system

Abstract

The system according to this embodiment aims to generate and provide commercially usable, copyright-free music based on themes and moods entered by the user. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives user input. The analysis unit analyzes the information received by the reception unit. The generation unit generates music based on the information analyzed by the analysis unit. The provision unit provides the music generated by the generation unit.

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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to generate music based on the theme or mood input by the user, and it is difficult to easily obtain commercially available copyright-free music.

[0005] The system according to the embodiment aims to generate and provide commercially available copyright-free music based on the theme or mood input by the user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives user input. The analysis unit analyzes the information received by the reception unit. The generation unit generates music based on the information analyzed by the analysis unit. The provision unit provides the music generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can generate and provide commercially usable, copyright-free music based on themes and moods entered by the user. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] 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.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An online service according to an embodiment of the present invention is a system that generates copyright-free music using AI. This system allows users to input parameters such as theme, mood, and instrument type, and the AI ​​analyzes these parameters to generate original music. The generated music is commercially usable and free from copyright concerns. This service contributes to reducing music production costs and shortening production time, and can provide high-quality music for creative projects. For example, a user inputs parameters such as theme, mood, and instrument type. The user can specify a particular theme, mood, and the types of instruments they wish to use. For example, "a song with a cheerful theme, using piano and guitar." Next, the AI ​​analyzes the input parameters. The AI ​​analyzes the user's input by combining natural language processing and music theory-based algorithms. For example, it determines the tempo and key of the song based on the theme and mood, and selects the timbre of the instruments to be used based on the instrument type. Based on the analyzed information, the AI ​​generates original music. The generated music is commercially usable and free from copyright concerns. Users can download the generated music and use it in commercial projects. This service contributes to reducing music production costs and shortening production time. For example, traditional music production requires hiring composers and performers, which is costly and time-consuming, but by using this service, these costs and time can be significantly reduced. Furthermore, it can provide high-quality music for creative projects. For example, it can quickly provide original music for projects such as advertisements, films, and games. This improves the quality of the project. As a result, the online service can generate and deliver music based on user input.

[0029] The online service according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception unit provides, for example, an interface for receiving text input. The reception unit may also be equipped with a microphone and speech recognition technology for receiving voice input. Furthermore, the reception unit may be equipped with a camera and image analysis technology for receiving image input. For example, the reception unit receives text data entered by the user. The reception unit can also receive voice data spoken by the user with a microphone and convert it into text data using speech recognition technology. Furthermore, the reception unit can also receive image data captured by the user with a camera and analyze it using image analysis technology. The analysis unit analyzes the information received by the reception unit. The analysis is performed by, for example, natural language processing, data mining, and machine learning algorithms, but is not limited to these methods. For example, the analysis unit analyzes user input using natural language processing technology. The analysis unit can also extract useful information from user input data using data mining technology. Furthermore, the analysis unit can analyze user input data using machine learning algorithms. For example, the analysis unit can analyze user input text using morphological analysis. The analysis unit can also classify user input data using clustering algorithms. Furthermore, the analysis unit can analyze user input data using deep learning algorithms. The generation unit generates music based on the information analyzed by the analysis unit. Music generation is performed by methods such as generation based on music theory or automatic generation by AI, but is not limited to these examples. For example, the generation unit generates music based on music theory. The generation unit can also automatically generate music using AI. Furthermore, the generation unit can customize music based on user input. For example, the generation unit generates music based on chord progressions. The generation unit can also generate music using melody generation algorithms. Furthermore, the generation unit can customize music based on user preferences.The providing unit provides music generated by the generating unit. The provision is carried out by methods such as streaming, downloading, and real-time playback, but is not limited to these examples. For example, the providing unit provides the generated music in streaming format. The providing unit can also provide the generated music in a downloadable format. Furthermore, the providing unit can play the generated music in real time. For example, the providing unit makes the generated music downloadable in MP3 format. The providing unit can also provide the generated music in WAV format. Furthermore, the providing unit can stream the generated music in real time. Thus, the online service according to this embodiment can generate and provide music based on user input.

[0030] The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception unit provides, for example, an interface for receiving text input. Specifically, this could be a text box that operates on a web browser or an input field in a mobile application. This allows the user to easily enter text. The reception unit may also be equipped with a microphone and speech recognition technology for receiving voice input. In the case of voice input, the microphone receives what the user says and converts it into text data using speech recognition technology. Speech recognition technology includes a process of extracting features of the voice and converting them into text using a language model. Furthermore, the reception unit may also be equipped with a camera and image analysis technology for receiving image input. In the case of image input, the camera receives image data captured by the user and analyzes it using image analysis technology. Image analysis technology includes a process of extracting features of the image and performing object recognition or scene analysis. For example, the reception unit receives text data entered by the user. The reception unit can also receive voice data spoken by the user via a microphone and convert it into text data using speech recognition technology. Furthermore, the reception unit can receive image data captured by users via a camera and analyze it using image analysis technology. This allows the reception unit to support a variety of input formats, improving user convenience.

[0031] The analysis unit analyzes the information received by the reception unit. Analysis is performed using methods such as natural language processing, data mining, and machine learning algorithms, but is not limited to these examples. Specifically, it analyzes user input using natural language processing techniques. Natural language processing includes processes such as morphological analysis, syntactic analysis, and semantic analysis. Morphological analysis divides text into words and identifies the part of speech of each word. Syntactic analysis analyzes the structure of a sentence and clarifies relationships such as subject, predicate, and object. Semantic analysis understands the meaning of a sentence and provides an appropriate interpretation based on the context. The analysis unit can also extract useful information from user input data using data mining techniques. Data mining includes methods such as clustering, association analysis, and anomaly detection. Clustering groups similar data and discovers patterns. Association analysis finds relationships between data and identifies frequently occurring patterns. Anomaly detection detects unusual data and identifies abnormal patterns. Furthermore, the analysis unit can also analyze user input data using machine learning algorithms. Machine learning includes techniques such as supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, a model is trained using labeled data and then used to make predictions on new data. In unsupervised learning, data structure is learned using unlabeled data and patterns are discovered. In reinforcement learning, an agent learns optimal actions through interaction with the environment. For example, the analysis unit can analyze user input text using morphological analysis. The analysis unit can also classify user input data using clustering algorithms. Furthermore, the analysis unit can analyze user input data using deep learning algorithms. Deep learning includes convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models. This allows the analysis unit to utilize a variety of analysis methods to analyze user input data in detail and extract useful information.

[0032] The generation unit generates music based on the information analyzed by the analysis unit. Music generation is performed by methods such as generation based on music theory or automatic generation by AI, but is not limited to these examples. Specifically, music is generated based on music theory. Music theory includes elements such as chord progressions, melody construction, and rhythm patterns. In chord progressions, the framework of the music is created based on specific chord progressions. In melody construction, a melody line is created in accordance with the chord progression. In rhythm patterns, the tempo and beat of the music are set and a rhythm section is constructed. The generation unit can also automatically generate music using AI. Generative AI and deep learning algorithms are used for AI-based music generation. Generative AI is a model that generates new music based on training data, learning from past music data to generate new melodies and chord progressions. Deep learning algorithms include generative adversarial networks (GANs) and variational autoencoders (VAEs). GANs are a method that generates realistic music by having a generative model and a discriminative model compete, while VAEs are a method that generates new data by learning the latent space of data. Furthermore, the generation unit can also customize music based on user input. For example, if a user specifies a particular genre or mood, the system will generate music to meet those requirements. By customizing music based on user preferences, a more personalized musical experience can be provided. For instance, the generation unit can generate music based on chord progressions. It can also generate music using melody generation algorithms. Furthermore, the generation unit can customize music based on user preferences. This allows the generation unit to generate music in a variety of ways and provide music that meets the user's needs.

[0033] The provider provides music generated by the generator. This provision can be, but is not limited to, methods such as streaming, downloading, and real-time playback. Specifically, the provider provides generated music in streaming format. Streaming is a method of playing music in real time over the internet, allowing users to listen instantly without downloading. The provider can also provide generated music in downloadable format. Downloadable music allows users to save the music file to their device and play it offline. The provider can provide music in various file formats such as MP3, WAV, and FLAC. Furthermore, the provider can also play generated music in real time. Real-time playback allows users to play music immediately after generating it, enabling them to check the quality and content of the generated music. For example, the provider can make generated music downloadable in MP3 format. The provider can also provide generated music in WAV format. Furthermore, the provider can stream generated music in real time. This allows the provider to provide music to users in diverse ways, improving convenience. Additionally, the provider can collect user feedback and use it to improve the delivery method. For example, a feedback function could be provided that allows users to express their opinions on the quality and delivery methods of the music. This would enable the service provider to implement flexible delivery methods tailored to user needs and improve the quality of the service.

[0034] The generation unit includes a guarantee unit that ensures the generated music is available for commercial use. For example, the generation unit includes an algorithm for verifying that the generated music is available for commercial use. For example, the generation unit verifies that the generated music is copyright-free. The generation unit can also verify that the generated music meets quality standards. Furthermore, the generation unit may include procedures for ensuring that the generated music is suitable for commercial use. For example, the generation unit uses a music generation algorithm to verify that the generated music is copyright-free. The generation unit may also use a quality evaluation algorithm to verify that the generated music meets quality standards. Furthermore, the generation unit may include commercial use permission procedures to ensure that the generated music is suitable for commercial use. This allows the generation unit to guarantee that the generated music is available for commercial use.

[0035] The generation unit includes a customization unit that customizes the music based on user input. For example, the generation unit can adjust the tempo and key of the music based on user input. For instance, it can adjust the tempo based on a theme or mood entered by the user. It can also adjust the key of the music based on the type of instrument specified by the user. Furthermore, the generation unit can change the arrangement of the music based on the user's preferences. For example, it can speed up the tempo based on a theme entered by the user. It can also change the key of the music based on the type of instrument specified by the user. Furthermore, the generation unit can change the arrangement of the music based on the user's preferences. In this way, the generation unit can customize the music based on user input.

[0036] The analysis unit analyzes user input by combining natural language processing and music theory-based algorithms. For example, the analysis unit analyzes user input text using natural language processing techniques. For example, the analysis unit analyzes user input text using morphological analysis. Furthermore, the analysis unit can also analyze user input data using music theory-based algorithms. For example, the analysis unit analyzes user input data using chord progression algorithms. In addition, the analysis unit can analyze user input by combining natural language processing techniques and music theory-based algorithms. For example, the analysis unit analyzes user input text using natural language processing techniques and inputs the results into a music theory-based algorithm. This allows the analysis unit to analyze user input by combining natural language processing and music theory-based algorithms.

[0037] The service provider will provide the generated music in a downloadable format. For example, the service provider can make the generated music available for download in MP3 format. Alternatively, the service provider can also provide the generated music in WAV format. Furthermore, the service provider can also provide the generated music in FLAC format. In this way, the service provider can provide the generated music in a downloadable format.

[0038] The reception desk analyzes the user's past input history and suggests the optimal input method. For example, the reception desk automatically displays themes, moods, and instrument types that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest themes, moods, and instrument types to be used during specific time periods based on the user's past input history. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest themes, moods, and instrument types to be used during specific time periods based on the user's past input history. This allows the reception desk to analyze the user's past input history and suggest the optimal input method. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0039] The reception desk customizes input fields based on the user's current projects and areas of interest during input. For example, if the user is working on an advertising project, the reception desk suggests themes, moods, and instrument types suitable for advertising. The reception desk can also suggest themes, moods, and instrument types that match movie scenes if the user is working on a film project. Furthermore, if the user is working on a game project, the reception desk can suggest themes, moods, and instrument types that are appropriate for the game genre. This allows the reception desk to customize input fields based on the user's current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not.

[0040] The reception desk prioritizes displaying the most relevant input fields, taking into account the user's geographical location during input. For example, if the user is in a specific region, the reception desk suggests themes, moods, and instrument types related to the region's music style. The reception desk can also suggest themes, moods, and instrument types related to the culture and music style of the destination if the user is traveling. Furthermore, if the user is attending a specific event, the reception desk can suggest themes, moods, and instrument types related to that event. This allows the reception desk to prioritize displaying the most relevant input fields, taking into account the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0041] The reception desk analyzes the user's social media activity during input and suggests relevant input fields. For example, the reception desk suggests relevant themes, moods, and instrument types based on the content the user has shared on social media. The reception desk can also suggest relevant themes, moods, and instrument types based on the artists and bands the user follows. Furthermore, the reception desk can also suggest relevant themes, moods, and instrument types based on the online communities the user participates in. For example, the reception desk suggests relevant themes, moods, and instrument types based on the artists and bands the user follows. Furthermore, the reception desk can also suggest relevant themes, moods, and instrument types based on the online communities the user participates in. This allows the reception desk to analyze the user's social media activity and suggest relevant input fields. Some or all of the above processing in the reception desk may be performed using AI, for example, or not.

[0042] The analysis unit improves analysis accuracy by referring to the user's past input data during analysis. For example, the analysis unit improves analysis accuracy by referring to themes, moods, and instrument types previously entered by the user. For example, the analysis unit improves analysis accuracy by referring to themes, moods, and instrument types previously entered by the user. The analysis unit can also extract specific patterns from the user's past input data and reflect them in the analysis algorithm. Furthermore, the analysis unit can select the optimal analysis method based on the user's past input data. For example, the analysis unit can extract specific patterns from the user's past input data and reflect them in the analysis algorithm. Furthermore, the analysis unit can select the optimal analysis method based on the user's past input data. In this way, the analysis unit can improve analysis accuracy by referring to the user's past input data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0043] The analysis unit applies different analysis methods depending on the user's input category during analysis. For example, if the user is working on an advertising project, the analysis unit applies an analysis method suitable for advertising. For example, if the user is working on an advertising project, the analysis unit applies an analysis method suitable for advertising. The analysis unit can also apply an analysis method tailored to movie scenes if the user is working on a movie project. Furthermore, if the user is working on a game project, the analysis unit can also apply an analysis method suitable for the game genre. For example, if the user is working on a movie project, the analysis unit applies an analysis method tailored to movie scenes. Furthermore, if the user is working on a game project, the analysis unit can also apply an analysis method suitable for the game genre. This allows the analysis unit to apply different analysis methods depending on the user's input category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0044] The analysis unit improves analysis accuracy by considering the user's geographical location information during analysis. For example, if the user is in a specific region, the analysis unit applies analysis methods related to the music style of that region. For example, if the user is in a specific region, the analysis unit applies analysis methods related to the music style of that region. The analysis unit can also apply analysis methods related to the culture and music style of the destination if the user is traveling. Furthermore, if the user is participating in a specific event, the analysis unit can also apply analysis methods related to that event. For example, if the analysis unit is traveling, the analysis unit applies analysis methods related to the culture and music style of the destination. The analysis unit can also apply analysis methods related to the event if the user is participating in a specific event. This allows the analysis unit to improve analysis accuracy by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0045] The analysis unit improves analysis accuracy by referring to the user's social media activity during analysis. For example, the analysis unit applies relevant analysis methods based on the content the user has shared on social media. The analysis unit can also apply relevant analysis methods based on the artists and bands the user follows. Furthermore, the analysis unit can also apply relevant analysis methods based on the online communities the user participates in. For example, the analysis unit applies relevant analysis methods based on the artists and bands the user follows. Furthermore, the analysis unit can also apply relevant analysis methods based on the online communities the user participates in. This allows the analysis unit to improve analysis accuracy by referring to the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI.

[0046] The generation unit improves generation accuracy by referring to the user's past song generation history during generation. For example, the generation unit improves generation accuracy by referring to the themes, moods, and instrument types of songs previously generated by the user. For example, the generation unit improves generation accuracy by referring to the themes, moods, and instrument types of songs previously generated by the user. The generation unit can also extract specific patterns from the user's past song generation history and reflect them in the song generation algorithm. Furthermore, the generation unit can select the optimal generation method based on the user's past song generation history. For example, the generation unit can extract specific patterns from the user's past song generation history and reflect them in the song generation algorithm. Furthermore, the generation unit can select the optimal generation method based on the user's past song generation history. In this way, the generation unit can improve generation accuracy by referring to the user's past song generation history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0047] The generation unit applies different generation methods depending on the user's input category during generation. For example, if the user is working on an advertising project, the generation unit applies a generation method suitable for advertising. For example, if the user is working on an advertising project, the generation unit applies a generation method suitable for advertising. The generation unit can also apply a generation method suited to movie scenes if the user is working on a movie project. Furthermore, if the user is working on a game project, the generation unit can also apply a generation method suited to the game genre. For example, if the user is working on a movie project, the generation unit applies a generation method suited to movie scenes. Furthermore, if the user is working on a game project, the generation unit can also apply a generation method suited to the game genre. This allows the generation unit to apply different generation methods depending on the user's input category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0048] The generation unit improves generation accuracy by considering the user's geographical location information during generation. For example, if the user is in a specific region, the generation unit applies a generation method related to the music style of that region. For example, if the user is in a specific region, the generation unit applies a generation method related to the music style of that region. The generation unit can also apply a generation method related to the culture and music style of the destination if the user is traveling. Furthermore, if the user is participating in a specific event, the generation unit can also apply a generation method related to that event. For example, if the user is traveling, the generation unit applies a generation method related to the culture and music style of the destination. The generation unit can also apply a generation method related to the event if the user is participating in a specific event. This allows the generation unit to improve generation accuracy by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0049] The generation unit improves generation accuracy by referencing the user's social media activity during generation. For example, the generation unit applies relevant generation techniques based on content shared by the user on social media. The generation unit can also apply relevant generation techniques based on artists and bands followed by the user. Furthermore, the generation unit can also apply relevant generation techniques based on online communities in which the user participates. For example, the generation unit applies relevant generation techniques based on artists and bands followed by the user. Furthermore, the generation unit can also apply relevant generation techniques based on online communities in which the user participates. This allows the generation unit to improve generation accuracy by referencing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0050] The delivery unit selects the optimal delivery method by referring to the user's past download history at the time of delivery. For example, the delivery unit selects the optimal delivery method by referring to the theme, mood, and instrument type of music the user has downloaded in the past. For example, the delivery unit selects the optimal delivery method by referring to the theme, mood, and instrument type of music the user has downloaded in the past. The delivery unit can also extract specific patterns from the user's past download history and reflect them in the delivery method. Furthermore, the delivery unit can select the optimal delivery method based on the user's past download history. For example, the delivery unit can extract specific patterns from the user's past download history and reflect them in the delivery method. Furthermore, the delivery unit can select the optimal delivery method based on the user's past download history. In this way, the delivery unit can select the optimal delivery method by referring to the user's past download history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI.

[0051] The delivery unit customizes the delivery format based on the user's current project at the time of delivery. For example, if the user is working on an advertising project, the delivery unit will suggest a delivery format suitable for advertising. For example, if the user is working on an advertising project, the delivery unit will suggest a delivery format suitable for advertising. The delivery unit can also suggest a delivery format that matches movie scenes if the user is working on a film project. Furthermore, if the user is working on a game project, the delivery unit can also suggest a delivery format that matches the game genre. For example, if the delivery unit is working on a film project, the delivery unit will suggest a delivery format that matches movie scenes. Furthermore, if the user is working on a game project, the delivery unit can also suggest a delivery format that matches the game genre. This allows the delivery unit to customize the delivery format based on the user's current project. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI.

[0052] The service provider selects the optimal service delivery method at the time of delivery, taking into account the user's geographical location. For example, if the user is in a specific region, the service provider may suggest a service delivery method related to the music style of that region. The service provider may also suggest a service delivery method related to the culture and music style of the destination if the user is traveling. Furthermore, if the user is participating in a specific event, the service provider may also suggest a service delivery method related to that event. For example, if the service provider is traveling, the service provider may suggest a service delivery method related to the culture and music style of the destination if the user is traveling. The service provider may also suggest a service delivery method related to the event if the user is participating in a specific event. This allows the service provider to select the optimal service delivery method, taking into account the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI.

[0053] The delivery unit analyzes the user's social media activity and proposes a delivery method at the time of delivery. For example, the delivery unit proposes a relevant delivery method based on the content the user has shared on social media. For example, the delivery unit proposes a relevant delivery method based on the content the user has shared on social media. The delivery unit can also propose a relevant delivery method based on the artists and bands the user follows. Furthermore, the delivery unit can also propose a relevant delivery method based on the online communities the user participates in. For example, the delivery unit proposes a relevant delivery method based on the artists and bands the user follows. Furthermore, the delivery unit can also propose a relevant delivery method based on the online communities the user participates in. This allows the delivery unit to analyze the user's social media activity and propose a delivery method. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI.

[0054] The warranty unit improves the accuracy of its warranty by referring to the user's past usage history. For example, the warranty unit improves the accuracy of its warranty by referring to the themes, moods, and instrument types of songs the user has used in the past. For example, the warranty unit improves the accuracy of its warranty by referring to the themes, moods, and instrument types of songs the user has used in the past. The warranty unit can also extract specific patterns from the user's past usage history and reflect them in the warranty content. Furthermore, the warranty unit can select the optimal warranty method based on the user's past usage history. For example, the warranty unit can extract specific patterns from the user's past usage history and reflect them in the warranty content. Furthermore, the warranty unit can select the optimal warranty method based on the user's past usage history. In this way, the warranty unit can improve the accuracy of its warranty by referring to the user's past usage history. Some or all of the above processing in the warranty unit may be performed using AI, for example, or without using AI.

[0055] The warranty department customizes the warranty content when providing a warranty, taking into account the user's geographical location. For example, if the user is in a specific region, the warranty department customizes the warranty content based on the laws and regulations of that region. For example, if the user is in a specific region, the warranty department customizes the warranty content based on the laws and regulations of that region. The warranty department can also customize the warranty content based on the laws and regulations of the destination if the user is traveling. Furthermore, if the user is participating in a specific event, the warranty department can provide warranty content related to that event. For example, if the warranty department is traveling, the warranty department customizes the warranty content based on the laws and regulations of the destination. The warranty department can also provide warranty content related to the event if the user is participating in a specific event. This allows the warranty department to customize the warranty content taking into account the user's geographical location. Some or all of the above processing in the warranty department may be performed using AI, for example, or not using AI.

[0056] The customization unit selects the optimal customization method by referring to the user's past customization history during the customization process. For example, the customization unit selects the optimal customization method by referring to the theme, mood, and instrument type of songs that the user has customized in the past. For example, the customization unit selects the optimal customization method by referring to the theme, mood, and instrument type of songs that the user has customized in the past. The customization unit can also extract specific patterns from the user's past customization history and reflect them in the customization method. Furthermore, the customization unit can select the optimal customization method based on the user's past customization history. For example, the customization unit extracts specific patterns from the user's past customization history and reflects them in the customization method. Furthermore, the customization unit can select the optimal customization method based on the user's past customization history. In this way, the customization unit can select the optimal customization method by referring to the user's past customization history. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without using AI.

[0057] The customization unit selects the optimal customization method when customizing, taking into account the user's geographical location. For example, if the user is in a specific region, the customization unit will suggest a customization method related to the music style of that region. For example, if the user is in a specific region, the customization unit will suggest a customization method related to the music style of that region. Furthermore, if the user is traveling, the customization unit can suggest a customization method related to the culture and music style of the destination. Furthermore, if the user is participating in a specific event, the customization unit can suggest a customization method related to that event. For example, if the customization unit is traveling, the customization unit will suggest a customization method related to the culture and music style of the destination. Furthermore, if the user is participating in a specific event, the customization unit can suggest a customization method related to that event. This allows the customization unit to select the optimal customization method, taking into account the user's geographical location. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The reception desk can refer to the user's past input history based on their input and suggest the most suitable input method. For example, the reception desk can automatically display themes, moods, and instrument types that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest themes, moods, and instrument types that the user will use during specific time periods based on their past input history. This allows the reception desk to analyze the user's past input history and suggest the most suitable input method.

[0060] The generation unit can include a customization unit that customizes the music based on user input. For example, it can adjust the tempo and key of the music based on user input. It can also adjust the tempo based on a theme or mood entered by the user. Furthermore, it can adjust the key of the music based on the type of instrument specified by the user. In addition, it can change the arrangement of the music based on the user's preferences. In this way, the generation unit can customize the music based on user input.

[0061] The service provider can provide the generated music in a downloadable format. For example, the generated music can be made available for download in MP3 format. It can also be provided in WAV format. Furthermore, it can be provided in FLAC format. This allows the service provider to provide the generated music in a downloadable format.

[0062] The analysis unit can improve its accuracy by referring to the user's past input data during analysis. For example, it can improve analysis accuracy by referring to themes, moods, and instrument types previously entered by the user. It can also extract specific patterns from the user's past input data and reflect them in the analysis algorithm. Furthermore, it can select the optimal analysis method based on the user's past input data. In this way, the analysis unit can improve its analysis accuracy by referring to the user's past input data.

[0063] The delivery unit can select the optimal delivery method by referring to the user's past download history at the time of delivery. For example, it can select the optimal delivery method by referring to the theme, mood, and instrument type of music the user has downloaded in the past. It can also extract specific patterns from the user's past download history and reflect them in the delivery method. Furthermore, it can select the optimal delivery method based on the user's past download history. In this way, the delivery unit can select the optimal delivery method by referring to the user's past download history.

[0064] The customization unit can select the optimal customization method by referring to the user's past customization history during the customization process. For example, it can select the optimal customization method by referring to the themes, moods, and instrument types of songs the user has customized in the past. It can also extract specific patterns from the user's past customization history and reflect them in the customization method. Furthermore, it can select the optimal customization technique based on the user's past customization history. In this way, the customization unit can select the optimal customization method by referring to the user's past customization history.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception unit receives user input. User input includes text input, voice input, and image input. The reception unit may be equipped with an interface for receiving text input, a microphone and voice recognition technology for receiving voice input, and a camera and image analysis technology for receiving image input. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using methods such as natural language processing, data mining, and machine learning algorithms. For example, the analysis unit analyzes the user's input data using natural language processing techniques, data mining techniques, and machine learning algorithms. Step 3: The generation unit generates music based on the information analyzed by the analysis unit. Music generation can be performed using methods such as generation based on music theory or automatic generation using AI. For example, the generation unit can generate music based on music theory or automatically generate music using AI. Step 4: The provider unit provides the music generated by the generator unit. The provision is carried out by methods such as streaming, downloading, and real-time playback. For example, the provider unit provides the generated music in streaming format, downloadable format, and real-time playback format.

[0067] (Example of form 2) An online service according to an embodiment of the present invention is a system that generates copyright-free music using AI. This system allows users to input parameters such as theme, mood, and instrument type, and the AI ​​analyzes these parameters to generate original music. The generated music is commercially usable and free from copyright concerns. This service contributes to reducing music production costs and shortening production time, and can provide high-quality music for creative projects. For example, a user inputs parameters such as theme, mood, and instrument type. The user can specify a particular theme, mood, and the types of instruments they wish to use. For example, "a song with a cheerful theme, using piano and guitar." Next, the AI ​​analyzes the input parameters. The AI ​​analyzes the user's input by combining natural language processing and music theory-based algorithms. For example, it determines the tempo and key of the song based on the theme and mood, and selects the timbre of the instruments to be used based on the instrument type. Based on the analyzed information, the AI ​​generates original music. The generated music is commercially usable and free from copyright concerns. Users can download the generated music and use it in commercial projects. This service contributes to reducing music production costs and shortening production time. For example, traditional music production requires hiring composers and performers, which is costly and time-consuming, but by using this service, these costs and time can be significantly reduced. Furthermore, it can provide high-quality music for creative projects. For example, it can quickly provide original music for projects such as advertisements, films, and games. This improves the quality of the project. As a result, the online service can generate and deliver music based on user input.

[0068] The online service according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception unit provides, for example, an interface for receiving text input. The reception unit may also be equipped with a microphone and speech recognition technology for receiving voice input. Furthermore, the reception unit may be equipped with a camera and image analysis technology for receiving image input. For example, the reception unit receives text data entered by the user. The reception unit can also receive voice data spoken by the user with a microphone and convert it into text data using speech recognition technology. Furthermore, the reception unit can also receive image data captured by the user with a camera and analyze it using image analysis technology. The analysis unit analyzes the information received by the reception unit. The analysis is performed by, for example, natural language processing, data mining, and machine learning algorithms, but is not limited to these methods. For example, the analysis unit analyzes user input using natural language processing technology. The analysis unit can also extract useful information from user input data using data mining technology. Furthermore, the analysis unit can analyze user input data using machine learning algorithms. For example, the analysis unit can analyze user input text using morphological analysis. The analysis unit can also classify user input data using clustering algorithms. Furthermore, the analysis unit can analyze user input data using deep learning algorithms. The generation unit generates music based on the information analyzed by the analysis unit. Music generation is performed by methods such as generation based on music theory or automatic generation by AI, but is not limited to these examples. For example, the generation unit generates music based on music theory. The generation unit can also automatically generate music using AI. Furthermore, the generation unit can customize music based on user input. For example, the generation unit generates music based on chord progressions. The generation unit can also generate music using melody generation algorithms. Furthermore, the generation unit can customize music based on user preferences.The providing unit provides music generated by the generating unit. The provision is carried out by methods such as streaming, downloading, and real-time playback, but is not limited to these examples. For example, the providing unit provides the generated music in streaming format. The providing unit can also provide the generated music in a downloadable format. Furthermore, the providing unit can play the generated music in real time. For example, the providing unit makes the generated music downloadable in MP3 format. The providing unit can also provide the generated music in WAV format. Furthermore, the providing unit can stream the generated music in real time. Thus, the online service according to this embodiment can generate and provide music based on user input.

[0069] The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception unit provides, for example, an interface for receiving text input. Specifically, this could be a text box that operates on a web browser or an input field in a mobile application. This allows the user to easily enter text. The reception unit may also be equipped with a microphone and speech recognition technology for receiving voice input. In the case of voice input, the microphone receives what the user says and converts it into text data using speech recognition technology. Speech recognition technology includes a process of extracting features of the voice and converting them into text using a language model. Furthermore, the reception unit may also be equipped with a camera and image analysis technology for receiving image input. In the case of image input, the camera receives image data captured by the user and analyzes it using image analysis technology. Image analysis technology includes a process of extracting features of the image and performing object recognition or scene analysis. For example, the reception unit receives text data entered by the user. The reception unit can also receive voice data spoken by the user via a microphone and convert it into text data using speech recognition technology. Furthermore, the reception unit can receive image data captured by users via a camera and analyze it using image analysis technology. This allows the reception unit to support a variety of input formats, improving user convenience.

[0070] The analysis unit analyzes the information received by the reception unit. Analysis is performed using methods such as natural language processing, data mining, and machine learning algorithms, but is not limited to these examples. Specifically, it analyzes user input using natural language processing techniques. Natural language processing includes processes such as morphological analysis, syntactic analysis, and semantic analysis. Morphological analysis divides text into words and identifies the part of speech of each word. Syntactic analysis analyzes the structure of a sentence and clarifies relationships such as subject, predicate, and object. Semantic analysis understands the meaning of a sentence and provides an appropriate interpretation based on the context. The analysis unit can also extract useful information from user input data using data mining techniques. Data mining includes methods such as clustering, association analysis, and anomaly detection. Clustering groups similar data and discovers patterns. Association analysis finds relationships between data and identifies frequently occurring patterns. Anomaly detection detects unusual data and identifies abnormal patterns. Furthermore, the analysis unit can also analyze user input data using machine learning algorithms. Machine learning includes techniques such as supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, a model is trained using labeled data and then used to make predictions on new data. In unsupervised learning, data structure is learned using unlabeled data and patterns are discovered. In reinforcement learning, an agent learns optimal actions through interaction with the environment. For example, the analysis unit can analyze user input text using morphological analysis. The analysis unit can also classify user input data using clustering algorithms. Furthermore, the analysis unit can analyze user input data using deep learning algorithms. Deep learning includes convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models. This allows the analysis unit to utilize a variety of analysis methods to analyze user input data in detail and extract useful information.

[0071] The generation unit generates music based on the information analyzed by the analysis unit. Music generation is performed by methods such as generation based on music theory or automatic generation by AI, but is not limited to these examples. Specifically, music is generated based on music theory. Music theory includes elements such as chord progressions, melody construction, and rhythm patterns. In chord progressions, the framework of the music is created based on specific chord progressions. In melody construction, a melody line is created in accordance with the chord progression. In rhythm patterns, the tempo and beat of the music are set and a rhythm section is constructed. The generation unit can also automatically generate music using AI. Generative AI and deep learning algorithms are used for AI-based music generation. Generative AI is a model that generates new music based on training data, learning from past music data to generate new melodies and chord progressions. Deep learning algorithms include generative adversarial networks (GANs) and variational autoencoders (VAEs). GANs are a method that generates realistic music by having a generative model and a discriminative model compete, while VAEs are a method that generates new data by learning the latent space of data. Furthermore, the generation unit can also customize music based on user input. For example, if a user specifies a particular genre or mood, the system will generate music to meet those requirements. By customizing music based on user preferences, a more personalized musical experience can be provided. For instance, the generation unit can generate music based on chord progressions. It can also generate music using melody generation algorithms. Furthermore, the generation unit can customize music based on user preferences. This allows the generation unit to generate music in a variety of ways and provide music that meets the user's needs.

[0072] The provider provides music generated by the generator. This provision can be, but is not limited to, methods such as streaming, downloading, and real-time playback. Specifically, the provider provides generated music in streaming format. Streaming is a method of playing music in real time over the internet, allowing users to listen instantly without downloading. The provider can also provide generated music in downloadable format. Downloadable music allows users to save the music file to their device and play it offline. The provider can provide music in various file formats such as MP3, WAV, and FLAC. Furthermore, the provider can also play generated music in real time. Real-time playback allows users to play music immediately after generating it, enabling them to check the quality and content of the generated music. For example, the provider can make generated music downloadable in MP3 format. The provider can also provide generated music in WAV format. Furthermore, the provider can stream generated music in real time. This allows the provider to provide music to users in diverse ways, improving convenience. Additionally, the provider can collect user feedback and use it to improve the delivery method. For example, a feedback function could be provided that allows users to express their opinions on the quality and delivery methods of the music. This would enable the service provider to implement flexible delivery methods tailored to user needs and improve the quality of the service.

[0073] The generation unit includes a guarantee unit that ensures the generated music is available for commercial use. For example, the generation unit includes an algorithm for verifying that the generated music is available for commercial use. For example, the generation unit verifies that the generated music is copyright-free. The generation unit can also verify that the generated music meets quality standards. Furthermore, the generation unit may include procedures for ensuring that the generated music is suitable for commercial use. For example, the generation unit uses a music generation algorithm to verify that the generated music is copyright-free. The generation unit may also use a quality evaluation algorithm to verify that the generated music meets quality standards. Furthermore, the generation unit may include commercial use permission procedures to ensure that the generated music is suitable for commercial use. This allows the generation unit to guarantee that the generated music is available for commercial use.

[0074] The generation unit includes a customization unit that customizes the music based on user input. For example, the generation unit can adjust the tempo and key of the music based on user input. For instance, it can adjust the tempo based on a theme or mood entered by the user. It can also adjust the key of the music based on the type of instrument specified by the user. Furthermore, the generation unit can change the arrangement of the music based on the user's preferences. For example, it can speed up the tempo based on a theme entered by the user. It can also change the key of the music based on the type of instrument specified by the user. Furthermore, the generation unit can change the arrangement of the music based on the user's preferences. In this way, the generation unit can customize the music based on user input.

[0075] The analysis unit analyzes user input by combining natural language processing and music theory-based algorithms. For example, the analysis unit analyzes user input text using natural language processing techniques. For example, the analysis unit analyzes user input text using morphological analysis. Furthermore, the analysis unit can also analyze user input data using music theory-based algorithms. For example, the analysis unit analyzes user input data using chord progression algorithms. In addition, the analysis unit can analyze user input by combining natural language processing techniques and music theory-based algorithms. For example, the analysis unit analyzes user input text using natural language processing techniques and inputs the results into a music theory-based algorithm. This allows the analysis unit to analyze user input by combining natural language processing and music theory-based algorithms.

[0076] The service provider will provide the generated music in a downloadable format. For example, the service provider can make the generated music available for download in MP3 format. Alternatively, the service provider can also provide the generated music in WAV format. Furthermore, the service provider can also provide the generated music in FLAC format. In this way, the service provider can provide the generated music in a downloadable format.

[0077] The reception desk estimates the user's emotions and adjusts the display of the input interface based on the estimated emotions. For example, if the user is stressed, the reception desk provides a simple interface and minimizes the input steps. For example, if the user is stressed, the reception desk provides a simple interface. Also, if the user is relaxed, the reception desk can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of themes, moods, and instrument types. For example, if the user is relaxed, the reception desk provides detailed input options. Also, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of themes, moods, and instrument types. This allows the reception desk to adjust the display of the input interface based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The reception desk analyzes the user's past input history and suggests the optimal input method. For example, the reception desk automatically displays themes, moods, and instrument types that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest themes, moods, and instrument types to be used during specific time periods based on the user's past input history. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest themes, moods, and instrument types to be used during specific time periods based on the user's past input history. This allows the reception desk to analyze the user's past input history and suggest the optimal input method. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0079] The reception desk customizes input fields based on the user's current projects and areas of interest during input. For example, if the user is working on an advertising project, the reception desk suggests themes, moods, and instrument types suitable for advertising. The reception desk can also suggest themes, moods, and instrument types that match movie scenes if the user is working on a film project. Furthermore, if the user is working on a game project, the reception desk can suggest themes, moods, and instrument types that are appropriate for the game genre. This allows the reception desk to customize input fields based on the user's current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not.

[0080] The reception desk estimates the user's emotions and prioritizes input fields based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize displaying the most important input fields and simplify the input process. For example, if the user is stressed, the reception desk will prioritize displaying the most important input fields. The reception desk can also provide detailed input fields and suggest customizable input methods if the user is relaxed. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of themes, moods, and instrument types. For example, if the user is relaxed, the reception desk will provide detailed input fields. The reception desk can also prioritize voice input to allow for quick input of themes, moods, and instrument types if the user is in a hurry. This allows the reception desk to prioritize input fields based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The reception desk prioritizes displaying the most relevant input fields, taking into account the user's geographical location during input. For example, if the user is in a specific region, the reception desk suggests themes, moods, and instrument types related to the region's music style. The reception desk can also suggest themes, moods, and instrument types related to the culture and music style of the destination if the user is traveling. Furthermore, if the user is attending a specific event, the reception desk can suggest themes, moods, and instrument types related to that event. This allows the reception desk to prioritize displaying the most relevant input fields, taking into account the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0082] The reception desk analyzes the user's social media activity during input and suggests relevant input fields. For example, the reception desk suggests relevant themes, moods, and instrument types based on the content the user has shared on social media. The reception desk can also suggest relevant themes, moods, and instrument types based on the artists and bands the user follows. Furthermore, the reception desk can also suggest relevant themes, moods, and instrument types based on the online communities the user participates in. For example, the reception desk suggests relevant themes, moods, and instrument types based on the artists and bands the user follows. Furthermore, the reception desk can also suggest relevant themes, moods, and instrument types based on the online communities the user participates in. This allows the reception desk to analyze the user's social media activity and suggest relevant input fields. Some or all of the above processing in the reception desk may be performed using AI, for example, or not.

[0083] The analysis unit estimates the user's emotions and adjusts the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit adjusts the analysis algorithm to generate music with a relaxed tempo. The analysis unit can also adjust the analysis algorithm to generate music with a fast tempo if the user is in a hurry. Furthermore, if the user is excited, the analysis unit can adjust the analysis algorithm to generate music with an energetic tempo. For example, if the user is in a hurry, the analysis unit adjusts the analysis algorithm to generate music with a fast tempo. Furthermore, if the user is excited, the analysis unit can also adjust the analysis algorithm to generate music with an energetic tempo. This allows the analysis unit to adjust the analysis algorithm based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The analysis unit improves analysis accuracy by referring to the user's past input data during analysis. For example, the analysis unit improves analysis accuracy by referring to themes, moods, and instrument types previously entered by the user. For example, the analysis unit improves analysis accuracy by referring to themes, moods, and instrument types previously entered by the user. The analysis unit can also extract specific patterns from the user's past input data and reflect them in the analysis algorithm. Furthermore, the analysis unit can select the optimal analysis method based on the user's past input data. For example, the analysis unit can extract specific patterns from the user's past input data and reflect them in the analysis algorithm. Furthermore, the analysis unit can select the optimal analysis method based on the user's past input data. In this way, the analysis unit can improve analysis accuracy by referring to the user's past input data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0085] The analysis unit applies different analysis methods depending on the user's input category during analysis. For example, if the user is working on an advertising project, the analysis unit applies an analysis method suitable for advertising. For example, if the user is working on an advertising project, the analysis unit applies an analysis method suitable for advertising. The analysis unit can also apply an analysis method tailored to movie scenes if the user is working on a movie project. Furthermore, if the user is working on a game project, the analysis unit can also apply an analysis method suitable for the game genre. For example, if the user is working on a movie project, the analysis unit applies an analysis method tailored to movie scenes. Furthermore, if the user is working on a game project, the analysis unit can also apply an analysis method suitable for the game genre. This allows the analysis unit to apply different analysis methods depending on the user's input category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0086] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple and easy-to-read display method. For example, if the user is nervous, the analysis unit provides a simple and easy-to-read display method. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. In this way, the analysis unit can adjust the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The analysis unit improves analysis accuracy by considering the user's geographical location information during analysis. For example, if the user is in a specific region, the analysis unit applies analysis methods related to the music style of that region. For example, if the user is in a specific region, the analysis unit applies analysis methods related to the music style of that region. The analysis unit can also apply analysis methods related to the culture and music style of the destination if the user is traveling. Furthermore, if the user is participating in a specific event, the analysis unit can also apply analysis methods related to that event. For example, if the analysis unit is traveling, the analysis unit applies analysis methods related to the culture and music style of the destination. The analysis unit can also apply analysis methods related to the event if the user is participating in a specific event. This allows the analysis unit to improve analysis accuracy by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0088] The analysis unit improves analysis accuracy by referring to the user's social media activity during analysis. For example, the analysis unit applies relevant analysis methods based on the content the user has shared on social media. The analysis unit can also apply relevant analysis methods based on the artists and bands the user follows. Furthermore, the analysis unit can also apply relevant analysis methods based on the online communities the user participates in. For example, the analysis unit applies relevant analysis methods based on the artists and bands the user follows. Furthermore, the analysis unit can also apply relevant analysis methods based on the online communities the user participates in. This allows the analysis unit to improve analysis accuracy by referring to the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI.

[0089] The generation unit estimates the user's emotions and adjusts the music generation algorithm based on the estimated emotions. For example, if the user is relaxed, the generation unit adjusts the music generation algorithm to produce music with a relaxed tempo. The generation unit can also adjust the music generation algorithm to produce music with a fast tempo if the user is in a hurry. Furthermore, if the user is excited, the generation unit can also adjust the music generation algorithm to produce energetic music. For example, if the user is in a hurry, the generation unit adjusts the music generation algorithm to produce music with a fast tempo. Furthermore, if the user is excited, the generation unit can also adjust the music generation algorithm to produce energetic music. In this way, the generation unit can adjust the music generation algorithm based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0090] The generation unit improves generation accuracy by referring to the user's past song generation history during generation. For example, the generation unit improves generation accuracy by referring to the themes, moods, and instrument types of songs previously generated by the user. For example, the generation unit improves generation accuracy by referring to the themes, moods, and instrument types of songs previously generated by the user. The generation unit can also extract specific patterns from the user's past song generation history and reflect them in the song generation algorithm. Furthermore, the generation unit can select the optimal generation method based on the user's past song generation history. For example, the generation unit can extract specific patterns from the user's past song generation history and reflect them in the song generation algorithm. Furthermore, the generation unit can select the optimal generation method based on the user's past song generation history. In this way, the generation unit can improve generation accuracy by referring to the user's past song generation history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0091] The generation unit applies different generation methods depending on the user's input category during generation. For example, if the user is working on an advertising project, the generation unit applies a generation method suitable for advertising. For example, if the user is working on an advertising project, the generation unit applies a generation method suitable for advertising. The generation unit can also apply a generation method suited to movie scenes if the user is working on a movie project. Furthermore, if the user is working on a game project, the generation unit can also apply a generation method suited to the game genre. For example, if the user is working on a movie project, the generation unit applies a generation method suited to movie scenes. Furthermore, if the user is working on a game project, the generation unit can also apply a generation method suited to the game genre. This allows the generation unit to apply different generation methods depending on the user's input category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0092] The generation unit estimates the user's emotions and adjusts how the generated music is displayed based on the estimated emotions. For example, if the user is tense, the generation unit provides a simple and easy-to-read display. The generation unit can also provide a display that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the generation unit can provide a display that gets straight to the point. For example, if the user is relaxed, the generation unit provides a display that includes detailed information. The generation unit can also provide a display that gets straight to the point if the user is in a hurry. This allows the generation unit to adjust how the generated music is displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The generation unit improves generation accuracy by considering the user's geographical location information during generation. For example, if the user is in a specific region, the generation unit applies a generation method related to the music style of that region. For example, if the user is in a specific region, the generation unit applies a generation method related to the music style of that region. The generation unit can also apply a generation method related to the culture and music style of the destination if the user is traveling. Furthermore, if the user is participating in a specific event, the generation unit can also apply a generation method related to that event. For example, if the user is traveling, the generation unit applies a generation method related to the culture and music style of the destination. The generation unit can also apply a generation method related to the event if the user is participating in a specific event. This allows the generation unit to improve generation accuracy by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0094] The generation unit improves generation accuracy by referencing the user's social media activity during generation. For example, the generation unit applies relevant generation techniques based on content shared by the user on social media. The generation unit can also apply relevant generation techniques based on artists and bands followed by the user. Furthermore, the generation unit can also apply relevant generation techniques based on online communities in which the user participates. For example, the generation unit applies relevant generation techniques based on artists and bands followed by the user. Furthermore, the generation unit can also apply relevant generation techniques based on online communities in which the user participates. This allows the generation unit to improve generation accuracy by referencing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0095] The service provider estimates the user's emotions and adjusts the method of delivering music based on the estimated emotions. For example, if the user is relaxed, the service provider will deliver music at a relaxed pace. The service provider can also deliver music quickly if the user is in a hurry. Furthermore, if the user is excited, the service provider can suggest a method of delivering music with visually stimulating effects. For example, if the user is in a hurry, the service provider will deliver music quickly. The service provider can also suggest a method of delivering music with visually stimulating effects if the user is excited. This allows the service provider to adjust the method of delivering music based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The delivery unit selects the optimal delivery method by referring to the user's past download history at the time of delivery. For example, the delivery unit selects the optimal delivery method by referring to the theme, mood, and instrument type of music the user has downloaded in the past. For example, the delivery unit selects the optimal delivery method by referring to the theme, mood, and instrument type of music the user has downloaded in the past. The delivery unit can also extract specific patterns from the user's past download history and reflect them in the delivery method. Furthermore, the delivery unit can select the optimal delivery method based on the user's past download history. For example, the delivery unit can extract specific patterns from the user's past download history and reflect them in the delivery method. Furthermore, the delivery unit can select the optimal delivery method based on the user's past download history. In this way, the delivery unit can select the optimal delivery method by referring to the user's past download history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI.

[0097] The delivery unit customizes the delivery format based on the user's current project at the time of delivery. For example, if the user is working on an advertising project, the delivery unit will suggest a delivery format suitable for advertising. For example, if the user is working on an advertising project, the delivery unit will suggest a delivery format suitable for advertising. The delivery unit can also suggest a delivery format that matches movie scenes if the user is working on a film project. Furthermore, if the user is working on a game project, the delivery unit can also suggest a delivery format that matches the game genre. For example, if the delivery unit is working on a film project, the delivery unit will suggest a delivery format that matches movie scenes. Furthermore, if the user is working on a game project, the delivery unit can also suggest a delivery format that matches the game genre. This allows the delivery unit to customize the delivery format based on the user's current project. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI.

[0098] The service provider estimates the user's emotions and determines the priority of the music to be provided based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize providing relaxing music. The service provider can also provide music containing detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the service provider can prioritize providing music that can be delivered quickly. This allows the service provider to determine the priority of the music to be provided based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The service provider selects the optimal service delivery method at the time of delivery, taking into account the user's geographical location. For example, if the user is in a specific region, the service provider may suggest a service delivery method related to the music style of that region. The service provider may also suggest a service delivery method related to the culture and music style of the destination if the user is traveling. Furthermore, if the user is participating in a specific event, the service provider may also suggest a service delivery method related to that event. For example, if the service provider is traveling, the service provider may suggest a service delivery method related to the culture and music style of the destination if the user is traveling. The service provider may also suggest a service delivery method related to the event if the user is participating in a specific event. This allows the service provider to select the optimal service delivery method, taking into account the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI.

[0100] The delivery unit analyzes the user's social media activity and proposes a delivery method at the time of delivery. For example, the delivery unit proposes a relevant delivery method based on the content the user has shared on social media. For example, the delivery unit proposes a relevant delivery method based on the content the user has shared on social media. The delivery unit can also propose a relevant delivery method based on the artists and bands the user follows. Furthermore, the delivery unit can also propose a relevant delivery method based on the online communities the user participates in. For example, the delivery unit proposes a relevant delivery method based on the artists and bands the user follows. Furthermore, the delivery unit can also propose a relevant delivery method based on the online communities the user participates in. This allows the delivery unit to analyze the user's social media activity and propose a delivery method. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI.

[0101] The assurance unit estimates the user's emotions and adjusts the assurance content based on the estimated user emotions. For example, if the user is feeling anxious, the assurance unit provides detailed assurance content to reassure them. For example, if the user is feeling anxious, the assurance unit provides detailed assurance content to reassure them. The assurance unit can also provide concise assurance content if the user is relaxed. Furthermore, if the user is in a hurry, the assurance unit can provide assurance content that can be quickly understood. For example, if the user is relaxed, the assurance unit provides concise assurance content. Furthermore, if the user is in a hurry, the assurance unit can also provide assurance content that can be quickly understood. In this way, the assurance unit can adjust the assurance content based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The warranty unit improves the accuracy of its warranty by referring to the user's past usage history. For example, the warranty unit improves the accuracy of its warranty by referring to the themes, moods, and instrument types of songs the user has used in the past. For example, the warranty unit improves the accuracy of its warranty by referring to the themes, moods, and instrument types of songs the user has used in the past. The warranty unit can also extract specific patterns from the user's past usage history and reflect them in the warranty content. Furthermore, the warranty unit can select the optimal warranty method based on the user's past usage history. For example, the warranty unit can extract specific patterns from the user's past usage history and reflect them in the warranty content. Furthermore, the warranty unit can select the optimal warranty method based on the user's past usage history. In this way, the warranty unit can improve the accuracy of its warranty by referring to the user's past usage history. Some or all of the above processing in the warranty unit may be performed using AI, for example, or without using AI.

[0103] The assurance unit estimates the user's emotions and determines the priority of assurances based on the estimated emotions. For example, if the user is feeling anxious, the assurance unit will prioritize providing the most important assurances. For example, if the user is feeling anxious, the assurance unit will prioritize providing the most important assurances. The assurance unit can also provide detailed assurances if the user is relaxed. Furthermore, if the user is in a hurry, the assurance unit can provide assurances that can be quickly understood. For example, if the user is relaxed, the assurance unit will provide detailed assurances. Furthermore, if the user is in a hurry, the assurance unit can also provide assurances that can be quickly understood. This allows the assurance unit to determine the priority of assurances based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The warranty department customizes the warranty content when providing a warranty, taking into account the user's geographical location. For example, if the user is in a specific region, the warranty department customizes the warranty content based on the laws and regulations of that region. For example, if the user is in a specific region, the warranty department customizes the warranty content based on the laws and regulations of that region. The warranty department can also customize the warranty content based on the laws and regulations of the destination if the user is traveling. Furthermore, if the user is participating in a specific event, the warranty department can provide warranty content related to that event. For example, if the warranty department is traveling, the warranty department customizes the warranty content based on the laws and regulations of the destination. The warranty department can also provide warranty content related to the event if the user is participating in a specific event. This allows the warranty department to customize the warranty content taking into account the user's geographical location. Some or all of the above processing in the warranty department may be performed using AI, for example, or not using AI.

[0105] The customization unit estimates the user's emotions and adjusts the customization method based on the estimated emotions. For example, if the user is relaxed, the customization unit provides detailed customization options. For example, if the user is relaxed, the customization unit provides detailed customization options. The customization unit can also provide concise customization options if the user is in a hurry. Furthermore, if the user is excited, the customization unit can also provide visually stimulating customization options. For example, if the user is in a hurry, the customization unit provides concise customization options. Furthermore, if the user is excited, the customization unit can also provide visually stimulating customization options. This allows the customization unit to adjust the customization method based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The customization unit selects the optimal customization method by referring to the user's past customization history during the customization process. For example, the customization unit selects the optimal customization method by referring to the theme, mood, and instrument type of songs that the user has customized in the past. For example, the customization unit selects the optimal customization method by referring to the theme, mood, and instrument type of songs that the user has customized in the past. The customization unit can also extract specific patterns from the user's past customization history and reflect them in the customization method. Furthermore, the customization unit can select the optimal customization method based on the user's past customization history. For example, the customization unit extracts specific patterns from the user's past customization history and reflects them in the customization method. Furthermore, the customization unit can select the optimal customization method based on the user's past customization history. In this way, the customization unit can select the optimal customization method by referring to the user's past customization history. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without using AI.

[0107] The customization unit estimates the user's emotions and determines customization priorities based on those emotions. For example, if the user is stressed, the customization unit will prioritize displaying the most important customization items and simplify the customization process. For example, if the user is stressed, the customization unit will prioritize displaying the most important customization items. Also, if the user is relaxed, the customization unit can provide detailed customization options and suggest customizable methods. Furthermore, if the user is in a hurry, the customization unit can prioritize displaying items that can be customized quickly. For example, if the user is relaxed, the customization unit will provide detailed customization options. Also, if the user is in a hurry, the customization unit can prioritize displaying items that can be customized quickly. This allows the customization unit to determine customization priorities based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The customization unit selects the optimal customization method when customizing, taking into account the user's geographical location. For example, if the user is in a specific region, the customization unit will suggest a customization method related to the music style of that region. For example, if the user is in a specific region, the customization unit will suggest a customization method related to the music style of that region. Furthermore, if the user is traveling, the customization unit can suggest a customization method related to the culture and music style of the destination. Furthermore, if the user is participating in a specific event, the customization unit can suggest a customization method related to that event. For example, if the customization unit is traveling, the customization unit will suggest a customization method related to the culture and music style of the destination. Furthermore, if the user is participating in a specific event, the customization unit can suggest a customization method related to that event. This allows the customization unit to select the optimal customization method, taking into account the user's geographical location. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The reception desk can refer to the user's past input history based on their input and suggest the most suitable input method. For example, the reception desk can automatically display themes, moods, and instrument types that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest themes, moods, and instrument types that the user will use during specific time periods based on their past input history. This allows the reception desk to analyze the user's past input history and suggest the most suitable input method.

[0111] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on those emotions. For example, if the user is relaxed, the analysis algorithm can be adjusted to generate a slow-tempo song. If the user is in a hurry, the analysis algorithm can be adjusted to generate a fast-tempo song. Furthermore, if the user is excited, the analysis algorithm can be adjusted to generate an energetic song. In this way, the analysis unit can adjust the analysis algorithm based on the user's emotions.

[0112] The generation unit can include a customization unit that customizes the music based on user input. For example, it can adjust the tempo and key of the music based on user input. It can also adjust the tempo based on a theme or mood entered by the user. Furthermore, it can adjust the key of the music based on the type of instrument specified by the user. In addition, it can change the arrangement of the music based on the user's preferences. In this way, the generation unit can customize the music based on user input.

[0113] The service provider can provide the generated music in a downloadable format. For example, the generated music can be made available for download in MP3 format. It can also be provided in WAV format. Furthermore, it can be provided in FLAC format. This allows the service provider to provide the generated music in a downloadable format.

[0114] The reception desk can estimate the user's emotions and adjust how the input interface is displayed based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick input of themes, moods, and instrument types. In this way, the reception desk can adjust how the input interface is displayed based on the user's emotions.

[0115] The analysis unit can improve its accuracy by referring to the user's past input data during analysis. For example, it can improve analysis accuracy by referring to themes, moods, and instrument types previously entered by the user. It can also extract specific patterns from the user's past input data and reflect them in the analysis algorithm. Furthermore, it can select the optimal analysis method based on the user's past input data. In this way, the analysis unit can improve its analysis accuracy by referring to the user's past input data.

[0116] The generation unit can estimate the user's emotions and adjust the music generation algorithm based on those emotions. For example, if the user is relaxed, the music generation algorithm can be adjusted to produce a slow-tempo song. If the user is in a hurry, the algorithm can be adjusted to produce a fast-tempo song. Furthermore, if the user is excited, the algorithm can be adjusted to produce an energetic song. In this way, the generation unit can adjust the music generation algorithm based on the user's emotions.

[0117] The delivery unit can select the optimal delivery method by referring to the user's past download history at the time of delivery. For example, it can select the optimal delivery method by referring to the theme, mood, and instrument type of music the user has downloaded in the past. It can also extract specific patterns from the user's past download history and reflect them in the delivery method. Furthermore, it can select the optimal delivery method based on the user's past download history. In this way, the delivery unit can select the optimal delivery method by referring to the user's past download history.

[0118] The warranty department can estimate the user's emotions and adjust the warranty content based on those emotions. For example, if the user is feeling anxious, it can provide detailed warranty information to reassure them. If the user is relaxed, it can provide concise warranty information. Furthermore, if the user is in a hurry, it can provide warranty information that can be quickly understood. In this way, the warranty department can adjust the warranty content based on the user's emotions.

[0119] The customization unit can select the optimal customization method by referring to the user's past customization history during the customization process. For example, it can select the optimal customization method by referring to the themes, moods, and instrument types of songs the user has customized in the past. It can also extract specific patterns from the user's past customization history and reflect them in the customization method. Furthermore, it can select the optimal customization technique based on the user's past customization history. In this way, the customization unit can select the optimal customization method by referring to the user's past customization history.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The reception unit receives user input. User input includes text input, voice input, and image input. The reception unit may be equipped with an interface for receiving text input, a microphone and voice recognition technology for receiving voice input, and a camera and image analysis technology for receiving image input. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using methods such as natural language processing, data mining, and machine learning algorithms. For example, the analysis unit analyzes the user's input data using natural language processing techniques, data mining techniques, and machine learning algorithms. Step 3: The generation unit generates music based on the information analyzed by the analysis unit. Music generation can be performed using methods such as generation based on music theory or automatic generation using AI. For example, the generation unit can generate music based on music theory or automatically generate music using AI. Step 4: The provider unit provides the music generated by the generator unit. The provision is carried out by methods such as streaming, downloading, and real-time playback. For example, the provider unit provides the generated music in streaming format, downloadable format, and real-time playback format.

[0122] 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.

[0123] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0124] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives user input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user input. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates music based on the analyzed information. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated music. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] 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.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0129] 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.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0131] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] 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.

[0133] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives user input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user input. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates music based on the analyzed information. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated music. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] 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.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0145] 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.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0147] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] 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.

[0149] 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.

[0150] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives user input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user input. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates music based on the analyzed information. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated music. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0161] 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.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0163] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] 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.

[0165] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0166] 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.

[0167] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] 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.

[0172] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives user input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user input. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates music based on the analyzed information. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated music. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0175] 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.

[0176] Figure 9 shows the 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.

[0177] 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.

[0178] 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.

[0179] 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, and motorcycles, 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 based, for example, 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.

[0180] 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."

[0181] 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.

[0182] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0191] 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 other things 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.

[0192] 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.

[0193] (Note 1) A reception area that receives user input, An analysis unit that analyzes the information received by the reception unit, A generation unit that generates music based on the information analyzed by the analysis unit, The system includes a providing unit that provides music generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is It includes a guarantee section that ensures the generated music is available for commercial use. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is It includes a customization section that customizes music based on user input. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, This system analyzes user input by combining natural language processing and music theory-based algorithms. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, The generated music will be provided in a downloadable format. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When users enter data, the input fields are customized based on their current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and prioritizes input fields based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users enter data, the system prioritizes displaying the most relevant input fields based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is During input, the system analyzes the user's social media activity and suggests relevant input fields. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the system improves analysis accuracy by referencing the user's past input data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis methods are applied depending on the user's input category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration to improve analysis accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by referencing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is The system estimates the user's emotions and adjusts the music generation algorithm based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, the system references the user's past music generation history to improve generation accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, different generation methods are applied depending on the user's input category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and adjusts how the generated music is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, the user's geographical location information is taken into consideration to improve generation accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, we improve generation accuracy by referencing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, We estimate the user's emotions and adjust the way we provide music based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the product, the system will refer to the user's past download history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, customize the delivery format based on the user's current project. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the songs to be offered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and propose a delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned warranty section is, We estimate the user's emotions and adjust the warranty terms based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned warranty section is, During warranty checks, we improve the accuracy of the warranty by referring to the user's past usage history. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned warranty section is, The system estimates user sentiment and determines guarantee priorities based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned warranty section is, During warranty claims, the warranty terms will be customized to take into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned customization unit is It estimates the user's emotions and adjusts the customization method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned customization unit is During customization, the system selects the optimal customization method by referring to the user's past customization history. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned customization unit is It estimates the user's emotions and determines the priority of customization based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned customization unit is During customization, the optimal customization method is selected by considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that receives user input, An analysis unit that analyzes the information received by the reception unit, A generation unit that generates music based on the information analyzed by the analysis unit, The system includes a providing unit that provides music generated by the generation unit. A system characterized by the following features.

2. The generating unit is It includes a guarantee section that ensures the generated music is available for commercial use. The system according to feature 1.

3. The generating unit is It includes a customization section that customizes music based on user input. The system according to feature 1.

4. The aforementioned analysis unit, This system analyzes user input by combining natural language processing and music theory-based algorithms. The system according to feature 1.

5. The aforementioned supply unit is, The generated music will be provided in a downloadable format. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When users enter data, the input fields are customized based on their current projects and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and prioritizes input fields based on those emotions. The system according to feature 1.

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