system

Abstract

The system according to this embodiment aims to generate music in real time that is tailored to the user's musical preferences and technical level. [Solution] The system according to this embodiment comprises an analysis unit, a reception unit, a generation unit, and a provision unit. The analysis unit analyzes the user's musical preferences and skill level. The reception unit receives the user's ideas for performance and composition. The generation unit generates a musical piece based on the ideas received by the reception unit. The provision unit provides the musical piece generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to generate music in real time according to a user's musical preference and technical level.

[0005] The system according to the embodiment aims to generate music in real time according to a user's musical preference and technical level.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a reception unit, a generation unit, and a provision unit. The analysis unit analyzes the user's musical preferences and skill level. The reception unit receives the user's ideas for performance and composition. The generation unit generates a musical piece based on the ideas received by the reception unit. The provision unit provides the musical piece generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can generate music in real time that is tailored to the user's musical preferences and technical level. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The music co-creation system according to an embodiment of the present invention is a system that analyzes a user's musical preferences and skill level and co-creates original music in real time. When a user provides ideas for performance or composition, the AI ​​develops these ideas and generates a high-quality musical piece. For example, the music co-creation system first analyzes the user's musical preferences and skill level. Next, the AI ​​receives the performance or composition ideas provided by the user and generates music based on them. The AI ​​uses an algorithm that combines music theory and machine learning to dynamically generate music based on the user's input (melody, rhythm). Furthermore, the AI ​​continuously learns and improves the personalization of the music to match the user's preferences. This system reduces the barriers to music creation and can accommodate users of all skill levels. It also provides an interactive experience that stimulates the user's creativity, realizing a fusion of music education and entertainment. Thus, the music co-creation system can generate and provide music based on the user's musical preferences and skill level.

[0029] The music co-creation system according to this embodiment comprises an analysis unit, a reception unit, a generation unit, and a provision unit. The analysis unit analyzes the user's musical preferences and skill level. For example, the analysis unit analyzes the user's preferred music genres, tempos, melodies, etc. The analysis unit can also evaluate the user's skill level using methods for evaluating performance and composition skills. The reception unit receives the user's performance and composition ideas. For example, the reception unit receives melody fragments and rhythm patterns provided by the user. The reception unit can also clarify the format and content of the ideas input by the user. The generation unit generates music based on the ideas received by the reception unit. For example, the generation unit generates music using an algorithm that combines music theory and machine learning. The generation unit can also dynamically generate music based on user input (melody, rhythm). The generation unit can also continuously learn and improve the personalization of music to match the user's preferences. The provision unit provides the music generated by the generation unit. For example, the provision unit provides an interactive experience that stimulates the user's creativity. The provision unit can also clarify how to provide the generated music to the user. As a result, the music co-creation system according to this embodiment can generate and provide music based on the user's musical preferences and technical level.

[0030] The analysis unit analyzes the user's musical preferences and skill level. Specifically, it analyzes the user's listening history and playlists to identify preferred music genres, tempos, and melodic patterns. For example, if a user prefers a specific genre such as rock, jazz, or classical music, it extracts characteristics associated with that genre. Regarding tempo preferences, it analyzes the user's preferred beat speed and rhythmic complexity. Regarding melodic preferences, it identifies the user's preferred scales and chord progressions. Furthermore, it evaluates the user's skill level using methods for evaluating performance and composition skills. For example, it analyzes recordings of performances and scores of compositions provided by the user to evaluate the accuracy and expressiveness of the performance, as well as the structure and creativity of the composition. This allows the analysis unit to gain a detailed understanding of the user's musical preferences and skill level, and provide the information necessary for the next step, music production.

[0031] The reception desk receives users' ideas for performances and compositions. Specifically, it receives melody fragments and rhythm patterns provided by users. For example, users can input melody fragments recorded using a smartphone or tablet, or drum patterns. The reception desk receives these ideas in digital format and makes them available for processing within the system. It can also provide guidelines and templates to clarify the format and content of the ideas that users input. For example, by setting rules such as melody fragments needing to follow a specific length or scale, the reception desk can efficiently receive user ideas. This allows the reception desk to accurately receive users' creative ideas and provide the information necessary for the next step, song generation.

[0032] The generation unit generates music based on ideas received by the reception unit. Specifically, it generates music using an algorithm that combines music theory and machine learning. For example, it can complete a song by adding chord progressions and rhythmic patterns based on a melody fragment provided by the user. The generation unit can also dynamically generate music based on user input (melody, rhythm). For example, it can automatically generate an appropriate accompaniment for a melody entered by the user, completing it as a whole song. Furthermore, the generation unit can continuously learn and improve the personalization of music to match the user's preferences. For example, by providing feedback on the generated music, the generation unit learns from that feedback and incorporates it into the next music generation. In this way, the generation unit can generate music that matches the user's preferences and skill level, supporting the user's creativity.

[0033] The provider unit provides the music generated by the generation unit. Specifically, it provides an interactive experience that stimulates user creativity. For example, it provides an interface that allows users to play the generated music in real time and edit or arrange it on the spot. It also clarifies how the generated music is provided to users. For example, it can provide the generated music in a downloadable format or save it to the cloud for access at any time. It can also provide a function to directly upload the generated music to social media and music sharing platforms. In this way, the provider unit provides an environment in which users can freely use and share the generated music. Furthermore, the provider unit can collect user feedback and use it to improve the overall system. For example, it can collect ratings and comments that users provide on the generated music and use that to improve the system's algorithms and interface. In this way, the provider unit can provide users with a high-quality music generation experience and improve the overall performance of the system.

[0034] The generation unit can generate music using algorithms that combine music theory and machine learning. The generation unit applies music theory such as harmony theory and counterpoint. The generation unit can also use machine learning algorithms such as neural networks and support vector machines. By combining music theory and machine learning, the generation unit can improve the accuracy of music generation. This improves the accuracy of music generation by using algorithms that combine music theory and machine learning. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input an algorithm that combines music theory and machine learning into a generation AI and have the generation AI perform music generation.

[0035] The generation unit can dynamically generate music based on user input. For example, the generation unit can generate music based on melodies and rhythms provided by the user. The generation unit can also analyze user input and dynamically generate music. The generation unit can also adjust the components of the music based on user input. This allows the generation unit to provide music that suits the user's preferences by dynamically generating music based on user input. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user input data into a generation AI and have the generation AI perform music generation.

[0036] The generation unit can continuously learn and improve the personalization of music to match user preferences. The generation unit can continuously learn, for example, using online learning or a feedback loop. The generation unit can also receive user feedback and improve its music generation algorithm. The generation unit can also improve music personalization based on user preferences. As a result, continuous learning improves the personalization of music to match user preferences. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user feedback data into a generation AI and have the generation AI perform improvements to its music generation algorithm.

[0037] The service provider can offer interactive experiences that stimulate the user's creativity. For example, it can provide real-time feedback. The service provider can also offer interactive experiences through dialogue with the user. The service provider can also provide interfaces to stimulate the user's creativity. This enhances the enjoyment of music creation by providing interactive experiences that stimulate the user's creativity. Some or all of the above-described processes in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input dialogue data with the user into a generative AI and have the generative AI perform the provision of an interactive experience.

[0038] The analysis unit can analyze a user's past music history and predict changes in their preferences. For example, the analysis unit can analyze the genres and artists of songs the user has listened to in the past and predict changes in their preferences. The analysis unit can also analyze the trends in music a user listens to during specific seasons or events and predict changes in their preferences. The analysis unit can also analyze how often a user tries new music and predict changes in their preferences. In this way, by analyzing a user's past music history, it is possible to predict changes in their preferences and provide appropriate music. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the user's past music history data into a generative AI and have the generative AI perform the prediction of changes in preferences.

[0039] The analysis unit can evaluate the user's performance skill progress in real time and reflect this in the analysis results. For example, the analysis unit can evaluate the user's skill level on the instrument they play in real time and reflect this in the analysis results. The analysis unit can also evaluate the frequency and duration of the user's practice in real time and reflect this in the analysis results. The analysis unit can also evaluate the speed at which the user acquires new skills in real time and reflect this in the analysis results. This allows the system to provide appropriate music by evaluating the user's performance skill progress in real time. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's performance skill data into a generative AI and have the generative AI perform an evaluation of the skill progress.

[0040] The analysis unit can analyze trends in musical preferences by region, taking into account the user's geographical location. For example, the analysis unit can analyze trends in musical preferences in the region where the user lives. The analysis unit can also analyze trends in musical preferences in regions the user has traveled to. The analysis unit can also analyze trends in musical preferences in regions the user has visited in the past. In this way, by taking into account the user's geographical location, trends in musical preferences by region can be analyzed. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's geographical location data into a generative AI and have the generative AI perform an analysis of trends in musical preferences by region.

[0041] The analysis unit can analyze users' social media activity and understand trends in their musical preferences. For example, the analysis unit can analyze songs that users share on social media to understand trends in their musical preferences. The analysis unit can also analyze artists and bands that users follow to understand trends in their musical preferences. The analysis unit can also analyze music-related communities that users participate in to understand trends in their musical preferences. In this way, trends in musical preferences can be understood by analyzing users' social media activity. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input user social media data into generative AI and have the generative AI perform an analysis of trends in musical preferences.

[0042] The reception department can analyze a user's past idea submission history and select the optimal timing for submission. For example, the reception department can analyze the time periods when a user has previously submitted ideas and select the optimal timing. The reception department can also analyze the frequency with which a user has previously submitted ideas and select the optimal timing. The reception department can also analyze the circumstances under which a user has previously submitted ideas and select the optimal timing. In this way, the optimal timing for submission can be selected by analyzing a user's past idea submission history. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or without a generative AI. For example, the reception department can input the user's past idea submission history data into a generative AI and have the generative AI select the optimal timing for submission.

[0043] The reception unit can filter ideas based on the user's current projects and areas of interest. For example, the reception unit may prioritize receiving ideas related to the user's current projects. The reception unit may also prioritize receiving ideas related to the user's areas of interest. The reception unit may also prioritize receiving ideas related to ideas the user has submitted in the past. This allows the reception unit to receive highly relevant ideas by filtering them based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input the user's current project and area of ​​interest data into a generative AI and have the generative AI perform the idea filtering.

[0044] The reception desk can prioritize receiving highly relevant ideas by taking into account the user's geographical location. For example, the reception desk can prioritize receiving ideas related to the area where the user lives. The reception desk can also prioritize receiving ideas related to the area the user is traveling to. The reception desk can also prioritize receiving ideas related to the area the user has visited in the past. In this way, by taking into account the user's geographical location, highly relevant ideas can be prioritized. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's geographical location data into a generative AI and have the generative AI select highly relevant ideas.

[0045] The reception unit can analyze a user's social media activity and receive relevant ideas. For example, the reception unit can analyze ideas that a user has shared on social media and receive relevant ideas. The reception unit can also receive ideas related to artists or bands that a user follows. The reception unit can also receive ideas related to music-related communities that a user participates in. In this way, relevant ideas can be received by analyzing a user's social media activity. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input the user's social media data into a generative AI and have the generative AI select relevant ideas.

[0046] The generation unit can generate music by combining elements from different music genres based on user input. For example, the generation unit can generate music by combining elements of jazz and classical music based on a melody provided by the user. The generation unit can also generate music by combining elements of rock and electronica based on a rhythm provided by the user. The generation unit can also generate music by combining elements of pop and hip hop based on a melody and rhythm provided by the user. This allows for the generation of original music by combining elements from different music genres based on user input. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input user input data into a generation AI and have the generation AI generate music by combining elements from different music genres.

[0047] The generation unit can analyze the user's past song generation history and optimize the generation algorithm. For example, the generation unit can analyze the trends of songs the user has generated in the past and optimize the generation algorithm. The generation unit can also analyze the genre and style of songs the user has generated in the past and optimize the generation algorithm. The generation unit can also analyze the ratings of songs the user has generated in the past and optimize the generation algorithm. In this way, by analyzing the user's past song generation history, the generation algorithm can be optimized and more appropriate songs can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's past song generation history data into a generation AI and have the generation AI perform the optimization of the generation algorithm.

[0048] The generation unit can generate music that reflects the musical characteristics of each region, taking into account the user's geographical location information. For example, the generation unit can generate music that reflects the musical characteristics of the region where the user lives. The generation unit can also generate music that reflects the musical characteristics of the region the user is traveling to. The generation unit can also generate music that reflects the musical characteristics of the region the user has visited in the past. In this way, by taking into account the user's geographical location information, music that reflects the musical characteristics of each region can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's geographical location data into a generation AI and have the generation AI generate music that reflects the musical characteristics of each region.

[0049] The generation unit can analyze a user's social media activity and generate music that matches current trends. For example, the generation unit can analyze the trends of music shared by the user on social media and generate music. The generation unit can also analyze the trends of artists and bands that the user follows and generate music. The generation unit can also analyze the trends of music-related communities that the user participates in and generate music. In this way, by analyzing the user's social media activity, music that matches current trends can be generated. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's social media data into a generation AI and have the generation AI generate music that matches current trends.

[0050] The service provider can analyze a user's past music usage history and select the optimal service timing. For example, the service provider can analyze the time periods in which the user has used music in the past and select the optimal service timing. The service provider can also analyze the frequency in which the user has used music in the past and select the optimal service timing. The service provider can also analyze the circumstances in which the user has used music in the past and select the optimal service timing. In this way, the service provider can select the optimal service timing by analyzing the user's past music usage history. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input the user's past music usage history data into a generation AI and have the generation AI select the optimal service timing.

[0051] The service provider can customize the means of providing music based on the user's current activity. For example, if the user is exercising, the service provider can provide energetic music. If the user is relaxing, the service provider can also provide calming music. If the user is studying, the service provider can also provide music that enhances concentration. By customizing the means of providing music based on the user's current activity, more appropriate music can be provided. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input data on the user's current activity status into a generative AI and have the generative AI perform the customization of the means of providing music.

[0052] The service provider can provide music that reflects the musical characteristics of each region, taking into account the user's geographical location information. For example, the service provider can provide music that reflects the musical characteristics of the region where the user lives. The service provider can also provide music that reflects the musical characteristics of the region the user is traveling to. The service provider can also provide music that reflects the musical characteristics of the region the user has visited in the past. In this way, by taking into account the user's geographical location information, it is possible to provide music that reflects the musical characteristics of each region. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input the user's geographical location data into a generative AI and have the generative AI perform the task of providing music that reflects the musical characteristics of each region.

[0053] The service provider can analyze a user's social media activity and provide music that matches current trends. For example, it can analyze the trends of music shared by users on social media and provide music based on those trends. It can also analyze the trends of artists and bands that users follow and provide music based on those trends. It can also analyze the trends of music-related communities that users participate in and provide music based on those trends. In this way, by analyzing a user's social media activity, it can provide music that matches current trends. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not. For example, the service provider can input the user's social media data into a generative AI and have the generative AI perform the task of providing music that matches current trends.

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

[0055] The generation unit can create music by combining musical elements from different cultural spheres based on user input. For example, it can generate music that combines African beats and Asian melodies based on a melody provided by the user. It can also generate music that combines Latin music and Middle Eastern musical elements based on a rhythm provided by the user. Furthermore, it can generate music that combines European classical music and Caribbean musical elements based on a melody and rhythm provided by the user. This allows for the creation of unique music by combining musical elements from different cultural spheres based on user input.

[0056] The analytics department can analyze a user's past music history and predict changes in their preferences. For example, it can analyze the genres and artists of music a user has listened to in the past to predict changes in their preferences. It can also analyze the music a user listens to during specific seasons or events to predict changes in their preferences. It can also analyze how often a user tries new music to predict changes in their preferences. In this way, by analyzing a user's past music history, it can predict changes in their preferences and provide them with appropriate music.

[0057] The service provider can customize the way music is delivered based on the user's current activity. For example, if the user is exercising, energetic music can be provided. If the user is relaxing, calming music can be provided. If the user is studying, music that enhances concentration can be provided. By customizing the way music is delivered based on the user's current activity, more appropriate music can be provided.

[0058] The generation unit can create music by combining elements from different music genres based on user input. For example, it can generate music that combines jazz and classical elements based on a melody provided by the user. It can also generate music that combines rock and electronica elements based on a rhythm provided by the user. It can even generate music that combines pop and hip-hop elements based on a melody and rhythm provided by the user. This allows users to create unique music by combining elements from different music genres based on their input.

[0059] The analysis unit can analyze regional musical preference trends by taking into account the user's geographical location. For example, it can analyze the musical preference trends of the area where the user lives. It can also analyze the musical preference trends of areas the user has traveled to. It can also analyze the musical preference trends of areas the user has visited in the past. In this way, by taking into account the user's geographical location, it is possible to analyze regional musical preference trends.

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

[0061] Step 1: The analysis unit analyzes the user's musical preferences and skill level. For example, it analyzes the user's preferred music genres, tempos, and melodies, and evaluates their skill level using methods for assessing their performance and composition skills. Step 2: The reception desk receives the user's performance and composition ideas. For example, it receives melody fragments or rhythm patterns provided by the user and clarifies the format and content of the ideas the user will input. Step 3: The generation unit generates music based on the ideas received by the reception unit. For example, it generates music using an algorithm that combines music theory and machine learning, and dynamically generates music based on user input (melody, rhythm). The generation unit continuously learns and improves the personalization of the music to match the user's preferences. Step 4: The provider unit provides the music generated by the generator unit. For example, it provides an interactive experience that stimulates the user's creativity and clarifies how to deliver the generated music to the user.

[0062] (Example of form 2) The music co-creation system according to an embodiment of the present invention is a system that analyzes a user's musical preferences and skill level and co-creates original music in real time. When a user provides ideas for performance or composition, the AI ​​develops these ideas and generates a high-quality musical piece. For example, the music co-creation system first analyzes the user's musical preferences and skill level. Next, the AI ​​receives the performance or composition ideas provided by the user and generates music based on them. The AI ​​uses an algorithm that combines music theory and machine learning to dynamically generate music based on the user's input (melody, rhythm). Furthermore, the AI ​​continuously learns and improves the personalization of the music to match the user's preferences. This system reduces the barriers to music creation and can accommodate users of all skill levels. It also provides an interactive experience that stimulates the user's creativity, realizing a fusion of music education and entertainment. Thus, the music co-creation system can generate and provide music based on the user's musical preferences and skill level.

[0063] The music co-creation system according to this embodiment comprises an analysis unit, a reception unit, a generation unit, and a provision unit. The analysis unit analyzes the user's musical preferences and skill level. For example, the analysis unit analyzes the user's preferred music genres, tempos, melodies, etc. The analysis unit can also evaluate the user's skill level using methods for evaluating performance and composition skills. The reception unit receives the user's performance and composition ideas. For example, the reception unit receives melody fragments and rhythm patterns provided by the user. The reception unit can also clarify the format and content of the ideas input by the user. The generation unit generates music based on the ideas received by the reception unit. For example, the generation unit generates music using an algorithm that combines music theory and machine learning. The generation unit can also dynamically generate music based on user input (melody, rhythm). The generation unit can also continuously learn and improve the personalization of music to match the user's preferences. The provision unit provides the music generated by the generation unit. For example, the provision unit provides an interactive experience that stimulates the user's creativity. The provision unit can also clarify how to provide the generated music to the user. As a result, the music co-creation system according to this embodiment can generate and provide music based on the user's musical preferences and technical level.

[0064] The analysis unit analyzes the user's musical preferences and skill level. Specifically, it analyzes the user's listening history and playlists to identify preferred music genres, tempos, and melodic patterns. For example, if a user prefers a specific genre such as rock, jazz, or classical music, it extracts characteristics associated with that genre. Regarding tempo preferences, it analyzes the user's preferred beat speed and rhythmic complexity. Regarding melodic preferences, it identifies the user's preferred scales and chord progressions. Furthermore, it evaluates the user's skill level using methods for evaluating performance and composition skills. For example, it analyzes recordings of performances and scores of compositions provided by the user to evaluate the accuracy and expressiveness of the performance, as well as the structure and creativity of the composition. This allows the analysis unit to gain a detailed understanding of the user's musical preferences and skill level, and provide the information necessary for the next step, music production.

[0065] The reception desk receives users' ideas for performances and compositions. Specifically, it receives melody fragments and rhythm patterns provided by users. For example, users can input melody fragments recorded using a smartphone or tablet, or drum patterns. The reception desk receives these ideas in digital format and makes them available for processing within the system. It can also provide guidelines and templates to clarify the format and content of the ideas that users input. For example, by setting rules such as melody fragments needing to follow a specific length or scale, the reception desk can efficiently receive user ideas. This allows the reception desk to accurately receive users' creative ideas and provide the information necessary for the next step, song generation.

[0066] The generation unit generates music based on ideas received by the reception unit. Specifically, it generates music using an algorithm that combines music theory and machine learning. For example, it can complete a song by adding chord progressions and rhythmic patterns based on a melody fragment provided by the user. The generation unit can also dynamically generate music based on user input (melody, rhythm). For example, it can automatically generate an appropriate accompaniment for a melody entered by the user, completing it as a whole song. Furthermore, the generation unit can continuously learn and improve the personalization of music to match the user's preferences. For example, by providing feedback on the generated music, the generation unit learns from that feedback and incorporates it into the next music generation. In this way, the generation unit can generate music that matches the user's preferences and skill level, supporting the user's creativity.

[0067] The provider unit provides the music generated by the generation unit. Specifically, it provides an interactive experience that stimulates user creativity. For example, it provides an interface that allows users to play the generated music in real time and edit or arrange it on the spot. It also clarifies how the generated music is provided to users. For example, it can provide the generated music in a downloadable format or save it to the cloud for access at any time. It can also provide a function to directly upload the generated music to social media and music sharing platforms. In this way, the provider unit provides an environment in which users can freely use and share the generated music. Furthermore, the provider unit can collect user feedback and use it to improve the overall system. For example, it can collect ratings and comments that users provide on the generated music and use that to improve the system's algorithms and interface. In this way, the provider unit can provide users with a high-quality music generation experience and improve the overall performance of the system.

[0068] The generation unit can generate music using algorithms that combine music theory and machine learning. The generation unit applies music theory such as harmony theory and counterpoint. The generation unit can also use machine learning algorithms such as neural networks and support vector machines. By combining music theory and machine learning, the generation unit can improve the accuracy of music generation. This improves the accuracy of music generation by using algorithms that combine music theory and machine learning. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input an algorithm that combines music theory and machine learning into a generation AI and have the generation AI perform music generation.

[0069] The generation unit can dynamically generate music based on user input. For example, the generation unit can generate music based on melodies and rhythms provided by the user. The generation unit can also analyze user input and dynamically generate music. The generation unit can also adjust the components of the music based on user input. This allows the generation unit to provide music that suits the user's preferences by dynamically generating music based on user input. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user input data into a generation AI and have the generation AI perform music generation.

[0070] The generation unit can continuously learn and improve the personalization of music to match user preferences. The generation unit can continuously learn, for example, using online learning or a feedback loop. The generation unit can also receive user feedback and improve its music generation algorithm. The generation unit can also improve music personalization based on user preferences. As a result, continuous learning improves the personalization of music to match user preferences. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user feedback data into a generation AI and have the generation AI perform improvements to its music generation algorithm.

[0071] The service provider can offer interactive experiences that stimulate the user's creativity. For example, it can provide real-time feedback. The service provider can also offer interactive experiences through dialogue with the user. The service provider can also provide interfaces to stimulate the user's creativity. This enhances the enjoyment of music creation by providing interactive experiences that stimulate the user's creativity. Some or all of the above-described processes in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input dialogue data with the user into a generative AI and have the generative AI perform the provision of an interactive experience.

[0072] The analysis unit can estimate the user's emotions and adjust the analysis results of musical preferences based on the estimated user emotions. For example, if the user is relaxed, the analysis unit will reflect in the analysis results a tendency to prefer relaxing music. If the user is excited, the analysis unit can also reflect in the analysis results a tendency to prefer energetic music. If the user is sad, the analysis unit can also reflect in the analysis results a tendency to prefer calming music. In this way, by adjusting the analysis results of musical preferences based on the user's emotions, more appropriate songs can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The analysis unit can analyze a user's past music history and predict changes in their preferences. For example, the analysis unit can analyze the genres and artists of songs the user has listened to in the past and predict changes in their preferences. The analysis unit can also analyze the trends in music a user listens to during specific seasons or events and predict changes in their preferences. The analysis unit can also analyze how often a user tries new music and predict changes in their preferences. In this way, by analyzing a user's past music history, it is possible to predict changes in their preferences and provide appropriate music. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the user's past music history data into a generative AI and have the generative AI perform the prediction of changes in preferences.

[0074] The analysis unit can evaluate the user's performance skill progress in real time and reflect this in the analysis results. For example, the analysis unit can evaluate the user's skill level on the instrument they play in real time and reflect this in the analysis results. The analysis unit can also evaluate the frequency and duration of the user's practice in real time and reflect this in the analysis results. The analysis unit can also evaluate the speed at which the user acquires new skills in real time and reflect this in the analysis results. This allows the system to provide appropriate music by evaluating the user's performance skill progress in real time. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's performance skill data into a generative AI and have the generative AI perform an evaluation of the skill progress.

[0075] The analysis unit can estimate the user's emotions and adjust the technical level evaluation criteria based on the estimated user emotions. For example, if the user is tense, the analysis unit may relax the technical level evaluation criteria. If the user is relaxed, the analysis unit may tighten the technical level evaluation criteria. If the user is excited, the analysis unit may adjust the technical level evaluation criteria. This allows for a more appropriate evaluation by adjusting the technical level evaluation criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The analysis unit can analyze trends in musical preferences by region, taking into account the user's geographical location. For example, the analysis unit can analyze trends in musical preferences in the region where the user lives. The analysis unit can also analyze trends in musical preferences in regions the user has traveled to. The analysis unit can also analyze trends in musical preferences in regions the user has visited in the past. In this way, by taking into account the user's geographical location, trends in musical preferences by region can be analyzed. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's geographical location data into a generative AI and have the generative AI perform an analysis of trends in musical preferences by region.

[0077] The analysis unit can analyze users' social media activity and understand trends in their musical preferences. For example, the analysis unit can analyze songs that users share on social media to understand trends in their musical preferences. The analysis unit can also analyze artists and bands that users follow to understand trends in their musical preferences. The analysis unit can also analyze music-related communities that users participate in to understand trends in their musical preferences. In this way, trends in musical preferences can be understood by analyzing users' social media activity. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input user social media data into generative AI and have the generative AI perform an analysis of trends in musical preferences.

[0078] The reception unit can estimate the user's emotions and adjust the way ideas are received based on the estimated emotions. For example, if the user is relaxed, the reception unit can provide an interface for receiving detailed ideas. If the user is tense, the reception unit can also provide a simple idea reception interface. If the user is excited, the reception unit can also provide an interactive idea reception interface. This allows for the reception of more appropriate ideas by adjusting the idea reception method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using a generative AI, or not using a generative AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The reception department can analyze a user's past idea submission history and select the optimal timing for submission. For example, the reception department can analyze the time periods when a user has previously submitted ideas and select the optimal timing. The reception department can also analyze the frequency with which a user has previously submitted ideas and select the optimal timing. The reception department can also analyze the circumstances under which a user has previously submitted ideas and select the optimal timing. In this way, the optimal timing for submission can be selected by analyzing a user's past idea submission history. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or without a generative AI. For example, the reception department can input the user's past idea submission history data into a generative AI and have the generative AI select the optimal timing for submission.

[0080] The reception unit can filter ideas based on the user's current projects and areas of interest. For example, the reception unit may prioritize receiving ideas related to the user's current projects. The reception unit may also prioritize receiving ideas related to the user's areas of interest. The reception unit may also prioritize receiving ideas related to ideas the user has submitted in the past. This allows the reception unit to receive highly relevant ideas by filtering them based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input the user's current project and area of ​​interest data into a generative AI and have the generative AI perform the idea filtering.

[0081] The reception desk can estimate the user's emotions and prioritize the ideas to be received based on the estimated emotions. For example, if the user is relaxed, the reception desk may prioritize detailed ideas. If the user is tense, the reception desk may also prioritize simple ideas. If the user is excited, the reception desk may also prioritize interactive ideas. This allows for prioritizing more appropriate ideas based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk may input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The reception desk can prioritize receiving highly relevant ideas by taking into account the user's geographical location. For example, the reception desk can prioritize receiving ideas related to the area where the user lives. The reception desk can also prioritize receiving ideas related to the area the user is traveling to. The reception desk can also prioritize receiving ideas related to the area the user has visited in the past. In this way, by taking into account the user's geographical location, highly relevant ideas can be prioritized. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's geographical location data into a generative AI and have the generative AI select highly relevant ideas.

[0083] The reception unit can analyze a user's social media activity and receive relevant ideas. For example, the reception unit can analyze ideas that a user has shared on social media and receive relevant ideas. The reception unit can also receive ideas related to artists or bands that a user follows. The reception unit can also receive ideas related to music-related communities that a user participates in. In this way, relevant ideas can be received by analyzing a user's social media activity. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input the user's social media data into a generative AI and have the generative AI select relevant ideas.

[0084] The generation unit can estimate the user's emotions and adjust the music generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate music that progresses at a relaxed pace. If the user is excited, the generation unit can also generate energetic music. If the user is sad, the generation unit can also generate calming music. In this way, by adjusting the music generation method based on the user's emotions, more appropriate music can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or not using a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0085] The generation unit can generate music by combining elements from different music genres based on user input. For example, the generation unit can generate music by combining elements of jazz and classical music based on a melody provided by the user. The generation unit can also generate music by combining elements of rock and electronica based on a rhythm provided by the user. The generation unit can also generate music by combining elements of pop and hip hop based on a melody and rhythm provided by the user. This allows for the generation of original music by combining elements from different music genres based on user input. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input user input data into a generation AI and have the generation AI generate music by combining elements from different music genres.

[0086] The generation unit can analyze the user's past song generation history and optimize the generation algorithm. For example, the generation unit can analyze the trends of songs the user has generated in the past and optimize the generation algorithm. The generation unit can also analyze the genre and style of songs the user has generated in the past and optimize the generation algorithm. The generation unit can also analyze the ratings of songs the user has generated in the past and optimize the generation algorithm. In this way, by analyzing the user's past song generation history, the generation algorithm can be optimized and more appropriate songs can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's past song generation history data into a generation AI and have the generation AI perform the optimization of the generation algorithm.

[0087] The generation unit can estimate the user's emotions and adjust the order in which songs are generated based on the estimated emotions. For example, if the user is relaxed, the generation unit will prioritize generating relaxing songs. If the user is excited, the generation unit can also prioritize generating energetic songs. If the user is sad, the generation unit can also prioritize generating calming songs. In this way, by adjusting the order in which songs are generated based on the user's emotions, more appropriate songs can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is 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 processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0088] The generation unit can generate music that reflects the musical characteristics of each region, taking into account the user's geographical location information. For example, the generation unit can generate music that reflects the musical characteristics of the region where the user lives. The generation unit can also generate music that reflects the musical characteristics of the region the user is traveling to. The generation unit can also generate music that reflects the musical characteristics of the region the user has visited in the past. In this way, by taking into account the user's geographical location information, music that reflects the musical characteristics of each region can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's geographical location data into a generation AI and have the generation AI generate music that reflects the musical characteristics of each region.

[0089] The generation unit can analyze a user's social media activity and generate music that matches current trends. For example, the generation unit can analyze the trends of music shared by the user on social media and generate music. The generation unit can also analyze the trends of artists and bands that the user follows and generate music. The generation unit can also analyze the trends of music-related communities that the user participates in and generate music. In this way, by analyzing the user's social media activity, music that matches current trends can be generated. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's social media data into a generation AI and have the generation AI generate music that matches current trends.

[0090] The service provider can estimate the user's emotions and adjust the way music is delivered based on the estimated emotions. For example, if the user is relaxed, the service provider can deliver relaxing music. If the user is excited, the service provider can deliver energetic music. If the user is sad, the service provider can deliver calming music. By adjusting the way music is delivered based on the user's emotions, more appropriate music can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The service provider can analyze a user's past music usage history and select the optimal service timing. For example, the service provider can analyze the time periods in which the user has used music in the past and select the optimal service timing. The service provider can also analyze the frequency in which the user has used music in the past and select the optimal service timing. The service provider can also analyze the circumstances in which the user has used music in the past and select the optimal service timing. In this way, the service provider can select the optimal service timing by analyzing the user's past music usage history. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input the user's past music usage history data into a generation AI and have the generation AI select the optimal service timing.

[0092] The service provider can customize the means of providing music based on the user's current activity. For example, if the user is exercising, the service provider can provide energetic music. If the user is relaxing, the service provider can also provide calming music. If the user is studying, the service provider can also provide music that enhances concentration. By customizing the means of providing music based on the user's current activity, more appropriate music can be provided. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input data on the user's current activity status into a generative AI and have the generative AI perform the customization of the means of providing music.

[0093] The service provider can estimate the user's emotions and adjust the order in which songs are provided based on the estimated emotions. For example, if the user is relaxed, the service provider will prioritize providing relaxing songs. If the user is excited, the service provider may also prioritize providing energetic songs. If the user is sad, the service provider may also prioritize providing calming songs. By adjusting the order in which songs are provided based on the user's emotions, more appropriate songs can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The service provider can provide music that reflects the musical characteristics of each region, taking into account the user's geographical location information. For example, the service provider can provide music that reflects the musical characteristics of the region where the user lives. The service provider can also provide music that reflects the musical characteristics of the region the user is traveling to. The service provider can also provide music that reflects the musical characteristics of the region the user has visited in the past. In this way, by taking into account the user's geographical location information, it is possible to provide music that reflects the musical characteristics of each region. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input the user's geographical location data into a generative AI and have the generative AI perform the task of providing music that reflects the musical characteristics of each region.

[0095] The service provider can analyze a user's social media activity and provide music that matches current trends. For example, it can analyze the trends of music shared by users on social media and provide music based on those trends. It can also analyze the trends of artists and bands that users follow and provide music based on those trends. It can also analyze the trends of music-related communities that users participate in and provide music based on those trends. In this way, by analyzing a user's social media activity, it can provide music that matches current trends. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not. For example, the service provider can input the user's social media data into a generative AI and have the generative AI perform the task of providing music that matches current trends.

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

[0097] The analytics department can consider the user's health condition when analyzing their musical preferences and skill level. For example, if a user is tired, the analysis can reflect their tendency to prefer relaxing music. If a user is energetic, the analysis can reflect their tendency to prefer energetic music. If a user is stressed, the analysis can reflect their tendency to prefer calming music. By adjusting the analysis results of musical preferences based on the user's health condition, more appropriate music can be provided.

[0098] The generation unit can create music by combining musical elements from different cultural spheres based on user input. For example, it can generate music that combines African beats and Asian melodies based on a melody provided by the user. It can also generate music that combines Latin music and Middle Eastern musical elements based on a rhythm provided by the user. Furthermore, it can generate music that combines European classical music and Caribbean musical elements based on a melody and rhythm provided by the user. This allows for the creation of unique music by combining musical elements from different cultural spheres based on user input.

[0099] The service provider can estimate the user's emotions and adjust the way music is delivered based on those estimates. For example, if the user is relaxed, it can provide relaxing music. If the user is excited, it can provide energetic music. If the user is sad, it can provide calming music. By adjusting the way music is delivered based on the user's emotions, it can provide more appropriate music.

[0100] The analytics department can analyze a user's past music history and predict changes in their preferences. For example, it can analyze the genres and artists of music a user has listened to in the past to predict changes in their preferences. It can also analyze the music a user listens to during specific seasons or events to predict changes in their preferences. It can also analyze how often a user tries new music to predict changes in their preferences. In this way, by analyzing a user's past music history, it can predict changes in their preferences and provide them with appropriate music.

[0101] The generation unit can estimate the user's emotions and adjust the music generation method based on those emotions. For example, if the user is relaxed, it can generate music that progresses at a leisurely pace. If the user is excited, it can generate energetic music. If the user is sad, it can generate calming music. By adjusting the music generation method based on the user's emotions, it is possible to generate more appropriate music.

[0102] The service provider can customize the way music is delivered based on the user's current activity. For example, if the user is exercising, energetic music can be provided. If the user is relaxing, calming music can be provided. If the user is studying, music that enhances concentration can be provided. By customizing the way music is delivered based on the user's current activity, more appropriate music can be provided.

[0103] The analysis unit can estimate the user's emotions and adjust the technical level evaluation criteria based on those emotions. For example, if the user is nervous, the technical level evaluation criteria can be relaxed. If the user is relaxed, the technical level evaluation criteria can be made stricter. If the user is excited, the technical level evaluation criteria can also be adjusted. By adjusting the technical level evaluation criteria based on the user's emotions, a more appropriate evaluation becomes possible.

[0104] The generation unit can create music by combining elements from different music genres based on user input. For example, it can generate music that combines jazz and classical elements based on a melody provided by the user. It can also generate music that combines rock and electronica elements based on a rhythm provided by the user. It can even generate music that combines pop and hip-hop elements based on a melody and rhythm provided by the user. This allows users to create unique music by combining elements from different music genres based on their input.

[0105] The playback system can estimate the user's emotions and adjust the order in which songs are played based on those estimates. For example, if the user is relaxed, it can prioritize playing relaxing songs. If the user is excited, it can prioritize playing energetic songs. If the user is sad, it can prioritize playing calming songs. By adjusting the order of songs based on the user's emotions, the system can provide more appropriate music.

[0106] The analysis unit can analyze regional musical preference trends by taking into account the user's geographical location. For example, it can analyze the musical preference trends of the area where the user lives. It can also analyze the musical preference trends of areas the user has traveled to. It can also analyze the musical preference trends of areas the user has visited in the past. In this way, by taking into account the user's geographical location, it is possible to analyze regional musical preference trends.

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

[0108] Step 1: The analysis unit analyzes the user's musical preferences and skill level. For example, it analyzes the user's preferred music genres, tempos, and melodies, and evaluates their skill level using methods for assessing their performance and composition skills. Step 2: The reception desk receives the user's performance and composition ideas. For example, it receives melody fragments or rhythm patterns provided by the user and clarifies the format and content of the ideas the user will input. Step 3: The generation unit generates music based on the ideas received by the reception unit. For example, it generates music using an algorithm that combines music theory and machine learning, and dynamically generates music based on user input (melody, rhythm). The generation unit continuously learns and improves the personalization of the music to match the user's preferences. Step 4: The provider unit provides the music generated by the generator unit. For example, it provides an interactive experience that stimulates the user's creativity and clarifies how to deliver the generated music to the user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the analysis unit, reception unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's musical preferences and skill level. The reception unit is implemented by the control unit 46A of the smart device 14 and receives the user's ideas for performance and composition. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a musical piece based on the ideas received by the reception unit. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the musical piece generated by the generation unit to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the analysis unit, reception unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's musical preferences and skill level. The reception unit is implemented by the control unit 46A of the smart glasses 214 and receives the user's ideas for performance and composition. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a musical piece based on the ideas received by the reception unit. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the musical piece generated by the generation unit to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the analysis unit, reception unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's musical preferences and skill level. The reception unit is implemented by the control unit 46A of the headset terminal 314 and receives the user's ideas for performance and composition. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a musical piece based on the ideas received by the reception unit. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the musical piece generated by the generation unit to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the analysis unit, reception unit, generation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's musical preferences and skill level. The reception unit is implemented by the control unit 46A of the robot 414 and receives the user's ideas for performance or composition. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a musical piece based on the ideas received by the reception unit. The provision unit is implemented by the control unit 46A of the robot 414 and provides the musical piece generated by the generation unit to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) An analysis department that analyzes users' musical preferences and technical levels, A reception desk that receives users' ideas for performances and compositions, A generation unit that generates music based on the ideas received by the reception unit, The system includes a providing unit that provides music generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Music is generated using an algorithm that combines music theory and machine learning. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Dynamically generates music based on user input. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is It continuously learns and improves the personalization of music to match the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We provide interactive experiences that stimulate users' creativity. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis results of musical preferences based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is It analyzes the user's past music history and predicts changes in their preferences. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is The system evaluates the user's performance skills in real time and reflects this in the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is It estimates user sentiment and adjusts the technical level evaluation criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is We analyze regional musical preference trends, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is Analyze users' social media activity to understand their musical taste trends. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is We estimate the user's emotions and adjust the idea submission process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is We analyze the user's past idea submission history to select the optimal timing for submission. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is Filter ideas based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reception unit is It estimates the user's emotions and prioritizes the ideas to accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reception unit is We will prioritize accepting ideas that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reception unit is We analyze users' social media activity and accept related ideas. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and adjusts the music generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is Based on user input, the system generates music by combining elements from different music genres. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is Analyze the user's past music generation history and optimize the generation algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and adjusts the order in which songs are generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is By considering the user's geographical location, the system generates music that reflects the unique musical characteristics of each region. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is Analyzes users' social media activity and generates music that matches current trends. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the way music is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system analyzes the user's past music usage history to select the optimal timing for providing content. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, Customize the method of delivering music based on the user's current activity level. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the order in which songs are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, We provide music that reflects the musical characteristics of each region, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, Analyze users' social media activity and provide music that matches current trends. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An analysis department that analyzes users' musical preferences and technical levels, A reception desk that receives users' ideas for performances and compositions, A generation unit that generates music based on the ideas received by the reception unit, The system includes a providing unit that provides music generated by the generation unit. A system characterized by the following features.

2. The generating unit is Music is generated using an algorithm that combines music theory and machine learning. The system according to feature 1.

3. The generating unit is Dynamically generates music based on user input. The system according to feature 1.

4. The generating unit is It continuously learns and improves the personalization of music to match the user's preferences. The system according to feature 1.

5. The aforementioned supply unit is, We provide interactive experiences that stimulate users' creativity. The system according to feature 1.

6. The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis results of musical preferences based on the estimated user emotions. The system according to feature 1.

7. The aforementioned analysis unit is It analyzes the user's past music history and predicts changes in their preferences. The system according to feature 1.

8. The aforementioned analysis unit is The system evaluates the user's performance skills in real time and reflects this in the analysis results. The system according to feature 1.

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