inputs lyric ideas in text format. Simultaneously, the device collects emotional data to infer emotions from the user's face and voice.

[0822] Step 2:

[0823] The device packages recorded audio data, entered text data, and emotion data, and sends them to the server via the network.

[0824] Step 3:

[0825] The server uses speech recognition to convert the received audio data into digital waveform data and extracts a melody profile. This process allows the user's humming to be used as the basis for the music.

[0826] Step 4:

[0827] The server uses natural language processing to analyze text data and extract the themes and emotions of the lyrics. The analyzed information is then used as a factor in determining the structure of the song.

[0828] Step 5:

[0829] The server uses data from the emotion engine to evaluate the user's emotional state and integrates this with other analysis results. This integrated information is used to define the mood and emotional characteristics of the music.

[0830] Step 6:

[0831] The music generation method creates a song by considering the melody profile, the theme of the lyrics, and the user's emotions, and adjusts each element of the song to have a consistent emotional tone.

[0832] Step 7:

[0833] The video generation method creates a music video based on the emotions and themes of the generated song. The scene composition and color adjustments of the video reflect the user's emotional data.

[0834] Step 8:

[0835] The generated content is checked for inappropriate material and copyright infringement using moderation mechanisms. After this check, the server automatically uploads and publishes the content to the designated online platform.

[0836] (Example 2)

[0837] 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".

[0838] In a world with numerous entertainment systems, there is a growing demand for dynamic and personalized content that resonates with users' emotions. Traditional systems struggle to generate music and visuals that accurately reflect user emotions, and they lack the means to publish the created content without the risk of inappropriate content or copyright infringement. As a result, individual users' experiences are often limited.

[0839] 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.

[0840] In this invention, the server includes speech recognition means for converting input audio information into digital waveform data, analysis means for extracting melody data from the converted digital waveform data, and natural language processing means for analyzing input character data. This enables the generation of music and videos that reflect the user's emotions, as well as appropriate verification and publication of the content.

[0841] "Speech recognition means" refers to technology for converting speech information into digital waveform data.

[0842] "Analysis method" refers to a technique for extracting melody data from digital waveform data.

[0843] "Natural language processing means" refers to technologies that analyze text data and extract emotions and themes.

[0844] "Music generation means" refers to technology that automatically generates music based on analyzed data.

[0845] "Video generation means" refers to a technology that generates visual data based on a generated song to create a music video.

[0846] "Emotion measurement means" refers to technology that measures a user's emotional state and integrates that data within a system.

[0847] "Moderation methods" are technologies that inspect generated content to identify inappropriate material or copyright infringement.

[0848] This invention is a system that generates personalized music and video content based on the user's emotions. The system includes a terminal, a server, and an emotion engine.

[0849] The user records humming using the device. The device provides a lyric input function and also features an emotion engine to detect emotions from the user's voice tone and facial expressions. This engine analyzes the voice and facial expressions, and, if necessary, acquires physiological data from a heart rate sensor and other sources.

[0850] The terminal sends the acquired data to the server. The server uses speech recognition software to convert the speech information into digital waveform data. Here, common speech recognition technology is utilized. The server then performs analysis to extract a melody profile from the digital waveform data. Next, natural language processing technology is used to analyze the emotion and theme of the lyrics entered by the user. This determines how the information entered by the user should be reflected in the music.

[0851] In music generation, the server utilizes a learning model. This allows it to generate music based on the analyzed data. Emotional data is a factor that influences the rhythm, melody, and harmonic structure of the music.

[0852] In video generation, technology for generating visual data is used to automatically create music videos that match the music. The video generation method designs the video using visual materials that match the emotional tone of the generated music.

[0853] A concrete example of this invention is when a user records humming a tune with the theme of "a hopeful dawn," and the emotion engine detects "joy." In this case, the server automatically generates a positive and refreshing melody, and the video generation means provides a music video combining a bright dawn scene with a dynamic scene.

[0854] As an example of a prompt, the system can be instructed as follows: "If the user's emotion is 'joy,' generate a bright and refreshing song and a video that matches it." This makes it possible to efficiently generate content that resonates with the user's emotions.

[0855] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0856] Step 1:

[0857] The user records humming using the device and inputs the lyrics. The device captures the audio using its microphone and retrieves the lyrics as text through the input interface. The device also analyzes facial expressions and voice tone using its camera and sensors, and uses an emotion engine to infer the user's current emotion. The input data includes audio data, text data, and emotion data.

[0858] Step 2:

[0859] The terminal sends the acquired voice data, text data, and sentiment data to the server. Encryption technology is used to ensure the security of this communication. The server receives this data and prepares it for use in the next processing step. The output is all the input data passed to the server.

[0860] Step 3:

[0861] The server converts the received audio data into digital waveform data using speech recognition. Specifically, a speech recognition algorithm analyzes the audio data and converts it into text data. Next, it analyzes the waveform data to extract the melody profile. The input is audio data, and the output is the melody profile.

[0862] Step 4:

[0863] The server analyzes text data using natural language processing techniques to extract emotions and themes. In doing so, it processes the text data using sentiment analysis algorithms to understand the user's intent and themes. The input is the text data of the lyrics, and the output is the emotional data and themes of the text.

[0864] Step 5:

[0865] The server integrates analyzed melody profiles, themes from text, and emotion data, and automatically generates music using music generation methods. A generation AI model is used, and this data is reflected in the melody and rhythm of the music. The input is the melody profile and integrated emotion / theme data, and the output is the generated music.

[0866] Step 6:

[0867] The server uses a video generation system to create a visual music video based on the generated music. This is achieved by a video generation AI model that constructs visual materials according to the emotional tone of the music. The input is the generated music, and the output is a music video.

[0868] Step 7:

[0869] The generated music and video are verified by moderation mechanisms on the server. Content moderation technology is used to ensure there is no inappropriate content or copyright issues. Once this is confirmed, the generated material proceeds to the next process. The output is verified content.

[0870] (Application Example 2)

[0871] 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".

[0872] Personalizing in-home entertainment experiences and providing music and video content tailored to users' emotions in real time has been difficult with conventional technologies. In particular, there is a need for an efficient system that can effectively recognize emotional states and dynamically generate content accordingly.

[0873] 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.

[0874] In this invention, the server includes a speech recognition means for converting input audio data into digital waveform data, an analysis means for extracting a melody profile from the converted digital waveform data, and a means for integrating music and video generated in response to the user's real-time emotions into the home system. This enables the real-time provision of personalized music and video that matches the user's emotions within the home.

[0875] "Speech recognition means" refers to a technology that processes input speech data into digital waveform data.

[0876] The "analysis method" is a technique for extracting a melody profile from the converted digital waveform data.

[0877] "Natural language processing means" refers to technologies that analyze input text data and extract emotions and themes.

[0878] "Music generation means" refers to technology that automatically generates music based on the results of analysis.

[0879] "Video generation means" refers to a technology that generates video based on a generated song to create a music video.

[0880] "Methods for integration into home systems" refers to technologies that integrate generated music and video into a system that provides them within the home in response to the user's real-time emotions.

[0881] A "machine learning model" is an algorithm that learns from empirical data during the analysis and generation process to perform predictions, classifications, and generation.

[0882] This invention describes a home entertainment system that generates and delivers music and video that reflects the user's emotions in real time. The main processes consist of input from the user's terminal, analysis and generation on a server, and delivery on the home system.

[0883] The user uses a terminal to input humming or text data and sends it to the server. The terminal is equipped with a microphone for speech recognition and a camera for real-time emotion detection. The voice data is converted into digital waveform data by a speech recognition system. The data is analyzed to extract a melody profile, and the emotion and theme are analyzed by a natural language processing system.

[0884] The server automatically generates music using music generation equipment based on the analyzed data, and then creates a music video using video generation equipment. This process utilizes machine learning models based on empirical data to refine the generation process.

[0885] The home system provides music and visuals generated in response to the user's real-time emotions. For example, if a user feels refreshed and hums a cheerful tune, the system will generate music with a refreshing melody and visuals of a sunny park as the background, and immediately display them on the screen.

[0886] Here is an example of a prompt message for this invention: "Take the humming the user sings in a refreshed mood as input, and generate content that combines an upbeat melody with a visual of a park on a sunny day."

[0887] This system allows users to easily enjoy personalized entertainment tailored to their emotional state.

[0888] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0889] Step 1:

[0890] The user records humming using the device and inputs the lyrics as text data. The input audio data is captured via the device's microphone, and the text data is provided by the user through the device's interface. The device sends this data to the server for the next processing step.

[0891] Step 2:

[0892] The server converts the received audio data into digital waveform data using speech recognition. This conversion process extracts audio patterns from the recorded humming and converts them into an analyzable digital format. The output data is digital waveform data ready for extracting a melody profile.

[0893] Step 3:

[0894] The server extracts a melody profile from the digital waveform data converted using an analysis tool. Here, the melody phrases and rhythms are identified. The extracted melody profile becomes the input for the next music generation process.

[0895] Step 4:

[0896] The server uses natural language processing to analyze the received text data and extract emotions and themes. This process identifies emotional expressions and keywords contained within the text and extracts elements that influence the overall theme of the song. The extracted emotional data and theme information are then supplied to the music generation process.

[0897] Step 5:

[0898] The server uses music generation tools to automatically generate music by combining analyzed melody profiles and emotion data. During the generation process, the server adjusts musical attributes such as melody and rhythm according to the emotion elements. The generated music is then used in the next video generation step.

[0899] Step 6:

[0900] The server uses video generation tools to create visuals that match the emotional tone and theme of the generated song, completing the music video. In this process, the server selects relevant video footage and seamlessly integrates it with the song for a visually appealing presentation. The output is a custom video for the user's entertainment experience.

[0901] Step 7:

[0902] Based on emotion prediction data transmitted from the device, the home system proposes generated music and video that matches the user's real-time emotions. The home system then instantly delivers the content via a display and sound system, allowing the user to enjoy it.

[0903] 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.

[0904] 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.

[0905] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0906] 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.

[0907] 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.

[0908] 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.

[0909] 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.

[0910] 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.

[0911] 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."

[0912] 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.

[0913] 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.

[0914] 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.

[0915] 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.

[0916] 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.

[0917] 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.

[0918] 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.

[0919] 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.

[0920] 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.

[0921] 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.

[0922] 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.

[0923] 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.

[0924] The following is further disclosed regarding the embodiments described above.

[0925] (Claim 1)

[0926] A speech recognition means that converts input audio data into digital waveform data,

[0927] An analysis means for extracting a melody profile from converted digital waveform data,

[0928] A natural language processing method that analyzes input text data and extracts emotions and themes,

[0929] A music generation method that automatically generates music based on the analyzed results,

[0930] A video generation method that generates video based on the generated music to create a music video,

[0931] Moderation measures to check for inappropriate content and copyright infringement in the generated content,

[0932] A system that includes this.

[0933] (Claim 2)

[0934] The system according to claim 1, comprising means for automatically uploading the generated music video and song to an online platform.

[0935] (Claim 3)

[0936] The system according to claim 1, comprising means of using a machine learning model in the analysis and generation process.

[0937] "Example 1"

[0938] (Claim 1)

[0939] A speech recognition means that converts input audio information into digital waveform information,

[0940] An analysis means for extracting melodic characteristics from converted digital waveform information,

[0941] A natural language processing means that analyzes input text information and extracts emotions and themes,

[0942] A music generation method that automatically generates musical works based on the results of analysis,

[0943] A video generation method that generates video based on a generated musical work to create a music video work,

[0944] A content review mechanism for checking for inappropriate content and copyright infringement in the generated information,

[0945] A system that includes this.

[0946] (Claim 2)

[0947] The system according to claim 1, comprising means for automatically uploading generated music video works and music works to a network infrastructure.

[0948] (Claim 3)

[0949] The system according to claim 1, comprising means of using a machine learning model in the analysis and generation process.

[0950] "Application Example 1"

[0951] (Claim 1)

[0952] An acoustic processing means that converts input audio information into digital waveform information,

[0953] An analysis means for extracting a melody profile from converted digital waveform information,

[0954] A natural language processing means that analyzes input text information and extracts emotions and themes,

[0955] A music generation method that automatically generates music based on the analyzed results,

[0956] A video generation method that generates video based on generated music and creates visual media,

[0957] A means for automatically transmitting the generated visual media and music to an online information dissemination platform,

[0958] Moderation measures to identify inappropriate content and copyright infringement in the generated information,

[0959] A system that includes this.

[0960] (Claim 2)

[0961] The system according to claim 1, which uses a machine learning model in the analysis and generation process.

[0962] (Claim 3)

[0963] The system according to claim 1, which automatically generates music and videos with a nostalgic atmosphere from user input voice and text.

[0964] "Example 2 of combining an emotion engine"

[0965] (Claim 1)

[0966] A speech recognition means that converts input audio information into digital waveform data,

[0967] An analysis means for extracting melody data from converted digital waveform data,

[0968] A natural language processing method that analyzes input text data and extracts emotions and themes,

[0969] A music generation method that automatically generates music based on the analyzed results,

[0970] A video generation method that generates visual data based on the generated music and creates a music video,

[0971] An emotion measurement means that measures the user's emotional state and integrates that data within the system,

[0972] Moderation measures to check for inappropriate content and copyright infringement in the generated content,

[0973] A system that includes this.

[0974] (Claim 2)

[0975] The system according to claim 1, comprising means for automatically uploading generated music videos and music to a digital platform.

[0976] (Claim 3)

[0977] The system according to claim 1, comprising means for using a learning model in the analysis and generation process.

[0978] "Application example 2 when combining with an emotional engine"

[0979] (Claim 1)

[0980] A speech recognition means that converts input audio data into digital waveform data,

[0981] An analysis means for extracting a melody profile from converted digital waveform data,

[0982] A natural language processing method that analyzes input text data and extracts emotions and themes,

[0983] A music generation method that automatically generates music based on the analyzed results,

[0984] A video generation method that generates video based on the generated music to create a music video,

[0985] A means of integrating the generated music and video into a home system that provides them in response to the user's real-time emotions,

[0986] A system that includes this.

[0987] (Claim 2)

[0988] The system according to claim 1, comprising means for automatically uploading the generated music video and song to an online platform.

[0989] (Claim 3)

[0990] The system according to claim 1, which uses a machine learning model in the analysis and generation process. [Explanation of symbols]

[0991] 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 speech recognition means that converts input audio data into digital waveform data, An analysis means for extracting a melody profile from converted digital waveform data, A natural language processing method that analyzes input text data and extracts emotions and themes, A music generation method that automatically generates music based on the analyzed results, A video generation method that generates video based on the generated music to create a music video, Moderation measures to check for inappropriate content and copyright infringement in the generated content, A system that includes this.

2. The system according to claim 1, comprising means for automatically uploading the generated music video and song to an online platform.

3. The system according to claim 1, comprising means for using a machine learning model in the analysis and generation process.

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