system

Abstract

The system according to this embodiment aims to generate background music in real time that harmonizes with natural sounds, thereby providing users with an immersive musical experience. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects natural sounds. The analysis unit analyzes the natural sounds collected by the collection unit and understands the characteristics of the natural sounds. The generation unit generates background music (BGM) based on the characteristics of the natural sounds understood by the analysis unit. The provision unit provides the BGM generated by the generation unit to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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, it is difficult to generate BGM that harmonizes with natural sounds in real time, and there is room for improvement in providing a music experience with immersion for users.

[0005] The system according to the embodiment aims to generate BGM that harmonizes with natural sounds in real time and provide a music experience with immersion for users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects natural sounds. The analysis unit analyzes the natural sounds collected by the collection unit and understands the characteristics of the natural sounds. The generation unit generates background music (BGM) based on the characteristics of the natural sounds understood by the analysis unit. The provision unit provides the BGM generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can generate background music in real time that harmonizes with natural sounds, providing users with an immersive musical experience. [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 numbered 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 applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[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) The music generation system according to an embodiment of the present invention is a music application that uses generation AI technology to create background music in real time that harmonizes with natural sounds. This music generation system instantly generates music that perfectly matches the characteristics and atmosphere of various natural sounds, such as the whispering of rain, the rustling of wind, and the rhythm of waves. Users can enjoy an immersive musical experience as if they were in nature simply by launching the application. Furthermore, by utilizing the built-in microphone of a smartphone, it is also possible to capture actual ambient sounds and generate background music that is perfectly synchronized with the surrounding natural sounds. This elevates any outdoor activity, such as a picnic in a park or a walk on the beach, into a special experience fused with music. Indoors, opening a window allows users to enjoy music that harmonizes with outside sounds, transforming even the hustle and bustle of the city into a pleasant musical experience. This music generation system offers a new way of engaging with music to a wide range of users, including students who want to improve their concentration, business people seeking stress relief, and yoga enthusiasts pursuing meditation and deep relaxation. For example, the music generation system can use generation AI to understand the characteristics of natural sounds and learn their patterns and structures. For example, by providing natural sounds such as rain, wind, or waves as input, the generating AI analyzes the waveform and frequency spectrum of those sounds and captures their characteristics. Next, it generates new natural sounds based on those characteristics. It is important that the generated natural sounds are realistic. Once the natural sounds are generated, the next step is to generate background music (BGM). The generating AI utilizes its learned knowledge of musical patterns, chord progressions, and rhythms to generate BGM that is suitable for the given natural sounds. BGM that supports relaxation or concentration is generated in accordance with the natural sounds selected by the user. The generating AI is also used to generate music in real time. When a user selects a natural sound in the app, the generating AI instantly generates BGM that matches the natural sound and provides it to the user. This allows users to enjoy music that promotes relaxation or concentration anytime, anywhere. By providing feedback on the generated music, the generating AI can further improve its accuracy.For example, if a user fails to generate background music that matches their preferred sound in response to a particular natural sound, the generation AI learns from that feedback and incorporates it into future generation. This allows the music generation system to create and provide background music that harmonizes with natural sounds in real time.

[0029] The music generation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects natural sounds. The collection unit can collect natural sounds such as the whispering of rain, the rustling of wind, and the rhythm of waves. The collection unit can also collect actual ambient sounds using the built-in microphone of a smartphone. For example, the collection unit can use the built-in microphone of a smartphone to collect ambient sounds such as picnics in parks or walks on the beach. The analysis unit analyzes the natural sounds collected by the collection unit and understands the characteristics of the natural sounds. The analysis unit can analyze the waveform and frequency spectrum of natural sounds, for example, and understand their characteristics. The analysis unit uses techniques such as Fourier transform and spectrogram analysis to analyze the characteristics of natural sounds in detail. The generation unit generates background music (BGM) based on the characteristics of natural sounds understood by the analysis unit. The generation unit can use generation AI to generate BGM based on the characteristics of natural sounds. The generation unit, for example, uses a generation AI to analyze the waveform and frequency spectrum of natural sounds, capture their characteristics, and generate background music (BGM). The generation unit utilizes the generation AI's learned knowledge of music patterns, chord progressions, rhythms, etc., to generate BGM that matches the natural sounds. The provision unit provides the BGM generated by the generation unit to the user. The provision unit can provide the generated BGM to the user in real time. The provision unit provides the generated BGM to the user by methods such as streaming, downloading, or real-time distribution. As a result, the music generation system according to this embodiment can generate and provide BGM based on natural sounds to the user in real time.

[0030] The sound collection unit collects natural sounds. For example, it can collect natural sounds such as the whispering of rain, the rustling of wind, and the rhythm of waves. Specifically, the unit uses high-sensitivity microphones to collect sounds from the natural environment with high precision. These microphones are equipped with filters to reduce wind noise and other background noise, allowing for the collection of natural sounds with clear sound quality. The unit can also utilize the built-in microphone of a smartphone to collect actual ambient sounds. For example, the unit can use the smartphone's built-in microphone to collect ambient sounds such as those from a picnic in a park or a walk on the beach. It is also possible to record the location information of the collected sounds using the smartphone's GPS function, allowing for the later identification of natural sounds collected in specific locations. Furthermore, the unit can perform stereo recording using multiple microphones to collect more three-dimensional and immersive natural sounds. This allows the unit to collect high-quality natural sounds from diverse environments, providing the data necessary for subsequent analysis and generation processes.

[0031] The analysis unit analyzes the natural sounds collected by the collection unit and understands their characteristics. For example, the analysis unit can analyze the waveform and frequency spectrum of natural sounds and understand their characteristics. Specifically, the analysis unit uses techniques such as Fourier transform and spectrogram analysis to analyze the temporal and frequency characteristics of natural sounds in detail. By using the Fourier transform, it is possible to extract the frequency components of natural sounds and analyze the intensity and phase of each component. Spectrogram analysis allows for the simultaneous visualization of both temporal and frequency information, enabling the understanding of the fluctuation patterns of natural sounds. Furthermore, the analysis unit can classify the characteristics of the collected natural sounds and perform pattern recognition using machine learning algorithms. For example, it can classify rain sounds, wind sounds, and wave sounds into different categories and learn the characteristics of each. As a result, the analysis unit can understand the characteristics of the collected natural sounds with high accuracy and provide the basic data for the generation unit to generate background music.

[0032] The generation unit generates background music (BGM) based on the characteristics of natural sounds understood by the analysis unit. The generation unit can generate BGM based on the characteristics of natural sounds using a generation AI. Specifically, the generation AI analyzes the waveform and frequency spectrum of natural sounds, captures their characteristics, and generates BGM. The generation AI utilizes deep learning technology to leverage knowledge of musical patterns, chord progressions, and rhythms learned from a vast music dataset. For example, the generation AI can generate a piano melody that matches the rhythm of rain, or a string instrument harmony that harmonizes with the sound of wind. The generation AI can generate BGM that reflects the characteristics of natural sounds in real time and can also be customized according to the user's preferences. For example, if the user wants to relax, it can generate a gentle melody, and if they want to concentrate, it can generate a fast-paced rhythm. As a result, the generation unit can generate high-quality BGM that matches natural sounds and provide it to the user.

[0033] The service provider delivers background music (BGM) generated by the generation unit to the user. The service provider can deliver the generated BGM to the user in real time. Specifically, the service provider delivers the generated BGM to the user through methods such as streaming, downloading, and real-time delivery. By using streaming services, users can enjoy the generated BGM anytime, anywhere via an internet connection. By using the download function, users can save the generated BGM to their device and play it offline. With real-time delivery, the generated BGM is delivered to the user instantly, allowing them to enjoy the music as if it were live. Furthermore, the service provider has a function that allows users to rate the generated BGM and provide feedback through the user interface. This allows the service provider to collect user preferences and feedback and provide that feedback to the generation unit, thereby continuously improving the quality of the generated BGM. As a result, the service provider can quickly and effectively deliver high-quality BGM to users and increase user satisfaction.

[0034] The sound collection unit can collect actual ambient sounds using the built-in microphone of a smartphone. For example, the sound collection unit can use the smartphone's built-in microphone to collect ambient sounds such as those from a picnic in a park or a walk on the beach. The sound collection unit can also use the smartphone's built-in microphone to collect ambient natural sounds in real time. For example, the sound collection unit can use the smartphone's built-in microphone to collect urban noise or indoor sounds. By collecting actual ambient sounds, it is possible to generate background music based on more realistic natural sounds. Some or all of the above processing in the sound collection unit may be performed using AI, for example, or without AI. For example, the sound collection unit can input ambient sound data acquired with the smartphone's built-in microphone into a generation AI and have the generation AI perform analysis of the ambient sound data.

[0035] The generation unit can generate background music (BGM) based on the characteristics of natural sounds using a generation AI. For example, the generation unit's generation AI analyzes the waveform and frequency spectrum of natural sounds, captures their characteristics, and generates BGM. The generation unit can also generate BGM that matches natural sounds by utilizing the generation AI's learned knowledge of musical patterns, chord progressions, and rhythms. For example, the generation unit's generation AI understands the characteristics of natural sounds and generates BGM that supports relaxation or concentration based on those characteristics. The generation unit is also used for real-time music generation by the generation AI. For example, when a user selects a natural sound in the app, the generation unit's generation AI generates BGM that matches the natural sound at that moment and provides it to the user. This allows for high-precision generation of BGM that matches natural sounds by using the generation AI. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit can generate BGM using a generation AI model that takes the characteristics of natural sounds as input and outputs BGM.

[0036] The service provider can provide the generated background music (BGM) to the user in real time. For example, the service provider can provide the generated BGM to the user through methods such as streaming, downloading, or real-time delivery. By providing the generated BGM to the user in real time, the user can immediately enjoy the musical experience. For example, the service provider can provide the generated BGM to the user through web applications or mobile applications. If the user desires paper-based feedback on the generated BGM, the service provider can also print the results using a printer. The service provider can also provide rapid feedback by sending the results directly to the user via email. This allows the user to immediately enjoy the musical experience by providing the BGM in real time. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can provide the generated BGM to the user in real time using an AI model.

[0037] The analysis unit can analyze the waveform and frequency spectrum of natural sounds and understand their characteristics. For example, the analysis unit can analyze the waveform and frequency spectrum of natural sounds and understand their characteristics. The analysis unit can use techniques such as Fourier transform and spectrogram analysis to analyze the characteristics of natural sounds in detail. For example, the analysis unit can use Fourier transform to analyze the frequency components of natural sounds and understand their characteristics. The analysis unit can also use spectrogram analysis to analyze the temporal changes of natural sounds and understand their characteristics. By understanding the characteristics of natural sounds, the analysis unit provides the generation unit with basic data to generate more appropriate background music. This allows for the generation of more appropriate background music by analyzing the characteristics of natural sounds in detail. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not. For example, the analysis unit can input natural sound waveform data into a generation AI and have the generation AI perform the analysis of the natural sound characteristics.

[0038] The generation unit can improve its accuracy by having the generation AI learn from user feedback. For example, if a user provides feedback on the generated background music (BGM), the generation AI will learn from that feedback and improve its accuracy. If a user does not receive BGM that matches their preference for a particular natural sound, the generation AI can learn from that feedback and reflect it in future generation. For example, if a user provides an evaluation score for the generated BGM, the generation AI will learn from that evaluation score and improve its accuracy. The generation unit can also collect user feedback, and the generation AI will learn from that feedback and improve its accuracy. In this way, the accuracy of the generated BGM improves by reflecting user feedback. Some or all of the above processes in the generation unit are performed using the generation AI. For example, the generation unit can input user feedback data into the generation AI, which can then learn and improve its accuracy.

[0039] The collection unit can analyze the user's past ambient sound collection history and select the optimal collection method. For example, the collection unit can analyze patterns of natural sounds previously collected by the user and suggest the most preferred collection method. The collection unit can also optimize the types of natural sounds to collect at specific time periods based on the user's past collection history. The collection unit can also adjust the collection frequency and timing based on the user's past collection history. In this way, by analyzing past collection history, the collection unit can provide the user with the most suitable method for collecting natural sounds. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's past ambient sound collection history data into a generating AI and have the generating AI select the optimal collection method.

[0040] The sound collection unit can filter natural sounds based on the user's current activity and environment. For example, if the user is outdoors, the unit will prioritize collecting natural sounds such as wind and birdsong. If the user is relaxing indoors, the unit can also collect quiet natural sounds such as rain and waves. If the user is concentrating on a task, the unit can filter out ambient noise and collect natural sounds that do not interfere with concentration. This allows for the generation of more appropriate background music by collecting natural sounds that are tailored to the user's activity and environment. Some or all of the above processing in the sound collection unit may be performed using AI or not. For example, the sound collection unit can input the user's current activity and environment data into a generating AI and have the generating AI perform the filtering.

[0041] The sound collection unit can prioritize collecting sounds that are highly relevant based on the user's geographical location information when collecting natural sounds. For example, if the user is at the beach, the collection unit will prioritize collecting the sound of waves and the calls of seabirds. If the user is in the mountains, the collection unit can also prioritize collecting the sound of wind and birdsong. If the user is in an urban area, the collection unit can also prioritize collecting the sound of wind and rain. This allows for the generation of more appropriate background music by collecting natural sounds based on the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's geographical location information data into a generation AI and have the generation AI perform the collection of highly relevant sounds.

[0042] The collection unit can analyze the user's social media activity when collecting natural sounds and collect relevant sounds. For example, if the user posts about nature on social media, the collection unit can collect natural sounds related to those posts. If the user posts about relaxation on social media, the collection unit can also collect relaxing natural sounds. If the user posts about concentration on social media, the collection unit can also collect natural sounds that help with concentration. This allows for the generation of more appropriate background music by collecting natural sounds based on the user's social media activity. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant sounds.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the natural sounds during the analysis. For example, the analysis unit provides detailed analysis results for natural sounds of high importance. The analysis unit can also provide simplified analysis results for natural sounds of low importance. The analysis unit can also adjust the frequency of analysis according to importance. This allows for the provision of more appropriate analysis results by adjusting the level of detail of the analysis according to the importance of the natural sounds. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input natural sound importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of natural sound during analysis. For example, for birdsong, the analysis unit can use frequency analysis to extract detailed features. For wave sounds, the analysis unit can also use waveform analysis to extract rhythm and patterns. For wind sounds, the analysis unit can use spectral analysis to analyze the intensity and fluctuations of the sound. By applying an analysis algorithm appropriate to the category of natural sound, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input natural sound category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the timing of natural sound collection during the analysis. For example, the analysis unit may prioritize the analysis of recently collected natural sounds. The analysis unit may also postpone the analysis of natural sounds collected in the past. The analysis unit can also adjust the frequency of analysis according to the collection timing. This allows for the provision of more appropriate analysis results by determining the priority of analysis based on the timing of natural sound collection. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input natural sound collection timing data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of natural sounds during the analysis. For example, the analysis unit may prioritize the analysis of natural sounds with high relevance. The analysis unit may also postpone the analysis of natural sounds with low relevance. The analysis unit can also adjust the order of analysis according to the relevance. By adjusting the order of analysis based on the relevance of natural sounds, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance data of natural sounds into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The generation unit can adjust the level of detail of the background music (BGM) based on the characteristics of natural sounds during generation. For example, if the natural sounds are gentle, the generation unit will generate simple BGM. If the natural sounds are complex, the generation unit can also generate detailed BGM. The generation unit can also adjust the rhythm and melody of the BGM according to the characteristics of the natural sounds. This allows for the generation of more appropriate BGM by adjusting the level of detail based on the characteristics of natural sounds. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input natural sound characteristic data into a generation AI and have the generation AI perform the adjustment of the level of detail of the BGM.

[0048] The generation unit can apply different generation algorithms depending on the category of natural sound during generation. For example, the generation unit can apply an algorithm that generates melodic background music to birdsong. It can also apply an algorithm that generates rhythmic background music to the sound of waves. It can also apply an algorithm that generates ambient background music to the sound of wind. By applying a generation algorithm according to the category of natural sound, more appropriate background music can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input natural sound category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0049] The generation unit can determine the priority of background music (BGM) based on the timing of natural sound collection during generation. For example, the generation unit may prioritize generating BGM based on recently collected natural sounds. The generation unit may also postpone generating BGM based on previously collected natural sounds. The generation unit can also adjust the BGM generation order according to the collection timing. This allows for the generation of more appropriate BGM by determining the priority of BGM based on the timing of natural sound collection. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input natural sound collection timing data into a generation AI and have the generation AI perform the BGM priority determination.

[0050] The generation unit can adjust the order of background music (BGM) based on the relevance of natural sounds during generation. For example, the generation unit can prioritize generating BGM based on highly relevant natural sounds. The generation unit can also postpone generating BGM based on less relevant natural sounds. The generation unit can also adjust the generation order of BGM according to its relevance. This allows for the generation of more appropriate BGM by adjusting the order of BGM based on the relevance of natural sounds. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input natural sound relevance data into a generation AI and have the generation AI perform the adjustment of the BGM order.

[0051] The service provider can select the optimal service method by referring to the user's past BGM usage history at the time of service. For example, the service provider can prioritize providing BGM that the user has previously enjoyed using. The service provider can also provide BGM that is optimal for a specific time period based on the user's past usage history. The service provider can also adjust the frequency and timing of service based on the user's past usage history. This allows for a more appropriate musical experience by providing BGM that is optimal based on the user's past usage history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past BGM usage history data into a generating AI and have the generating AI select the optimal service method.

[0052] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the delivery unit can provide a display method that matches the screen size. If the user is using a tablet, the delivery unit can also provide a display method optimized for a larger screen. If the user is using a smartwatch, the delivery unit can also provide a simple and highly visible display method. By selecting the optimal delivery method considering the user's device information, a more appropriate music experience can be provided. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input user device information data into a generating AI and have the generating AI select the optimal delivery method.

[0053] The service provider can adjust its service delivery method based on user feedback at the time of delivery. For example, if a user provides feedback on the provided background music, the service provider can adjust its service delivery method based on that feedback. If a user does not like a particular piece of background music, the service provider can also adjust its service delivery method based on that information. If a user likes a particular piece of background music, the service provider can also adjust its service delivery method based on that information. In this way, by adjusting the service delivery method based on user feedback, more appropriate background music can be provided. Some or all of the above processing in the service provider may be performed using AI, or it may be performed without using AI. For example, the service provider can input user feedback data into a generating AI and have the generating AI perform the adjustment of the service delivery method.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The collection unit can analyze the user's past ambient sound collection history and select the optimal collection method. For example, the collection unit can analyze patterns of natural sounds previously collected by the user and suggest the most preferred collection method. The collection unit can also optimize the types of natural sounds to collect at specific time periods based on the user's past collection history. The collection unit can also adjust the collection frequency and timing based on the user's past collection history. In this way, by analyzing past collection history, the collection unit can provide the user with the most suitable method for collecting natural sounds. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's past ambient sound collection history data into a generating AI and have the generating AI select the optimal collection method.

[0056] The analysis unit can adjust the level of detail of the analysis based on the importance of the natural sounds during the analysis. For example, the analysis unit provides detailed analysis results for natural sounds of high importance. The analysis unit can also provide simplified analysis results for natural sounds of low importance. The analysis unit can also adjust the frequency of analysis according to importance. This allows for the provision of more appropriate analysis results by adjusting the level of detail of the analysis according to the importance of the natural sounds. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input natural sound importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0057] The generation unit can adjust the level of detail of the background music (BGM) based on the characteristics of natural sounds during generation. For example, if the natural sounds are gentle, the generation unit will generate simple BGM. If the natural sounds are complex, the generation unit can also generate detailed BGM. The generation unit can also adjust the rhythm and melody of the BGM according to the characteristics of the natural sounds. This allows for the generation of more appropriate BGM by adjusting the level of detail based on the characteristics of natural sounds. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input natural sound characteristic data into a generation AI and have the generation AI perform the adjustment of the level of detail of the BGM.

[0058] The service provider can select the optimal service method by referring to the user's past BGM usage history at the time of service. For example, the service provider can prioritize providing BGM that the user has previously enjoyed using. The service provider can also provide BGM that is optimal for a specific time period based on the user's past usage history. The service provider can also adjust the frequency and timing of service based on the user's past usage history. This allows for a more appropriate musical experience by providing BGM that is optimal based on the user's past usage history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past BGM usage history data into a generating AI and have the generating AI select the optimal service method.

[0059] The analysis unit can apply different analysis algorithms depending on the category of natural sound during analysis. For example, for birdsong, the analysis unit can use frequency analysis to extract detailed features. For wave sounds, the analysis unit can also use waveform analysis to extract rhythm and patterns. For wind sounds, the analysis unit can use spectral analysis to analyze the intensity and fluctuations of the sound. By applying an analysis algorithm appropriate to the category of natural sound, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input natural sound category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The collection unit collects natural sounds. The collection unit can collect natural sounds such as the whispering of rain, the rustling of wind, and the rhythm of waves. The collection unit can also collect actual ambient sounds using the built-in microphone of a smartphone. For example, the collection unit can use the built-in microphone of a smartphone to collect ambient sounds such as those from a picnic in a park or a walk on the beach. Step 2: The analysis unit analyzes the natural sounds collected by the acquisition unit and understands their characteristics. For example, the analysis unit can analyze the waveform and frequency spectrum of the natural sounds and understand their characteristics. The analysis unit uses techniques such as Fourier transform and spectrogram analysis to analyze the characteristics of the natural sounds in detail. Step 3: The generation unit generates background music (BGM) based on the characteristics of natural sounds understood by the analysis unit. The generation unit can generate BGM based on the characteristics of natural sounds using a generation AI. For example, the generation AI analyzes the waveform and frequency spectrum of natural sounds, captures their characteristics, and generates BGM. The generation unit utilizes the knowledge of musical patterns, chord progressions, and rhythms that the generation AI has learned to generate BGM that matches the natural sounds. Step 4: The provider unit provides the BGM generated by the generator unit to the user. The provider unit can provide the generated BGM to the user in real time. For example, the provider unit can provide the generated BGM to the user through methods such as streaming, downloading, or real-time distribution.

[0062] (Example of form 2) The music generation system according to an embodiment of the present invention is a music application that uses generation AI technology to create background music in real time that harmonizes with natural sounds. This music generation system instantly generates music that perfectly matches the characteristics and atmosphere of various natural sounds, such as the whispering of rain, the rustling of wind, and the rhythm of waves. Users can enjoy an immersive musical experience as if they were in nature simply by launching the application. Furthermore, by utilizing the built-in microphone of a smartphone, it is also possible to capture actual ambient sounds and generate background music that is perfectly synchronized with the surrounding natural sounds. This elevates any outdoor activity, such as a picnic in a park or a walk on the beach, into a special experience fused with music. Indoors, opening a window allows users to enjoy music that harmonizes with outside sounds, transforming even the hustle and bustle of the city into a pleasant musical experience. This music generation system offers a new way of engaging with music to a wide range of users, including students who want to improve their concentration, business people seeking stress relief, and yoga enthusiasts pursuing meditation and deep relaxation. For example, the music generation system can use generation AI to understand the characteristics of natural sounds and learn their patterns and structures. For example, by providing natural sounds such as rain, wind, or waves as input, the generating AI analyzes the waveform and frequency spectrum of those sounds and captures their characteristics. Next, it generates new natural sounds based on those characteristics. It is important that the generated natural sounds are realistic. Once the natural sounds are generated, the next step is to generate background music (BGM). The generating AI utilizes its learned knowledge of musical patterns, chord progressions, and rhythms to generate BGM that is suitable for the given natural sounds. BGM that supports relaxation or concentration is generated in accordance with the natural sounds selected by the user. The generating AI is also used to generate music in real time. When a user selects a natural sound in the app, the generating AI instantly generates BGM that matches the natural sound and provides it to the user. This allows users to enjoy music that promotes relaxation or concentration anytime, anywhere. By providing feedback on the generated music, the generating AI can further improve its accuracy.For example, if a user fails to generate background music that matches their preferred sound in response to a particular natural sound, the generation AI learns from that feedback and incorporates it into future generation. This allows the music generation system to create and provide background music that harmonizes with natural sounds in real time.

[0063] The music generation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects natural sounds. The collection unit can collect natural sounds such as the whispering of rain, the rustling of wind, and the rhythm of waves. The collection unit can also collect actual ambient sounds using the built-in microphone of a smartphone. For example, the collection unit can use the built-in microphone of a smartphone to collect ambient sounds such as picnics in parks or walks on the beach. The analysis unit analyzes the natural sounds collected by the collection unit and understands the characteristics of the natural sounds. The analysis unit can analyze the waveform and frequency spectrum of natural sounds, for example, and understand their characteristics. The analysis unit uses techniques such as Fourier transform and spectrogram analysis to analyze the characteristics of natural sounds in detail. The generation unit generates background music (BGM) based on the characteristics of natural sounds understood by the analysis unit. The generation unit can use generation AI to generate BGM based on the characteristics of natural sounds. The generation unit, for example, uses a generation AI to analyze the waveform and frequency spectrum of natural sounds, capture their characteristics, and generate background music (BGM). The generation unit utilizes the generation AI's learned knowledge of music patterns, chord progressions, rhythms, etc., to generate BGM that matches the natural sounds. The provision unit provides the BGM generated by the generation unit to the user. The provision unit can provide the generated BGM to the user in real time. The provision unit provides the generated BGM to the user by methods such as streaming, downloading, or real-time distribution. As a result, the music generation system according to this embodiment can generate and provide BGM based on natural sounds to the user in real time.

[0064] The sound collection unit collects natural sounds. For example, it can collect natural sounds such as the whispering of rain, the rustling of wind, and the rhythm of waves. Specifically, the unit uses high-sensitivity microphones to collect sounds from the natural environment with high precision. These microphones are equipped with filters to reduce wind noise and other background noise, allowing for the collection of natural sounds with clear sound quality. The unit can also utilize the built-in microphone of a smartphone to collect actual ambient sounds. For example, the unit can use the smartphone's built-in microphone to collect ambient sounds such as those from a picnic in a park or a walk on the beach. It is also possible to record the location information of the collected sounds using the smartphone's GPS function, allowing for the later identification of natural sounds collected in specific locations. Furthermore, the unit can perform stereo recording using multiple microphones to collect more three-dimensional and immersive natural sounds. This allows the unit to collect high-quality natural sounds from diverse environments, providing the data necessary for subsequent analysis and generation processes.

[0065] The analysis unit analyzes the natural sounds collected by the collection unit and understands their characteristics. For example, the analysis unit can analyze the waveform and frequency spectrum of natural sounds and understand their characteristics. Specifically, the analysis unit uses techniques such as Fourier transform and spectrogram analysis to analyze the temporal and frequency characteristics of natural sounds in detail. By using the Fourier transform, it is possible to extract the frequency components of natural sounds and analyze the intensity and phase of each component. Spectrogram analysis allows for the simultaneous visualization of both temporal and frequency information, enabling the understanding of the fluctuation patterns of natural sounds. Furthermore, the analysis unit can classify the characteristics of the collected natural sounds and perform pattern recognition using machine learning algorithms. For example, it can classify rain sounds, wind sounds, and wave sounds into different categories and learn the characteristics of each. As a result, the analysis unit can understand the characteristics of the collected natural sounds with high accuracy and provide the basic data for the generation unit to generate background music.

[0066] The generation unit generates background music (BGM) based on the characteristics of natural sounds understood by the analysis unit. The generation unit can generate BGM based on the characteristics of natural sounds using a generation AI. Specifically, the generation AI analyzes the waveform and frequency spectrum of natural sounds, captures their characteristics, and generates BGM. The generation AI utilizes deep learning technology to leverage knowledge of musical patterns, chord progressions, and rhythms learned from a vast music dataset. For example, the generation AI can generate a piano melody that matches the rhythm of rain, or a string instrument harmony that harmonizes with the sound of wind. The generation AI can generate BGM that reflects the characteristics of natural sounds in real time and can also be customized according to the user's preferences. For example, if the user wants to relax, it can generate a gentle melody, and if they want to concentrate, it can generate a fast-paced rhythm. As a result, the generation unit can generate high-quality BGM that matches natural sounds and provide it to the user.

[0067] The service provider delivers background music (BGM) generated by the generation unit to the user. The service provider can deliver the generated BGM to the user in real time. Specifically, the service provider delivers the generated BGM to the user through methods such as streaming, downloading, and real-time delivery. By using streaming services, users can enjoy the generated BGM anytime, anywhere via an internet connection. By using the download function, users can save the generated BGM to their device and play it offline. With real-time delivery, the generated BGM is delivered to the user instantly, allowing them to enjoy the music as if it were live. Furthermore, the service provider has a function that allows users to rate the generated BGM and provide feedback through the user interface. This allows the service provider to collect user preferences and feedback and provide that feedback to the generation unit, thereby continuously improving the quality of the generated BGM. As a result, the service provider can quickly and effectively deliver high-quality BGM to users and increase user satisfaction.

[0068] The sound collection unit can collect actual ambient sounds using the built-in microphone of a smartphone. For example, the sound collection unit can use the smartphone's built-in microphone to collect ambient sounds such as those from a picnic in a park or a walk on the beach. The sound collection unit can also use the smartphone's built-in microphone to collect ambient natural sounds in real time. For example, the sound collection unit can use the smartphone's built-in microphone to collect urban noise or indoor sounds. By collecting actual ambient sounds, it is possible to generate background music based on more realistic natural sounds. Some or all of the above processing in the sound collection unit may be performed using AI, for example, or without AI. For example, the sound collection unit can input ambient sound data acquired with the smartphone's built-in microphone into a generation AI and have the generation AI perform analysis of the ambient sound data.

[0069] The generation unit can generate background music (BGM) based on the characteristics of natural sounds using a generation AI. For example, the generation unit's generation AI analyzes the waveform and frequency spectrum of natural sounds, captures their characteristics, and generates BGM. The generation unit can also generate BGM that matches natural sounds by utilizing the generation AI's learned knowledge of musical patterns, chord progressions, and rhythms. For example, the generation unit's generation AI understands the characteristics of natural sounds and generates BGM that supports relaxation or concentration based on those characteristics. The generation unit is also used for real-time music generation by the generation AI. For example, when a user selects a natural sound in the app, the generation unit's generation AI generates BGM that matches the natural sound at that moment and provides it to the user. This allows for high-precision generation of BGM that matches natural sounds by using the generation AI. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit can generate BGM using a generation AI model that takes the characteristics of natural sounds as input and outputs BGM.

[0070] The service provider can provide the generated background music (BGM) to the user in real time. For example, the service provider can provide the generated BGM to the user through methods such as streaming, downloading, or real-time delivery. By providing the generated BGM to the user in real time, the user can immediately enjoy the musical experience. For example, the service provider can provide the generated BGM to the user through web applications or mobile applications. If the user desires paper-based feedback on the generated BGM, the service provider can also print the results using a printer. The service provider can also provide rapid feedback by sending the results directly to the user via email. This allows the user to immediately enjoy the musical experience by providing the BGM in real time. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can provide the generated BGM to the user in real time using an AI model.

[0071] The analysis unit can analyze the waveform and frequency spectrum of natural sounds and understand their characteristics. For example, the analysis unit can analyze the waveform and frequency spectrum of natural sounds and understand their characteristics. The analysis unit can use techniques such as Fourier transform and spectrogram analysis to analyze the characteristics of natural sounds in detail. For example, the analysis unit can use Fourier transform to analyze the frequency components of natural sounds and understand their characteristics. The analysis unit can also use spectrogram analysis to analyze the temporal changes of natural sounds and understand their characteristics. By understanding the characteristics of natural sounds, the analysis unit provides the generation unit with basic data to generate more appropriate background music. This allows for the generation of more appropriate background music by analyzing the characteristics of natural sounds in detail. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not. For example, the analysis unit can input natural sound waveform data into a generation AI and have the generation AI perform the analysis of the natural sound characteristics.

[0072] The generation unit can improve its accuracy by having the generation AI learn from user feedback. For example, if a user provides feedback on the generated background music (BGM), the generation AI will learn from that feedback and improve its accuracy. If a user does not receive BGM that matches their preference for a particular natural sound, the generation AI can learn from that feedback and reflect it in future generation. For example, if a user provides an evaluation score for the generated BGM, the generation AI will learn from that evaluation score and improve its accuracy. The generation unit can also collect user feedback, and the generation AI will learn from that feedback and improve its accuracy. In this way, the accuracy of the generated BGM improves by reflecting user feedback. Some or all of the above processes in the generation unit are performed using the generation AI. For example, the generation unit can input user feedback data into the generation AI, which can then learn and improve its accuracy.

[0073] The collection unit can estimate the user's emotions and adjust the timing of nature sound collection based on the estimated emotions. For example, if the user is relaxed, the collection unit can set the frequency of nature sound collection low to maintain a quiet environment. If the user is stressed, the collection unit can also set the frequency of nature sound collection high to enhance the relaxation effect. If the user is concentrating, the collection unit can adjust the timing of nature sound collection to avoid disrupting their concentration. By adjusting the timing of nature sound collection according to the user's emotions, more appropriate nature sounds can be collected. 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. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0074] The collection unit can analyze the user's past ambient sound collection history and select the optimal collection method. For example, the collection unit can analyze patterns of natural sounds previously collected by the user and suggest the most preferred collection method. The collection unit can also optimize the types of natural sounds to collect at specific time periods based on the user's past collection history. The collection unit can also adjust the collection frequency and timing based on the user's past collection history. In this way, by analyzing past collection history, the collection unit can provide the user with the most suitable method for collecting natural sounds. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's past ambient sound collection history data into a generating AI and have the generating AI select the optimal collection method.

[0075] The sound collection unit can filter natural sounds based on the user's current activity and environment. For example, if the user is outdoors, the unit will prioritize collecting natural sounds such as wind and birdsong. If the user is relaxing indoors, the unit can also collect quiet natural sounds such as rain and waves. If the user is concentrating on a task, the unit can filter out ambient noise and collect natural sounds that do not interfere with concentration. This allows for the generation of more appropriate background music by collecting natural sounds that are tailored to the user's activity and environment. Some or all of the above processing in the sound collection unit may be performed using AI or not. For example, the sound collection unit can input the user's current activity and environment data into a generating AI and have the generating AI perform the filtering.

[0076] The sound collection unit can estimate the user's emotions and determine the priority of natural sounds to collect based on the estimated emotions. For example, if the user is relaxed, the sound collection unit may prioritize collecting sounds like waves or wind. If the user is stressed, the sound collection unit may also prioritize collecting sounds like birdsong or rain. If the user is focused, the sound collection unit may also prioritize collecting quiet ambient sounds. This allows for the collection of more appropriate natural sounds by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sound collection unit may be performed using AI or not. For example, the sound collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The sound collection unit can prioritize collecting sounds that are highly relevant based on the user's geographical location information when collecting natural sounds. For example, if the user is at the beach, the collection unit will prioritize collecting the sound of waves and the calls of seabirds. If the user is in the mountains, the collection unit can also prioritize collecting the sound of wind and birdsong. If the user is in an urban area, the collection unit can also prioritize collecting the sound of wind and rain. This allows for the generation of more appropriate background music by collecting natural sounds based on the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's geographical location information data into a generation AI and have the generation AI perform the collection of highly relevant sounds.

[0078] The collection unit can analyze the user's social media activity when collecting natural sounds and collect relevant sounds. For example, if the user posts about nature on social media, the collection unit can collect natural sounds related to those posts. If the user posts about relaxation on social media, the collection unit can also collect relaxing natural sounds. If the user posts about concentration on social media, the collection unit can also collect natural sounds that help with concentration. This allows for the generation of more appropriate background music by collecting natural sounds based on the user's social media activity. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant sounds.

[0079] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can display the analysis results in visually calming colors. If the user is stressed, the analysis unit can also display the analysis results in a simple and easy-to-understand format. If the user is focused, the analysis unit can display the analysis results in detail, increasing the amount of information. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the natural sounds during the analysis. For example, the analysis unit provides detailed analysis results for natural sounds of high importance. The analysis unit can also provide simplified analysis results for natural sounds of low importance. The analysis unit can also adjust the frequency of analysis according to importance. This allows for the provision of more appropriate analysis results by adjusting the level of detail of the analysis according to the importance of the natural sounds. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input natural sound importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the category of natural sound during analysis. For example, for birdsong, the analysis unit can use frequency analysis to extract detailed features. For wave sounds, the analysis unit can also use waveform analysis to extract rhythm and patterns. For wind sounds, the analysis unit can use spectral analysis to analyze the intensity and fluctuations of the sound. By applying an analysis algorithm appropriate to the category of natural sound, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input natural sound category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can summarize the results concisely. If the user is stressed, the analysis unit can also provide detailed results. If the user is focused, the analysis unit can lengthen the results to increase the amount of information. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The analysis unit can determine the priority of analysis based on the timing of natural sound collection during the analysis. For example, the analysis unit may prioritize the analysis of recently collected natural sounds. The analysis unit may also postpone the analysis of natural sounds collected in the past. The analysis unit can also adjust the frequency of analysis according to the collection timing. This allows for the provision of more appropriate analysis results by determining the priority of analysis based on the timing of natural sound collection. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input natural sound collection timing data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0084] The analysis unit can adjust the order of analysis based on the relevance of natural sounds during the analysis. For example, the analysis unit may prioritize the analysis of natural sounds with high relevance. The analysis unit may also postpone the analysis of natural sounds with low relevance. The analysis unit can also adjust the order of analysis according to the relevance. By adjusting the order of analysis based on the relevance of natural sounds, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance data of natural sounds into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0085] The generation unit can estimate the user's emotions and adjust the way it expresses the generated background music (BGM) based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate BGM with a calm melody. If the user is stressed, the generation unit can also generate BGM with a relaxing effect. If the user is concentrating, the generation unit can also generate BGM that enhances concentration. In this way, by adjusting the way the BGM is expressed according to the user's emotions, more appropriate BGM can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using 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. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0086] The generation unit can adjust the level of detail of the background music (BGM) based on the characteristics of natural sounds during generation. For example, if the natural sounds are gentle, the generation unit will generate simple BGM. If the natural sounds are complex, the generation unit can also generate detailed BGM. The generation unit can also adjust the rhythm and melody of the BGM according to the characteristics of the natural sounds. This allows for the generation of more appropriate BGM by adjusting the level of detail based on the characteristics of natural sounds. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input natural sound characteristic data into a generation AI and have the generation AI perform the adjustment of the level of detail of the BGM.

[0087] The generation unit can apply different generation algorithms depending on the category of natural sound during generation. For example, the generation unit can apply an algorithm that generates melodic background music to birdsong. It can also apply an algorithm that generates rhythmic background music to the sound of waves. It can also apply an algorithm that generates ambient background music to the sound of wind. By applying a generation algorithm according to the category of natural sound, more appropriate background music can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input natural sound category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0088] The generation unit can estimate the user's emotions and adjust the length of the background music (BGM) it generates based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate longer BGM. If the user is stressed, the generation unit can also generate shorter BGM. If the user is focused, the generation unit can also generate BGM of an appropriate length. By adjusting the length of the BGM according to the user's emotions, more appropriate BGM can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using 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. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0089] The generation unit can determine the priority of background music (BGM) based on the timing of natural sound collection during generation. For example, the generation unit may prioritize generating BGM based on recently collected natural sounds. The generation unit may also postpone generating BGM based on previously collected natural sounds. The generation unit can also adjust the BGM generation order according to the collection timing. This allows for the generation of more appropriate BGM by determining the priority of BGM based on the timing of natural sound collection. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input natural sound collection timing data into a generation AI and have the generation AI perform the BGM priority determination.

[0090] The generation unit can adjust the order of background music (BGM) based on the relevance of natural sounds during generation. For example, the generation unit can prioritize generating BGM based on highly relevant natural sounds. The generation unit can also postpone generating BGM based on less relevant natural sounds. The generation unit can also adjust the generation order of BGM according to its relevance. This allows for the generation of more appropriate BGM by adjusting the order of BGM based on the relevance of natural sounds. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input natural sound relevance data into a generation AI and have the generation AI perform the adjustment of the BGM order.

[0091] The service provider can estimate the user's emotions and adjust the way the background music (BGM) is presented based on the estimated emotions. For example, if the user is relaxed, the service provider can provide BGM with a calm melody. If the user is stressed, the service provider can also provide BGM with a relaxing effect. If the user is concentrating, the service provider can also provide BGM that enhances concentration. By adjusting the way the BGM is presented according to the user's emotions, more appropriate BGM can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0092] The service provider can select the optimal service method by referring to the user's past BGM usage history at the time of service. For example, the service provider can prioritize providing BGM that the user has previously enjoyed using. The service provider can also provide BGM that is optimal for a specific time period based on the user's past usage history. The service provider can also adjust the frequency and timing of service based on the user's past usage history. This allows for a more appropriate musical experience by providing BGM that is optimal based on the user's past usage history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past BGM usage history data into a generating AI and have the generating AI select the optimal service method.

[0093] The service provider can estimate the user's emotions and determine the priority of background music (BGM) to be provided based on the estimated emotions. For example, if the user is relaxed, the service provider can prioritize providing BGM with a relaxing effect. If the user is stressed, the service provider can also prioritize providing BGM with stress-relieving effects. If the user is concentrating, the service provider can also prioritize providing BGM that enhances concentration. In this way, by determining the priority of BGM according to the user's emotions, more appropriate BGM can be provided. 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. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the delivery unit can provide a display method that matches the screen size. If the user is using a tablet, the delivery unit can also provide a display method optimized for a larger screen. If the user is using a smartwatch, the delivery unit can also provide a simple and highly visible display method. By selecting the optimal delivery method considering the user's device information, a more appropriate music experience can be provided. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input user device information data into a generating AI and have the generating AI select the optimal delivery method.

[0095] The service provider can adjust its service delivery method based on user feedback at the time of delivery. For example, if a user provides feedback on the provided background music, the service provider can adjust its service delivery method based on that feedback. If a user does not like a particular piece of background music, the service provider can also adjust its service delivery method based on that information. If a user likes a particular piece of background music, the service provider can also adjust its service delivery method based on that information. In this way, by adjusting the service delivery method based on user feedback, more appropriate background music can be provided. Some or all of the above processing in the service provider may be performed using AI, or it may be performed without using AI. For example, the service provider can input user feedback data into a generating AI and have the generating AI perform the adjustment of the service delivery method.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit will prioritize analyzing natural sounds that have a relaxing effect. If the user is stressed, the analysis unit can also prioritize analyzing natural sounds that have a stress-relieving effect. If the user is concentrating, the analysis unit can also prioritize analyzing natural sounds that enhance concentration. By determining the priority of analysis according to the user's emotions, more appropriate analysis results can be provided. 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 these examples. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0098] The service provider can estimate the user's emotions and adjust the length of the background music (BGM) provided based on the estimated emotions. For example, if the user is relaxed, the service provider can provide longer BGM. If the user is stressed, the service provider can also provide shorter BGM. If the user is concentrating, the service provider can provide BGM of an appropriate length. By adjusting the length of the BGM according to the user's emotions, more appropriate BGM can be provided. 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. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0099] The sound collection unit can estimate the user's emotions and determine the type of natural sound to collect based on the estimated emotions. For example, if the user is relaxed, the sound collection unit may prioritize collecting sounds like waves or wind. If the user is stressed, the sound collection unit may also prioritize collecting sounds like birdsong or rain. If the user is concentrating, the sound collection unit may also prioritize collecting quiet ambient sounds. This allows for the collection of more appropriate natural sounds by determining the type of natural sound according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the sound collection unit may be performed using AI or not. For example, the sound collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] The generation unit can estimate the user's emotions and adjust the tempo of the generated background music (BGM) based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate BGM with a relaxed tempo. If the user is stressed, the generation unit can also generate BGM with a relaxing tempo. If the user is focused, the generation unit can also generate BGM with a tempo that enhances concentration. By adjusting the tempo of the BGM according to the user's emotions, more appropriate BGM can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using 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. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0101] The service provider can estimate the user's emotions and adjust the volume of the background music (BGM) based on the estimated emotions. For example, if the user is relaxed, the service provider can provide BGM at a gentle volume. If the user is stressed, the service provider can also provide BGM at a relaxing volume. If the user is concentrating, the service provider can also provide BGM at a volume that enhances concentration. By adjusting the BGM volume according to the user's emotions, more appropriate BGM can be provided. 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. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0102] The collection unit can analyze the user's past ambient sound collection history and select the optimal collection method. For example, the collection unit can analyze patterns of natural sounds previously collected by the user and suggest the most preferred collection method. The collection unit can also optimize the types of natural sounds to collect at specific time periods based on the user's past collection history. The collection unit can also adjust the collection frequency and timing based on the user's past collection history. In this way, by analyzing past collection history, the collection unit can provide the user with the most suitable method for collecting natural sounds. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's past ambient sound collection history data into a generating AI and have the generating AI select the optimal collection method.

[0103] The analysis unit can adjust the level of detail of the analysis based on the importance of the natural sounds during the analysis. For example, the analysis unit provides detailed analysis results for natural sounds of high importance. The analysis unit can also provide simplified analysis results for natural sounds of low importance. The analysis unit can also adjust the frequency of analysis according to importance. This allows for the provision of more appropriate analysis results by adjusting the level of detail of the analysis according to the importance of the natural sounds. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input natural sound importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0104] The generation unit can adjust the level of detail of the background music (BGM) based on the characteristics of natural sounds during generation. For example, if the natural sounds are gentle, the generation unit will generate simple BGM. If the natural sounds are complex, the generation unit can also generate detailed BGM. The generation unit can also adjust the rhythm and melody of the BGM according to the characteristics of the natural sounds. This allows for the generation of more appropriate BGM by adjusting the level of detail based on the characteristics of natural sounds. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input natural sound characteristic data into a generation AI and have the generation AI perform the adjustment of the level of detail of the BGM.

[0105] The service provider can select the optimal service method by referring to the user's past BGM usage history at the time of service. For example, the service provider can prioritize providing BGM that the user has previously enjoyed using. The service provider can also provide BGM that is optimal for a specific time period based on the user's past usage history. The service provider can also adjust the frequency and timing of service based on the user's past usage history. This allows for a more appropriate musical experience by providing BGM that is optimal based on the user's past usage history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past BGM usage history data into a generating AI and have the generating AI select the optimal service method.

[0106] The analysis unit can apply different analysis algorithms depending on the category of natural sound during analysis. For example, for birdsong, the analysis unit can use frequency analysis to extract detailed features. For wave sounds, the analysis unit can also use waveform analysis to extract rhythm and patterns. For wind sounds, the analysis unit can use spectral analysis to analyze the intensity and fluctuations of the sound. By applying an analysis algorithm appropriate to the category of natural sound, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input natural sound category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The collection unit collects natural sounds. The collection unit can collect natural sounds such as the whispering of rain, the rustling of wind, and the rhythm of waves. The collection unit can also collect actual ambient sounds using the built-in microphone of a smartphone. For example, the collection unit can use the built-in microphone of a smartphone to collect ambient sounds such as those from a picnic in a park or a walk on the beach. Step 2: The analysis unit analyzes the natural sounds collected by the acquisition unit and understands their characteristics. For example, the analysis unit can analyze the waveform and frequency spectrum of the natural sounds and understand their characteristics. The analysis unit uses techniques such as Fourier transform and spectrogram analysis to analyze the characteristics of the natural sounds in detail. Step 3: The generation unit generates background music (BGM) based on the characteristics of natural sounds understood by the analysis unit. The generation unit can generate BGM based on the characteristics of natural sounds using a generation AI. For example, the generation AI analyzes the waveform and frequency spectrum of natural sounds, captures their characteristics, and generates BGM. The generation unit utilizes the knowledge of musical patterns, chord progressions, and rhythms that the generation AI has learned to generate BGM that matches the natural sounds. Step 4: The provider unit provides the BGM generated by the generator unit to the user. The provider unit can provide the generated BGM to the user in real time. For example, the provider unit can provide the generated BGM to the user through methods such as streaming, downloading, or real-time distribution.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the collection 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 collection unit collects natural sounds using the built-in microphone of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the waveform and frequency spectrum of the collected natural sounds. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates background music (BGM) based on the characteristics of the analyzed natural sounds. The provision unit is implemented in the control unit 46A of the smart device 14 and provides the generated BGM to the user in real time. 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.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection 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 collection unit collects natural sounds using the built-in microphone of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the waveform and frequency spectrum of the collected natural sounds. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates background music (BGM) based on the characteristics of the analyzed natural sounds. The provision unit is implemented in the control unit 46A of the smart glasses 214 and provides the generated BGM to the user in real time. 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.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the collection 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 collection unit collects natural sounds using the built-in microphone of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the waveform and frequency spectrum of the collected natural sounds. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates background music (BGM) based on the characteristics of the analyzed natural sounds. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides the generated BGM to the user in real time. 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.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the collection 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 collection unit collects natural sounds using the robot 414's built-in microphone. The analysis unit is implemented in the data processing unit 12, for example, by the specific processing unit 290, which analyzes the waveform and frequency spectrum of the collected natural sounds. The generation unit is implemented in the data processing unit 12, for example, by the specific processing unit 290, which generates background music (BGM) based on the characteristics of the analyzed natural sounds. The provision unit is implemented in the robot 414, for example, by the control unit 46A, which provides the generated BGM to the user in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A collection unit that collects natural sounds, An analysis unit analyzes the natural sounds collected by the aforementioned collection unit and understands the characteristics of those natural sounds, A generation unit that generates background music based on the characteristics of natural sounds understood by the analysis unit, The system includes a provisioning unit that provides the BGM generated by the generation unit to the user. A system characterized by the following features. (Note 2) The aforementioned collection unit is The smartphone's built-in microphone is used to collect actual ambient sounds. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The AI ​​generates background music based on the characteristics of natural sounds. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, The generated background music is provided to the user in real time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Analyze the waveforms and frequency spectra of natural sounds to understand their characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is The generative AI learns from user feedback and improves its accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of natural sound collection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system analyzes the user's past ambient sound collection history and selects the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting natural sounds, filtering is performed based on the user's current activities and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of natural sounds to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting natural sounds, the system prioritizes collecting sounds that are highly relevant based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting natural sounds, the system analyzes users' social media activity and collects relevant sounds. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of natural sounds. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of natural sound. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on the timing of natural sound collection. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of natural sounds. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the way background music is generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the level of detail in the background music is adjusted based on the characteristics of natural sounds. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, different generation algorithms are applied depending on the category of natural sound. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the length of the background music generated based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the priority of background music is determined based on when natural sounds were collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the order of background music is adjusted based on the relevance of natural sounds. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the way background music is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the music, the system will refer to the user's past BGM usage history to select the most suitable delivery method. 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 background music to provide 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 device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, We will adjust the delivery method based on user feedback at the time of release. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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 collection unit that collects natural sounds, An analysis unit analyzes the natural sounds collected by the aforementioned collection unit and understands the characteristics of those natural sounds, A generation unit that generates background music based on the characteristics of natural sounds understood by the analysis unit, The system includes a provisioning unit that provides the BGM generated by the generation unit to the user. A system characterized by the following features.

2. The aforementioned collection unit is The smartphone's built-in microphone is used to collect actual ambient sounds. The system according to feature 1.

3. The generating unit is The AI ​​generates background music based on the characteristics of natural sounds. The system according to feature 1.

4. The aforementioned supply unit is, The generated background music is provided to the user in real time. The system according to feature 1.

5. The aforementioned analysis unit, Analyze the waveforms and frequency spectra of natural sounds to understand their characteristics. The system according to feature 1.

6. The generating unit is The generative AI learns from user feedback and improves its accuracy. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of natural sound collection based on those emotions. The system according to feature 1.

8. The aforementioned collection unit is The system analyzes the user's past ambient sound collection history and selects the optimal collection method. The system according to feature 1.

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