System

Abstract

An object of the system according to the embodiment is to generate and provide music in real time even if a user does not have advanced knowledge or technique.SOLUTION: A system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives a user's performance. The generation unit generates music based on the performance input by the reception unit. The providing unit provides the music generated by the generating unit in real time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology requires advanced knowledge and skills to generate music and provide it in real time, making it difficult for average users to do so.

[0005] The system according to the embodiment aims to generate and provide music in real time, even if the user does not have advanced knowledge or skills. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives input of a user's performance. The generating unit generates music based on the performance input by the receiving unit. The providing unit provides the music generated by the generating unit in real time. [Effects of the Invention]

[0007] The system according to the embodiment allows users to generate and provide music in real time, even if they do not have advanced knowledge or skills. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A music generation system according to an embodiment of the present invention inputs a user's performance and uses a generation AI to generate and provide music in real time. The music generation system inputs the user's performance and uses a generation AI to generate music based on the performance and provide it in real time. For example, a user inputs an instrumental performance or vocals into the music generation system. For example, the user can record their own performance or singing and upload it to a platform. The music generation system then uses a generation AI to generate music in real time based on the user's input. The generation AI generates appropriate accompaniment or additional musical elements to match the user's performance or singing. For example, if a user plays guitar, the generation AI generates drum and bass accompaniment. The generated music is combined with the user's performance to create a real-time jam session. By playing along with the music generated by the generation AI, users can enjoy the experience of playing as if they were part of a band. Furthermore, the music generation system allows users to post their creations on the platform and share them with other users. This allows users to receive feedback from other users and create new collaboration opportunities. For example, other users may add additional parts to their own performances, resulting in the creation of a new song. This allows the music generation system to enable users to easily create high-quality music and collaborate with other users to produce more creative works. This allows the music generation system to enable users to easily create high-quality music and collaborate with other users to produce more creative works. For example, a user can input an instrument or vocal performance and the generative AI can generate and provide music in real time, allowing the user to enjoy the experience of playing as if they were part of a band. In addition, users can post the works they create on the platform and share them with other users, creating new opportunities for collaboration.

[0029] A music generation system according to an embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit inputs a user's performance. The user's performance may include, but is not limited to, an instrumental performance, vocals, digital music, etc. For example, the receiving unit allows the user to record their own performance or singing and upload it to the platform. The receiving unit may also allow the user to input the performance in real time. The generating unit generates music based on the performance input by the receiving unit using a generation AI. The generating unit generates, for example, accompaniment or additional musical elements to match the user's performance. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates appropriate music based on the user's performance. For example, if the user is playing guitar, the generating unit generates a drum or bass accompaniment. The generating unit may also generate additional musical elements, such as piano or strings, to match the user's performance. The providing unit provides the music generated by the generating unit in real time. For example, the providing unit provides the generated music to the user using streaming technology. The providing unit minimizes latency, allowing the user to enjoy music in real time. For example, the providing unit may stream the generated music to a user's device and play it in real time. Alternatively, the providing unit may record the generated music and play it later. This allows the music generation system according to the embodiment to generate and provide music in real time based on the user's performance.

[0030] The generation unit can generate accompaniment or additional musical elements in accordance with the user's performance. For example, the generation unit generates a drum or bass accompaniment in accordance with the user's performance. The generation unit can also generate additional musical elements such as piano or strings in accordance with the user's performance. Furthermore, the generation unit can generate musical elements such as a synthesizer or percussion in accordance with the user's performance. This makes it possible to generate appropriate accompaniment or musical elements in accordance with the user's performance. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's performance data into the generation AI and cause the generation AI to generate the accompaniment or additional musical elements.

[0031] The providing unit can provide the generated music to the user in real time. The providing unit, for example, provides the generated music to the user using streaming technology. The providing unit minimizes delay time, allowing the user to enjoy the music in real time. For example, the providing unit streams the generated music to the user's device and plays it in real time. The providing unit can also record the generated music and play it later. In this way, by providing the generated music in real time, the user can instantly enjoy the music. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the generated music data to AI and cause the AI ​​to provide the music in real time.

[0032] The music generation system includes a communication unit that supports communication between users. The communication unit supports communication between users. For example, the communication unit provides a chat function, allowing users to exchange messages in real time. The communication unit also provides a video call function, allowing users to communicate face-to-face. The communication unit also provides a messaging function, allowing users to send and receive messages asynchronously. This supports communication between users, thereby creating new collaborations. For example, users can share ideas through the chat function and jointly create new music. Furthermore, the video call function allows users to exchange opinions in real time and create more creative works. This allows the music generation system to support communication between users, thereby creating new collaborations.

[0033] The music generation system includes a management unit that manages the generative AI's algorithm. The management unit manages the generative AI's algorithm. For example, the management unit manages the generative AI's learning data to improve the accuracy of the algorithm. The management unit can also update the generative AI's algorithm and incorporate the latest technology. Furthermore, the management unit can monitor the performance of the generative AI and make adjustments as necessary. In this way, by managing the generative AI's algorithm, the accuracy of the system can be improved. For example, the management unit regularly updates the generative AI's learning data to respond to the latest music trends. The management unit can also optimize the generative AI's algorithm to generate higher quality music. In this way, the music generation system manages the generative AI's algorithm and improves the system's accuracy.

[0034] The communication unit may provide a function that allows users to post works they have created and share them with other users. For example, the communication unit may provide a function that allows users to post works they have created on the platform. Users can share their performances and musical compositions with other users. The communication unit may also provide a function that allows users to receive feedback from other users. Users can improve their works through comments and ratings from other users. Furthermore, the communication unit may provide a function that supports collaboration between users. Users can create musical compositions in collaboration with other users. By sharing works created by users with other users, users can receive feedback and find new opportunities for collaboration. For example, a new musical composition may be created when a user posts their performance and another user adds additional parts to it. This allows the music generation system to share works created by users with other users and create new collaborations.

[0035] The management unit can manage the generative AI's learning data and algorithms. For example, the management unit can manage the generative AI's learning data to improve the accuracy of the algorithm. The management unit can also update the generative AI's algorithm and incorporate the latest technology. Furthermore, the management unit can monitor the generative AI's performance and make adjustments as necessary. In this way, by managing the generative AI's learning data and algorithms, the accuracy of the system can be improved. For example, the management unit can regularly update the generative AI's learning data to keep up with the latest music trends. In addition, the management unit can optimize the generative AI's algorithm to generate higher quality music. In this way, the music generation system can manage the generative AI's learning data and algorithms to improve the system's accuracy.

[0036] The reception unit can analyze the user's past performance history and select the optimal input method. For example, the reception unit can prioritize and suggest input methods for instruments or vocals that the user has frequently used in the past. The reception unit can also suggest input methods suitable for a specific genre based on the user's past performance history. Furthermore, the reception unit can analyze the user's past performance history and suggest the input method of the most successful session. In this way, the optimal input method can be provided by analyzing the user's past performance history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past performance data into the generation AI and cause the generation AI to select the optimal input method.

[0037] The reception unit can perform filtering based on the user's current musical style and preferences when inputting a performance. The reception unit can, for example, suggest appropriate accompaniments based on the user's currently preferred musical style. The reception unit can also filter and suggest specific instruments and timbres based on the user's current preferences. Furthermore, the reception unit can also suggest optimal tempos and rhythms based on the user's current musical style. This allows for filtering based on the user's current musical style and preferences, thereby providing a more appropriate performance experience. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's musical style data into a generation AI and have the generation AI perform filtering.

[0038] When inputting a performance, the reception unit can select an appropriate input means depending on the user's input method. For example, if the user inputs the performance by voice, the reception unit can have the AI ​​process the input using voice recognition technology. Furthermore, if the user inputs the performance by text, the reception unit can also have the AI ​​process the input using text analysis technology. Furthermore, if the user inputs the performance by image, the reception unit can also have the AI ​​process the input using image recognition technology. This allows for a more appropriate performance experience by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's input data to the generation AI and have the generation AI select the optimal input means.

[0039] The reception unit can suggest an optimal input method by referring to the user's past performance history. For example, the reception unit can prioritize suggesting input methods for instruments or vocals that the user has frequently used in the past. The reception unit can also suggest input methods suitable for a specific genre based on the user's past performance history. Furthermore, the reception unit can analyze the user's past performance history and suggest the input method of the most successful session. In this way, the optimal input method can be provided by referring to the user's past performance history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past performance data into the generation AI and cause the generation AI to suggest an optimal input method.

[0040] The reception unit can suggest an optimal input method based on the user's current musical style and preferences. The reception unit can suggest an appropriate accompaniment based on, for example, the user's currently preferred musical style. The reception unit can also filter and suggest specific instruments and timbres based on the user's current preferences. Furthermore, the reception unit can suggest optimal tempos and rhythms based on the user's current musical style. This makes it possible to provide a more appropriate playing experience by suggesting an optimal input method based on the user's current musical style and preferences. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's musical style data into a generation AI and cause the generation AI to suggest an optimal input method.

[0041] The reception unit can select an appropriate input means depending on the user's input method. For example, when the user inputs a performance by voice, the reception unit uses AI to process the input using voice recognition technology. Furthermore, when the user inputs a performance by text, the reception unit can also use AI to process the input using text analysis technology. Furthermore, when the user inputs a performance by image, the reception unit can also use AI to process the input using image recognition technology. This allows for a more appropriate performance experience by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's input data to the generation AI and have the generation AI select the optimal input means.

[0042] When generating music, the generation unit can adjust the specificity of the generation based on the importance of the performance. For example, the generation unit generates a detailed accompaniment for an important section. The generation unit can also generate a simple accompaniment when used as background music. Furthermore, the generation unit can generate a complex melody line for a solo part. In this way, by adjusting the specificity of the generation based on the importance of the performance, more appropriate music can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input performance data to the generation AI and cause the generation AI to adjust the specificity of the generation.

[0043] When generating music, the generation unit can apply different generation algorithms depending on the performance category. For example, in the case of jazz performance, the generation unit applies an improvisational performance algorithm. In addition, in the case of classical music, the generation unit can also apply a strict score generation algorithm. Furthermore, in the case of pop music, the generation unit can also apply a catchy melody generation algorithm. In this way, by applying different generation algorithms depending on the performance category, more appropriate music can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input performance data to the generation AI and cause the generation AI to apply a generation algorithm depending on the category.

[0044] When generating music, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit adjusts the generation algorithm based on, for example, the user's favorite musical styles in the past. The generation unit can also improve the generation accuracy by learning successful patterns from the user's past generation results. Furthermore, the generation unit can also optimize the generation algorithm by reflecting the user's past feedback. In this way, the generation accuracy can be improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past generation data into the generation AI and cause the generation AI to improve the generation accuracy.

[0045] When generating music, the generation unit can determine the order of generation based on the submission dates of the performances. For example, the generation unit can generate music with priority for performances with an approaching deadline. The generation unit can also generate music with priority for performances submitted earlier. Furthermore, the generation unit can postpone generating music for performances submitted later. In this way, by determining the order of generation based on the submission dates of the performances, music can be generated more efficiently. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input performance data to the generation AI and have the generation AI determine the order of generation.

[0046] When generating music, the generation unit can adjust the order of generation based on the relevance of the performance. For example, the generation unit prioritizes generating music that is highly relevant to the user's performance. The generation unit can also postpone generating music that is less relevant to the user's performance. Furthermore, the generation unit can prioritize generating music of a genre that is more relevant to the user's performance. In this way, by adjusting the order of generation based on the relevance of the performance, more appropriate music can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input performance data to the generation AI and cause the generation AI to adjust the order of generation based on the relevance.

[0047] The generation unit can adjust the application of technical terminology during music generation according to the user's level of expertise. For example, the generation unit generates music using simple technical terminology for a beginner user. The generation unit can also generate music using moderate technical terminology for an intermediate user. Furthermore, the generation unit can generate music using detailed technical terminology for an advanced user. This allows for the generation of more appropriate music by adjusting the use of technical terminology in the generation according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's technical knowledge data into the generation AI and cause the generation AI to adjust the application of technical terminology.

[0048] When providing music, the providing unit can select the optimal providing method by referring to the user's past operation history. The providing unit selects the optimal providing method, for example, based on a display method that the user has previously preferred. The providing unit can also select the most effective providing method from the user's past operation history. Furthermore, the providing unit can also optimize the providing method by reflecting the user's past feedback. In this way, the optimal providing method can be provided by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's operation history data to the generating AI and cause the generating AI to select the optimal providing method.

[0049] When providing music, the providing unit can customize the content to be provided according to the user's current task. For example, when the user is working, the providing unit can provide music that enhances concentration. Furthermore, when the user is relaxing, the providing unit can also provide music that has a relaxing effect. Furthermore, when the user is exercising, the providing unit can also provide energetic music. In this way, by customizing the content to be provided according to the user's current task, a more appropriate music experience can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's task data into a generation AI and cause the generation AI to customize the content to be provided.

[0050] The providing unit can improve the providing method by reflecting user feedback when providing music. For example, when a user provides feedback on the provided music, the providing unit allows the AI ​​to improve the providing method based on that feedback. The providing unit can also analyze the user's feedback and suggest an optimal providing method. Furthermore, the providing unit can also customize the provided content by reflecting user feedback. In this way, by reflecting user feedback, the providing method can be improved and a more appropriate music experience can be provided. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into the generating AI and cause the generating AI to improve the providing method.

[0051] When providing music, the providing unit can select an appropriate delivery method based on the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. This allows for a more appropriate music experience by selecting the optimal delivery method based on the user's device information. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal delivery method.

[0052] When providing music, the providing unit can make the provided content multilingual according to the user's language setting. The providing unit, for example, automatically sets explanations and operation procedures for the music to be provided based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can display the provided music content in that language. This makes it possible to provide a more appropriate music experience by making the provided content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data into the generation AI and cause the generation AI to perform multilingual settings.

[0053] The providing unit can adjust the providing method by reflecting the user's past feedback when providing music. The providing unit customizes the providing method based on, for example, feedback provided by the user on music provided in the past. The providing unit can also analyze the user's past feedback and suggest an optimal providing method. Furthermore, the providing unit can customize the content of the provided music by reflecting the user's past feedback. In this way, by reflecting the user's past feedback, the providing method can be customized and a more appropriate music experience can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's feedback data into a generating AI and cause the generating AI to adjust the providing method.

[0054] The communication unit can select the optimal method by referring to the user's past communication history when communicating. For example, the communication unit selects the optimal method based on the user's past preferred communication methods. The communication unit can also select the most effective method from the user's past communication history. Furthermore, the communication unit can also optimize the communication method by reflecting the user's past feedback. In this way, the optimal communication method can be provided by referring to the user's past communication history. Some or all of the above-mentioned processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input the user's communication history data into a generation AI and have the generation AI select the optimal method.

[0055] The communication unit can customize content based on the user's current interests during communication. The communication unit customizes communication content based on, for example, topics in which the user is currently interested. The communication unit can also provide related information based on the user's current interests. Furthermore, the communication unit can suggest an optimal communication method based on the user's current interests. This allows for more appropriate communication by customizing content based on the user's current interests. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input user interest data into a generation AI and have the generation AI customize the content.

[0056] The communication unit can improve the method by reflecting user feedback during communication. For example, the communication unit improves the method based on feedback provided by the user regarding the communication. The communication unit can also analyze the user's feedback and suggest an optimal communication method. Furthermore, the communication unit can also customize the content of the communication by reflecting the user's feedback. In this way, by reflecting the user's feedback, the communication method can be improved and more appropriate communication can be provided. Some or all of the above-mentioned processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input user feedback data into a generation AI and have the generation AI execute improvements to the method.

[0057] The communication unit can select an appropriate method based on the user's geographical location information when communicating. For example, if the user is in a specific area, the communication unit provides information related to that area. The communication unit can also select an optimal communication method based on the user's geographical location information. Furthermore, the communication unit can provide information on related events and places taking the user's geographical location information into consideration. This allows for more appropriate communication by selecting an optimal method based on the user's geographical location information. Some or all of the above-described processing in the communication unit may be performed using, or without, AI, for example. For example, the communication unit can input the user's location information data into a generation AI and have the generation AI select an optimal method.

[0058] The communication unit can analyze the user's social media activity and provide relevant content during communication. For example, the communication unit can provide information about places where the user has checked in on social media. The communication unit can also analyze the content of the user's social media posts and provide relevant information. Furthermore, the communication unit can provide relevant information by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, relevant content can be provided and more appropriate communication can be provided. Some or all of the above-described processing in the communication unit can be performed, for example, using AI or without AI. For example, the communication unit can input the user's social media data into a generation AI and cause the generation AI to provide relevant content.

[0059] The communication unit can adjust the communication method by reflecting the user's past feedback. For example, the communication unit customizes the communication method based on the user's past feedback. The communication unit can also analyze the user's past feedback and suggest an optimal communication method. Furthermore, the communication unit can customize the communication content by reflecting the user's past feedback. In this way, by reflecting the user's past feedback, the communication method can be customized and more appropriate communication can be provided. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input the user's feedback data into a generation AI and have the generation AI adjust the method.

[0060] During management, the management unit can improve the management algorithm by referring to past data. For example, the management unit analyzes past data and selects an optimal management algorithm. The management unit can also adjust the management algorithm based on past data. Furthermore, the management unit can improve the accuracy of the management algorithm by referring to past data. In this way, by referring to past data, the management algorithm can be optimized and more appropriate data management can be provided. Some or all of the above-mentioned processing in the management unit may be performed, for example, using AI or without AI. For example, the management unit can input past data into a generation AI and have the generation AI improve the management algorithm.

[0061] The management unit can update the data by reflecting user feedback during management. The management unit updates the data based on, for example, feedback provided by the user. The management unit can also analyze the user feedback and propose an optimal data update method. Furthermore, the management unit can customize the data by reflecting user feedback. This allows the data to be updated by reflecting user feedback, thereby providing more appropriate data management. Some or all of the above-described processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can input user feedback data into a generation AI and have the generation AI update the data.

[0062] During management, the management unit can select the optimal management method by referring to the user's past operation history. The management unit selects the optimal management method based on, for example, the user's past operation history. The management unit can also analyze the user's past operation history and propose the most effective management method. Furthermore, the management unit can customize the management method by referring to the user's past operation history. In this way, the optimal management method can be provided by referring to the user's past operation history. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the user's operation history data into a generation AI and have the generation AI select the optimal management method.

[0063] During management, the management unit can set the priority of managed data based on the time of data submission. For example, the management unit can set a high weight for data submitted earlier. The management unit can also set a low weight for data submitted later. Furthermore, the management unit can adjust the weighting of data based on the time of submission. In this way, by weighting managed data based on the time of data submission, more appropriate data management can be provided. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input data submission time information to the generation AI and have the generation AI set the priority.

[0064] During management, the management unit can integrate information from different data sources to enrich the management data. For example, the management unit integrates information from different data sources to enrich the management data. The management unit can also analyze information from different data sources and propose an optimal management method. Furthermore, the management unit can customize the management data based on information from different data sources. In this way, by integrating information from different data sources, the management data can be enriched and more appropriate data management can be provided. Some or all of the above-described processing in the management unit may be performed, for example, using AI or without AI. For example, the management unit can input information from different data sources into a generation AI and have the generation AI integrate the data.

[0065] During management, the management unit can adjust the management method by reflecting the user's past feedback. The management unit, for example, customizes the management method based on feedback provided by the user in the past. The management unit can also analyze the user's past feedback and propose an optimal management method. Furthermore, the management unit can customize the management content by reflecting the user's past feedback. In this way, by reflecting the user's past feedback, the management method can be customized and more appropriate data management can be provided. Some or all of the above-mentioned processing in the management unit may be performed, for example, using AI or without AI. For example, the management unit can input the user's feedback data into a generation AI and have the generation AI adjust the management method.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] When inputting a user's performance, the reception unit can analyze the user's past performance data and suggest the optimal input method. For example, the reception unit can prioritize the suggestion of an input method for an instrument or vocal that the user has frequently used in the past. The reception unit can also suggest an input method suitable for a specific genre based on the user's past performance history. Furthermore, the reception unit can analyze the user's past performance history and suggest the input method of the most successful session. In this way, the optimal input method can be provided by analyzing the user's past performance history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past performance data into the generation AI and cause the generation AI to select the optimal input method.

[0068] When providing music, the providing unit can select the optimal providing method by referring to the user's past operation history. For example, the optimal providing method is selected based on the user's preferred display method in the past. The providing unit can also select the most effective providing method from the user's past operation history. Furthermore, the providing unit can also optimize the providing method by reflecting the user's past feedback. In this way, the optimal providing method can be provided by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's operation history data into the generating AI and cause the generating AI to select the optimal providing method.

[0069] The reception unit can perform filtering based on the user's current musical style and preferences. For example, the reception unit can suggest appropriate accompaniments based on the user's current musical style. The reception unit can also filter and suggest specific instruments and timbres based on the user's current preferences. Furthermore, the reception unit can also suggest optimal tempos and rhythms based on the user's current musical style. This allows for filtering based on the user's current musical style and preferences, thereby providing a more appropriate performance experience. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's musical style data into a generation AI and have the generation AI perform the filtering.

[0070] When generating music, the generation unit can adjust the specificity of the generation based on the importance of the performance. For example, for important sections, the generation unit can generate a detailed accompaniment. The generation unit can also generate a simple accompaniment when used as background music. Furthermore, the generation unit can generate a complex melody line for a solo part. In this way, by adjusting the specificity of the generation based on the importance of the performance, more appropriate music can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input performance data to the generation AI and have the generation AI adjust the specificity of the generation.

[0071] When providing music, the providing unit can customize the content to be provided according to the user's current task. For example, if the user is working, music that enhances concentration is provided. The providing unit can also provide music with a relaxing effect if the user is relaxing. Furthermore, the providing unit can also provide energetic music if the user is exercising. This allows the user to customize the content to be provided according to the user's current task, thereby providing a more appropriate music experience. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's task data into a generation AI and cause the generation AI to customize the content to be provided.

[0072] The communication unit can select the optimal method by referring to the user's past communication history when communicating. For example, the optimal method is selected based on the user's preferred communication methods in the past. The communication unit can also select the most effective method from the user's past communication history. Furthermore, the communication unit can optimize the communication method by reflecting the user's past feedback. In this way, the optimal communication method can be provided by referring to the user's past communication history. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input the user's communication history data into a generation AI and have the generation AI select the optimal method.

[0073] During management, the management unit can integrate information from different data sources to enrich the management data. For example, the management unit integrates information from different data sources to enrich the management data. The management unit can also analyze information from different data sources and propose an optimal management method. Furthermore, the management unit can customize the management data based on information from different data sources. In this way, by integrating information from different data sources, the management data can be enriched and more appropriate data management can be provided. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input information from different data sources into a generation AI and have the generation AI integrate the data.

[0074] The processing flow of the first embodiment will be briefly explained below.

[0075] Step 1: The reception unit inputs the user's performance. The user's performance can include instrumental performance, vocals, digital music, etc. The reception unit allows the user to record their own performance or singing voice and upload it to the platform. The user can also input their performance in real time. Step 2: The generator uses the generation AI to generate music based on the performance input by the receiver. The generator generates accompaniment and additional musical elements to match the user's performance. For example, if the user is playing guitar, the generator can generate accompaniment for drums and bass, as well as additional musical elements such as piano and strings. Step 3: The providing unit provides the music generated by the generating unit in real time. The providing unit provides the generated music to the user using streaming technology and plays it in real time with minimal delay. The generated music can also be recorded and played back later.

[0076] (Example 2) A music generation system according to an embodiment of the present invention inputs a user's performance and uses a generation AI to generate and provide music in real time. The music generation system inputs the user's performance and uses a generation AI to generate music based on the performance and provide it in real time. For example, a user inputs an instrumental performance or vocals into the music generation system. For example, the user can record their own performance or singing and upload it to a platform. The music generation system then uses a generation AI to generate music in real time based on the user's input. The generation AI generates appropriate accompaniment or additional musical elements to match the user's performance or singing. For example, if a user plays guitar, the generation AI generates drum and bass accompaniment. The generated music is combined with the user's performance to create a real-time jam session. By playing along with the music generated by the generation AI, users can enjoy the experience of playing as if they were part of a band. Furthermore, the music generation system allows users to post their creations on the platform and share them with other users. This allows users to receive feedback from other users and create new collaboration opportunities. For example, other users may add additional parts to their own performances, resulting in the creation of a new song. This allows the music generation system to enable users to easily create high-quality music and collaborate with other users to produce more creative works. This allows the music generation system to enable users to easily create high-quality music and collaborate with other users to produce more creative works. For example, a user can input an instrument or vocal performance and the generative AI can generate and provide music in real time, allowing the user to enjoy the experience of playing as if they were part of a band. In addition, users can post the works they create on the platform and share them with other users, creating new opportunities for collaboration.

[0077] A music generation system according to an embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit inputs a user's performance. The user's performance may include, but is not limited to, an instrumental performance, vocals, digital music, etc. For example, the receiving unit allows the user to record their own performance or singing and upload it to the platform. The receiving unit may also allow the user to input the performance in real time. The generating unit generates music based on the performance input by the receiving unit using a generation AI. The generating unit generates, for example, accompaniment or additional musical elements to match the user's performance. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates appropriate music based on the user's performance. For example, if the user is playing guitar, the generating unit generates a drum or bass accompaniment. The generating unit may also generate additional musical elements, such as piano or strings, to match the user's performance. The providing unit provides the music generated by the generating unit in real time. For example, the providing unit provides the generated music to the user using streaming technology. The providing unit minimizes latency, allowing the user to enjoy music in real time. For example, the providing unit may stream the generated music to a user's device and play it in real time. Alternatively, the providing unit may record the generated music and play it later. This allows the music generation system according to the embodiment to generate and provide music in real time based on the user's performance.

[0078] The generation unit can generate accompaniment or additional musical elements in accordance with the user's performance. For example, the generation unit generates a drum or bass accompaniment in accordance with the user's performance. The generation unit can also generate additional musical elements such as piano or strings in accordance with the user's performance. Furthermore, the generation unit can generate musical elements such as a synthesizer or percussion in accordance with the user's performance. This makes it possible to generate appropriate accompaniment or musical elements in accordance with the user's performance. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's performance data into the generation AI and cause the generation AI to generate the accompaniment or additional musical elements.

[0079] The providing unit can provide the generated music to the user in real time. The providing unit, for example, provides the generated music to the user using streaming technology. The providing unit minimizes delay time, allowing the user to enjoy the music in real time. For example, the providing unit streams the generated music to the user's device and plays it in real time. The providing unit can also record the generated music and play it later. In this way, by providing the generated music in real time, the user can instantly enjoy the music. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the generated music data to AI and cause the AI ​​to provide the music in real time.

[0080] The music generation system includes a communication unit that supports communication between users. The communication unit supports communication between users. For example, the communication unit provides a chat function, allowing users to exchange messages in real time. The communication unit also provides a video call function, allowing users to communicate face-to-face. The communication unit also provides a messaging function, allowing users to send and receive messages asynchronously. This supports communication between users, thereby creating new collaborations. For example, users can share ideas through the chat function and jointly create new music. Furthermore, the video call function allows users to exchange opinions in real time and create more creative works. This allows the music generation system to support communication between users, thereby creating new collaborations.

[0081] The music generation system includes a management unit that manages the generative AI's algorithm. The management unit manages the generative AI's algorithm. For example, the management unit manages the generative AI's learning data to improve the accuracy of the algorithm. The management unit can also update the generative AI's algorithm and incorporate the latest technology. Furthermore, the management unit can monitor the performance of the generative AI and make adjustments as necessary. In this way, by managing the generative AI's algorithm, the accuracy of the system can be improved. For example, the management unit regularly updates the generative AI's learning data to respond to the latest music trends. The management unit can also optimize the generative AI's algorithm to generate higher quality music. In this way, the music generation system manages the generative AI's algorithm and improves the system's accuracy.

[0082] The communication unit may provide a function that allows users to post works they have created and share them with other users. For example, the communication unit may provide a function that allows users to post works they have created on the platform. Users can share their performances and musical compositions with other users. The communication unit may also provide a function that allows users to receive feedback from other users. Users can improve their works through comments and ratings from other users. Furthermore, the communication unit may provide a function that supports collaboration between users. Users can create musical compositions in collaboration with other users. By sharing works created by users with other users, users can receive feedback and find new opportunities for collaboration. For example, a new musical composition may be created when a user posts their performance and another user adds additional parts to it. This allows the music generation system to share works created by users with other users and create new collaborations.

[0083] The management unit can manage the generative AI's learning data and algorithms. For example, the management unit can manage the generative AI's learning data to improve the accuracy of the algorithm. The management unit can also update the generative AI's algorithm and incorporate the latest technology. Furthermore, the management unit can monitor the generative AI's performance and make adjustments as necessary. In this way, by managing the generative AI's learning data and algorithms, the accuracy of the system can be improved. For example, the management unit can regularly update the generative AI's learning data to keep up with the latest music trends. In addition, the management unit can optimize the generative AI's algorithm to generate higher quality music. In this way, the music generation system can manage the generative AI's learning data and algorithms to improve the system's accuracy.

[0084] The reception unit can estimate the user's emotions and adjust the timing of the performance input based on the estimated user emotions. For example, if the user is nervous, the reception unit can have the AI ​​delay the timing of the performance input to allow the user to relax. Furthermore, if the user is relaxed, the reception unit can also have the AI ​​advance the timing of the performance input to promote a smooth session. Furthermore, if the user is excited, the reception unit can have the AI ​​adjust the timing of the performance input to realize an energetic session. This allows for a more appropriate performance experience by adjusting the timing of the performance input according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0085] The reception unit can analyze the user's past performance history and select the optimal input method. For example, the reception unit can prioritize and suggest input methods for instruments or vocals that the user has frequently used in the past. The reception unit can also suggest input methods suitable for a specific genre based on the user's past performance history. Furthermore, the reception unit can analyze the user's past performance history and suggest the input method of the most successful session. In this way, the optimal input method can be provided by analyzing the user's past performance history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past performance data into the generation AI and cause the generation AI to select the optimal input method.

[0086] The reception unit can perform filtering based on the user's current musical style and preferences when inputting a performance. The reception unit can, for example, suggest appropriate accompaniments based on the user's currently preferred musical style. The reception unit can also filter and suggest specific instruments and timbres based on the user's current preferences. Furthermore, the reception unit can also suggest optimal tempos and rhythms based on the user's current musical style. This allows for filtering based on the user's current musical style and preferences, thereby providing a more appropriate performance experience. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's musical style data into a generation AI and have the generation AI perform filtering.

[0087] When inputting a performance, the reception unit can select an appropriate input means depending on the user's input method. For example, if the user inputs the performance by voice, the reception unit can have the AI ​​process the input using voice recognition technology. Furthermore, if the user inputs the performance by text, the reception unit can also have the AI ​​process the input using text analysis technology. Furthermore, if the user inputs the performance by image, the reception unit can also have the AI ​​process the input using image recognition technology. This allows for a more appropriate performance experience by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's input data to the generation AI and have the generation AI select the optimal input means.

[0088] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user emotions. For example, if the user is nervous, the reception unit can provide an interface with subdued colors to reduce visual stress. Furthermore, if the user is having fun, the reception unit can provide an interface with bright colors to make input tasks more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple, highly visible interface to make input tasks easier. This allows the input interface design to be adjusted according to the user's emotions, providing a more appropriate playing experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the interface design.

[0089] The reception unit can suggest an optimal input method by referring to the user's past performance history. For example, the reception unit can prioritize suggesting input methods for instruments or vocals that the user has frequently used in the past. The reception unit can also suggest input methods suitable for a specific genre based on the user's past performance history. Furthermore, the reception unit can analyze the user's past performance history and suggest the input method of the most successful session. In this way, the optimal input method can be provided by referring to the user's past performance history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past performance data into the generation AI and cause the generation AI to suggest an optimal input method.

[0090] The reception unit can suggest an optimal input method based on the user's current musical style and preferences. The reception unit can suggest an appropriate accompaniment based on, for example, the user's currently preferred musical style. The reception unit can also filter and suggest specific instruments and timbres based on the user's current preferences. Furthermore, the reception unit can suggest optimal tempos and rhythms based on the user's current musical style. This makes it possible to provide a more appropriate playing experience by suggesting an optimal input method based on the user's current musical style and preferences. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's musical style data into a generation AI and cause the generation AI to suggest an optimal input method.

[0091] The reception unit can select an appropriate input means depending on the user's input method. For example, when the user inputs a performance by voice, the reception unit uses AI to process the input using voice recognition technology. Furthermore, when the user inputs a performance by text, the reception unit can also use AI to process the input using text analysis technology. Furthermore, when the user inputs a performance by image, the reception unit can also use AI to process the input using image recognition technology. This allows for a more appropriate performance experience by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's input data to the generation AI and have the generation AI select the optimal input means.

[0092] The generation unit can estimate the user's emotion and adjust the expression method of the generated music based on the estimated user's emotion. For example, if the user is relaxed, the generation unit can generate music with a slow tempo. Furthermore, if the user is excited, the generation unit can generate music with an energetic rhythm. Furthermore, if the user is sad, the generation unit can generate a melody that reflects the emotion. This allows for the generation of more appropriate music by adjusting the expression method of the music according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the expression method of the music.

[0093] When generating music, the generation unit can adjust the specificity of the generation based on the importance of the performance. For example, the generation unit generates a detailed accompaniment for an important section. The generation unit can also generate a simple accompaniment when used as background music. Furthermore, the generation unit can generate a complex melody line for a solo part. In this way, by adjusting the specificity of the generation based on the importance of the performance, more appropriate music can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input performance data to the generation AI and cause the generation AI to adjust the specificity of the generation.

[0094] When generating music, the generation unit can apply different generation algorithms depending on the performance category. For example, in the case of jazz performance, the generation unit applies an improvisational performance algorithm. In addition, in the case of classical music, the generation unit can also apply a strict score generation algorithm. Furthermore, in the case of pop music, the generation unit can also apply a catchy melody generation algorithm. In this way, by applying different generation algorithms depending on the performance category, more appropriate music can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input performance data to the generation AI and cause the generation AI to apply a generation algorithm depending on the category.

[0095] When generating music, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit adjusts the generation algorithm based on, for example, the user's favorite musical styles in the past. The generation unit can also improve the generation accuracy by learning successful patterns from the user's past generation results. Furthermore, the generation unit can also optimize the generation algorithm by reflecting the user's past feedback. In this way, the generation accuracy can be improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past generation data into the generation AI and cause the generation AI to improve the generation accuracy.

[0096] The generation unit can estimate the user's emotion and adjust the length of the music to be generated based on the estimated user's emotion. For example, the generation unit can generate longer music when the user is relaxed. The generation unit can also generate shorter music when the user is in a hurry. Furthermore, the generation unit can generate music of an appropriate length when the user is concentrating. This allows for more appropriate music to be generated by adjusting the length of the music according to the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the music.

[0097] When generating music, the generation unit can determine the order of generation based on the submission dates of the performances. For example, the generation unit can generate music with priority for performances with an approaching deadline. The generation unit can also generate music with priority for performances submitted earlier. Furthermore, the generation unit can postpone generating music for performances submitted later. In this way, by determining the order of generation based on the submission dates of the performances, music can be generated more efficiently. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input performance data to the generation AI and have the generation AI determine the order of generation.

[0098] When generating music, the generation unit can adjust the order of generation based on the relevance of the performance. For example, the generation unit prioritizes generating music that is highly relevant to the user's performance. The generation unit can also postpone generating music that is less relevant to the user's performance. Furthermore, the generation unit can prioritize generating music of a genre that is more relevant to the user's performance. In this way, by adjusting the order of generation based on the relevance of the performance, more appropriate music can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input performance data to the generation AI and cause the generation AI to adjust the order of generation based on the relevance.

[0099] The generation unit can adjust the application of technical terminology during music generation according to the user's level of expertise. For example, the generation unit generates music using simple technical terminology for a beginner user. The generation unit can also generate music using moderate technical terminology for an intermediate user. Furthermore, the generation unit can generate music using detailed technical terminology for an advanced user. This allows for the generation of more appropriate music by adjusting the use of technical terminology in the generation according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's technical knowledge data into the generation AI and cause the generation AI to adjust the application of technical terminology.

[0100] The providing unit can estimate the user's emotions and adjust the display method of the music to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. This allows for a more appropriate music experience by adjusting the music display method according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0101] When providing music, the providing unit can select the optimal providing method by referring to the user's past operation history. The providing unit selects the optimal providing method, for example, based on a display method that the user has previously preferred. The providing unit can also select the most effective providing method from the user's past operation history. Furthermore, the providing unit can also optimize the providing method by reflecting the user's past feedback. In this way, the optimal providing method can be provided by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's operation history data to the generating AI and cause the generating AI to select the optimal providing method.

[0102] When providing music, the providing unit can customize the content to be provided according to the user's current task. For example, when the user is working, the providing unit can provide music that enhances concentration. Furthermore, when the user is relaxing, the providing unit can also provide music that has a relaxing effect. Furthermore, when the user is exercising, the providing unit can also provide energetic music. In this way, by customizing the content to be provided according to the user's current task, a more appropriate music experience can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's task data into a generation AI and cause the generation AI to customize the content to be provided.

[0103] The providing unit can improve the providing method by reflecting user feedback when providing music. For example, when a user provides feedback on the provided music, the providing unit allows the AI ​​to improve the providing method based on that feedback. The providing unit can also analyze the user's feedback and suggest an optimal providing method. Furthermore, the providing unit can also customize the provided content by reflecting user feedback. In this way, by reflecting user feedback, the providing method can be improved and a more appropriate music experience can be provided. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into the generating AI and cause the generating AI to improve the providing method.

[0104] The providing unit can estimate the user's emotions and adjust the operation procedures for the music to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide simple and intuitive operation procedures. Furthermore, if the user is relaxed, the providing unit can provide detailed operation procedures. Furthermore, if the user is in a hurry, the providing unit can provide procedures that allow for quick operation. This allows for adjusting the operation procedures according to the user's emotions to provide a more appropriate music experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the operation procedures.

[0105] When providing music, the providing unit can select an appropriate delivery method based on the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. This allows for a more appropriate music experience by selecting the optimal delivery method based on the user's device information. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal delivery method.

[0106] When providing music, the providing unit can make the provided content multilingual according to the user's language setting. The providing unit, for example, automatically sets explanations and operation procedures for the music to be provided based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can display the provided music content in that language. This makes it possible to provide a more appropriate music experience by making the provided content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data into the generation AI and cause the generation AI to perform multilingual settings.

[0107] The providing unit can adjust the providing method by reflecting the user's past feedback when providing music. The providing unit customizes the providing method based on, for example, feedback provided by the user on music provided in the past. The providing unit can also analyze the user's past feedback and suggest an optimal providing method. Furthermore, the providing unit can customize the content of the provided music by reflecting the user's past feedback. In this way, by reflecting the user's past feedback, the providing method can be customized and a more appropriate music experience can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's feedback data into a generating AI and cause the generating AI to adjust the providing method.

[0108] The communication unit can estimate the user's emotions and adjust the communication method based on the estimated user's emotions. For example, if the user is nervous, the communication unit can communicate in a calm tone. If the user is relaxed, the communication unit can also communicate in a friendly tone. If the user is excited, the communication unit can also communicate in an energetic tone. This allows for adjusting the communication method according to the user's emotions, thereby providing more appropriate communication. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the communication unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the communication unit can input the user's emotion data into the generation AI and have the generation AI adjust the communication method.

[0109] The communication unit can select the optimal method by referring to the user's past communication history when communicating. For example, the communication unit selects the optimal method based on the user's past preferred communication methods. The communication unit can also select the most effective method from the user's past communication history. Furthermore, the communication unit can also optimize the communication method by reflecting the user's past feedback. In this way, the optimal communication method can be provided by referring to the user's past communication history. Some or all of the above-mentioned processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input the user's communication history data into a generation AI and have the generation AI select the optimal method.

[0110] The communication unit can customize content based on the user's current interests during communication. The communication unit customizes communication content based on, for example, topics in which the user is currently interested. The communication unit can also provide related information based on the user's current interests. Furthermore, the communication unit can suggest an optimal communication method based on the user's current interests. This allows for more appropriate communication by customizing content based on the user's current interests. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input user interest data into a generation AI and have the generation AI customize the content.

[0111] The communication unit can improve the method by reflecting user feedback during communication. For example, the communication unit improves the method based on feedback provided by the user regarding the communication. The communication unit can also analyze the user's feedback and suggest an optimal communication method. Furthermore, the communication unit can also customize the content of the communication by reflecting the user's feedback. In this way, by reflecting the user's feedback, the communication method can be improved and more appropriate communication can be provided. Some or all of the above-mentioned processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input user feedback data into a generation AI and have the generation AI execute improvements to the method.

[0112] The communication unit can estimate the user's emotions and determine communication priorities based on the estimated user emotions. For example, the communication unit prioritizes communication when the user is nervous. Furthermore, the communication unit can also prioritize communication when the user is relaxed. Furthermore, the communication unit can also prioritize communication when the user is excited. This allows for more appropriate communication by determining communication priorities according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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-described processing in the communication unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the communication unit can input the user's emotion data into the generation AI and have the generation AI determine the priorities.

[0113] The communication unit can select an appropriate method based on the user's geographical location information when communicating. For example, if the user is in a specific area, the communication unit provides information related to that area. The communication unit can also select an optimal communication method based on the user's geographical location information. Furthermore, the communication unit can provide information on related events and places taking the user's geographical location information into consideration. This allows for more appropriate communication by selecting an optimal method based on the user's geographical location information. Some or all of the above-described processing in the communication unit may be performed using, or without, AI, for example. For example, the communication unit can input the user's location information data into a generation AI and have the generation AI select an optimal method.

[0114] The communication unit can analyze the user's social media activity and provide relevant content during communication. For example, the communication unit can provide information about places where the user has checked in on social media. The communication unit can also analyze the content of the user's social media posts and provide relevant information. Furthermore, the communication unit can provide relevant information by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, relevant content can be provided and more appropriate communication can be provided. Some or all of the above-described processing in the communication unit can be performed, for example, using AI or without AI. For example, the communication unit can input the user's social media data into a generation AI and cause the generation AI to provide relevant content.

[0115] The communication unit can adjust the communication method by reflecting the user's past feedback. For example, the communication unit customizes the communication method based on the user's past feedback. The communication unit can also analyze the user's past feedback and suggest an optimal communication method. Furthermore, the communication unit can customize the communication content by reflecting the user's past feedback. In this way, by reflecting the user's past feedback, the communication method can be customized and more appropriate communication can be provided. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input the user's feedback data into a generation AI and have the generation AI adjust the method.

[0116] The management unit can estimate the user's emotions and select data to manage based on the estimated user emotions. For example, if the user is nervous, the management unit can prioritize managing data with a relaxing effect. Furthermore, if the user is relaxed, the management unit can also manage normal data. Furthermore, if the user is excited, the management unit can prioritize managing energetic data. This allows for more appropriate data management by selecting data to manage based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the management unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the management unit can input the user's emotion data into the generation AI and have the generation AI select the data.

[0117] During management, the management unit can improve the management algorithm by referring to past data. For example, the management unit analyzes past data and selects an optimal management algorithm. The management unit can also adjust the management algorithm based on past data. Furthermore, the management unit can improve the accuracy of the management algorithm by referring to past data. In this way, by referring to past data, the management algorithm can be optimized and more appropriate data management can be provided. Some or all of the above-mentioned processing in the management unit may be performed, for example, using AI or without AI. For example, the management unit can input past data into a generation AI and have the generation AI improve the management algorithm.

[0118] The management unit can update the data by reflecting user feedback during management. The management unit updates the data based on, for example, feedback provided by the user. The management unit can also analyze the user feedback and propose an optimal data update method. Furthermore, the management unit can customize the data by reflecting user feedback. This allows the data to be updated by reflecting user feedback, thereby providing more appropriate data management. Some or all of the above-described processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can input user feedback data into a generation AI and have the generation AI update the data.

[0119] During management, the management unit can select the optimal management method by referring to the user's past operation history. The management unit selects the optimal management method based on, for example, the user's past operation history. The management unit can also analyze the user's past operation history and propose the most effective management method. Furthermore, the management unit can customize the management method by referring to the user's past operation history. In this way, the optimal management method can be provided by referring to the user's past operation history. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the user's operation history data into a generation AI and have the generation AI select the optimal management method.

[0120] The management unit can estimate the user's emotions and adjust the frequency of management based on the estimated user emotions. For example, if the user is nervous, the management unit reduces the frequency of management to reduce stress. Furthermore, if the user is relaxed, the management unit can perform management at a normal frequency. Furthermore, if the user is excited, the management unit can increase the frequency of management to provide energetic data. This allows for more appropriate data management by adjusting the frequency of management according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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-described processing in the management unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the management unit can input the user's emotion data into the generation AI and have the generation AI adjust the frequency of management.

[0121] During management, the management unit can set the priority of managed data based on the time of data submission. For example, the management unit can set a high weight for data submitted earlier. The management unit can also set a low weight for data submitted later. Furthermore, the management unit can adjust the weighting of data based on the time of submission. In this way, by weighting managed data based on the time of data submission, more appropriate data management can be provided. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input data submission time information to the generation AI and have the generation AI set the priority.

[0122] During management, the management unit can integrate information from different data sources to enrich the management data. For example, the management unit integrates information from different data sources to enrich the management data. The management unit can also analyze information from different data sources and propose an optimal management method. Furthermore, the management unit can customize the management data based on information from different data sources. In this way, by integrating information from different data sources, the management data can be enriched and more appropriate data management can be provided. Some or all of the above-described processing in the management unit may be performed, for example, using AI or without AI. For example, the management unit can input information from different data sources into a generation AI and have the generation AI integrate the data.

[0123] During management, the management unit can adjust the management method by reflecting the user's past feedback. The management unit, for example, customizes the management method based on feedback provided by the user in the past. The management unit can also analyze the user's past feedback and propose an optimal management method. Furthermore, the management unit can customize the management content by reflecting the user's past feedback. In this way, by reflecting the user's past feedback, the management method can be customized and more appropriate data management can be provided. Some or all of the above-mentioned processing in the management unit may be performed, for example, using AI or without AI. For example, the management unit can input the user's feedback data into a generation AI and have the generation AI adjust the management method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, communication unit, and management unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit inputs a user's performance via the control unit 46A of the smart device 14, and the generation unit generates music using a generation AI via the specific processing unit 290 of the data processing device 12. The provision unit streams the generated music to the smart device 14 and plays it in real time. The communication unit supports communication between users using the chat function and video call function of the smart device 14. The management unit manages the algorithm of the generation AI via the specific processing unit 290 of the data processing device 12 and adjusts the frequency of data selection and management based on the user's emotions. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, communication unit, and management unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit inputs a user's performance via the control unit 46A of the smart glasses 214, and the generation unit generates music using a generation AI via the specific processing unit 290 of the data processing device 12. The provision unit streams the generated music to the smart glasses 214 and plays it in real time. The communication unit supports communication between users using the chat function and video call function of the smart glasses 214. The management unit manages the algorithm of the generation AI via the specific processing unit 290 of the data processing device 12 and adjusts the frequency of data selection and management based on the user's emotions. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, communication unit, and management unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit inputs a user's performance via the control unit 46A of the headset type terminal 314, and the generation unit generates music using a generation AI via the specific processing unit 290 of the data processing device 12. The provision unit streams the generated music to the headset type terminal 314 and plays it in real time. The communication unit supports communication between users using the chat function and video call function of the headset type terminal 314. The management unit manages the algorithm of the generation AI via the specific processing unit 290 of the data processing device 12 and adjusts the frequency of data selection and management based on the user's emotions. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, communication unit, and management unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit inputs a user's performance via the control unit 46A of the robot 414, and the generation unit generates music using a generation AI via the specific processing unit 290 of the data processing device 12. The provision unit streams the generated music to the robot 414 and plays it in real time. The communication unit supports communication between users using the chat function and video call function of the robot 414. The management unit manages the algorithm of the generation AI via the specific processing unit 290 of the data processing device 12 and adjusts the frequency of data selection and management based on the user's emotions.

[0124] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0125] When inputting a user's performance, the reception unit can analyze the user's past performance data and suggest the optimal input method. For example, the reception unit can prioritize the suggestion of an input method for an instrument or vocal that the user has frequently used in the past. The reception unit can also suggest an input method suitable for a specific genre based on the user's past performance history. Furthermore, the reception unit can analyze the user's past performance history and suggest the input method of the most successful session. In this way, the optimal input method can be provided by analyzing the user's past performance history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past performance data into the generation AI and cause the generation AI to select the optimal input method.

[0126] The generation unit can estimate the user's emotion and adjust the expression method of the generated music based on the estimated user's emotion. For example, if the user is relaxed, the generation unit can generate music with a slow tempo. Furthermore, if the user is excited, the generation unit can generate music with an energetic rhythm. Furthermore, if the user is sad, the generation unit can generate a melody that reflects the emotion. This allows for more appropriate music to be generated by adjusting the expression method of the music according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the expression method of the music.

[0127] When providing music, the providing unit can select the optimal providing method by referring to the user's past operation history. For example, the optimal providing method is selected based on the user's preferred display method in the past. The providing unit can also select the most effective providing method from the user's past operation history. Furthermore, the providing unit can also optimize the providing method by reflecting the user's past feedback. In this way, the optimal providing method can be provided by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's operation history data into the generating AI and cause the generating AI to select the optimal providing method.

[0128] The communication unit can estimate the user's emotions and adjust the communication method based on the estimated user's emotions. For example, if the user is nervous, the communication unit can communicate in a calm tone. If the user is relaxed, the communication unit can also communicate in a friendly tone. If the user is excited, the communication unit can also communicate in an energetic tone. This allows for adjusting the communication method according to the user's emotions, thereby providing more appropriate communication. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the communication unit can be performed using, for example, AI, or without AI. For example, the communication unit can input the user's emotion data into the generation AI and have the generation AI adjust the communication method.

[0129] The management unit can estimate the user's emotions and select data to manage based on the estimated user emotions. For example, if the user is nervous, data with a relaxing effect is preferentially managed. Furthermore, if the user is relaxed, the management unit can also manage normal data. Furthermore, if the user is excited, the management unit can also manage energetic data preferentially. This allows for more appropriate data management by selecting data to manage based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the management unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the management unit can input the user's emotion data into the generation AI and have the generation AI select the data.

[0130] The reception unit can perform filtering based on the user's current musical style and preferences. For example, the reception unit can suggest appropriate accompaniments based on the user's current musical style. The reception unit can also filter and suggest specific instruments and timbres based on the user's current preferences. Furthermore, the reception unit can also suggest optimal tempos and rhythms based on the user's current musical style. This allows for filtering based on the user's current musical style and preferences, thereby providing a more appropriate performance experience. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's musical style data into a generation AI and have the generation AI perform the filtering.

[0131] When generating music, the generation unit can adjust the specificity of the generation based on the importance of the performance. For example, for important sections, the generation unit can generate a detailed accompaniment. The generation unit can also generate a simple accompaniment when used as background music. Furthermore, the generation unit can generate a complex melody line for a solo part. In this way, by adjusting the specificity of the generation based on the importance of the performance, more appropriate music can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input performance data to the generation AI and have the generation AI adjust the specificity of the generation.

[0132] When providing music, the providing unit can customize the content to be provided according to the user's current task. For example, if the user is working, music that enhances concentration is provided. The providing unit can also provide music with a relaxing effect if the user is relaxing. Furthermore, the providing unit can also provide energetic music if the user is exercising. This allows the user to customize the content to be provided according to the user's current task, thereby providing a more appropriate music experience. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's task data into a generation AI and cause the generation AI to customize the content to be provided.

[0133] The communication unit can select the optimal method by referring to the user's past communication history when communicating. For example, the optimal method is selected based on the user's preferred communication methods in the past. The communication unit can also select the most effective method from the user's past communication history. Furthermore, the communication unit can optimize the communication method by reflecting the user's past feedback. In this way, the optimal communication method can be provided by referring to the user's past communication history. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input the user's communication history data into a generation AI and have the generation AI select the optimal method.

[0134] During management, the management unit can integrate information from different data sources to enrich the management data. For example, the management unit integrates information from different data sources to enrich the management data. The management unit can also analyze information from different data sources and propose an optimal management method. Furthermore, the management unit can customize the management data based on information from different data sources. In this way, by integrating information from different data sources, the management data can be enriched and more appropriate data management can be provided. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input information from different data sources into a generation AI and have the generation AI integrate the data.

[0135] The processing flow of the second embodiment will be briefly explained below.

[0136] Step 1: The reception unit inputs the user's performance. The user's performance can include instrumental performance, vocals, digital music, etc. The reception unit allows the user to record their own performance or singing voice and upload it to the platform. The user can also input their performance in real time. Step 2: The generator uses the generation AI to generate music based on the performance input by the receiver. The generator generates accompaniment and additional musical elements to match the user's performance. For example, if the user is playing guitar, the generator can generate accompaniment for drums and bass, as well as additional musical elements such as piano and strings. Step 3: The providing unit provides the music generated by the generating unit in real time. The providing unit provides the generated music to the user using streaming technology and plays it in real time with minimal delay. The generated music can also be recorded and played back later.

[0137] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0138] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 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.

[0143] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0158] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0159] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0164] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0174] 7, a 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.

[0175] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0176] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0177] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0179] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0180] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0181] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0182] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0183] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0184] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0185] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0186] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0187] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0188] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0189] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0190] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0191] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0192] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0193] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0194] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0195] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0196] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0197] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0198] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0199] 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.

[0200] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0201] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0202] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0203] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0204] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0205] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0206] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0207] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0208] [Explanation of symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting a user's performance; a generation unit that generates music based on the performance input by the reception unit; a providing unit that provides the music generated by the generating unit in real time; Equipped with A system characterized by:

2. The generation unit Generate accompaniment or additional musical elements to accompany the user's performance 2. The system of claim 1.

3. The providing unit Providing generated music to users in real time 2. The system of claim 1.

4. Equipped with a communication section that supports communication between users 2. The system of claim 1.

5. Equipped with a management unit that manages the algorithm of the generated AI 2. The system of claim 1.

6. The communication unit Providing the ability for users to post and share their creations with other users 4. The system of claim 3.

7. The management unit Manage generative AI learning data and algorithms 5. The system of claim 4.

8. The reception unit Estimating the user's emotions and adjusting the timing of performance input based on the estimated user emotions 2. The system of claim 1.

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