system

Abstract

The system according to the embodiment aims to allow a user to automatically arrange music to suit his or her preferences. [Solution] A system according to an embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives arrangement parameters input from a user. The generating unit arranges a song based on the arrangement parameters received by the receiving unit. The providing unit provides the song arranged by the generating unit to the user.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem that it is difficult for users to easily arrange songs to suit their own preferences.

[0005] The system according to the embodiment aims to allow a user to automatically arrange music to suit his or her preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives arrangement parameters input from a user. The generating unit arranges a song based on the arrangement parameters received by the receiving unit. The providing unit provides the song arranged by the generating unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to automatically arrange music to suit his or her preferences. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A music arranging system according to an embodiment of the present invention is a system that automatically arranges songs to suit a user's preferences. In this music arranging system, the user selects a song they want to arrange and inputs the desired arrangement parameters (e.g., tempo, melody, instrument changes, added effects, etc.). Next, a generation AI arranges the song based on these parameters. The generation AI analyzes the input parameters and adjusts each element of the song (e.g., melody, rhythm, harmony, etc.). For example, it can speed up the tempo, change to a pop style, or add effects such as reverb or delay. The arranged song is provided to the user, who can enjoy songs that suit their preferences. This system allows users to try out infinite arrangements and discover and enjoy more songs that suit their preferences. For example, if a user wants to arrange a specific song in a pop style, the generation AI arranges the song in a pop style and provides it to the user. This allows the user to enjoy songs that suit their preferences. In this way, the music arranging system allows users to automatically arrange songs to suit their preferences.

[0029] A music arrangement system according to an embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives input of arrangement parameters from a user. The arrangement parameters from the user include, but are not limited to, tempo, melody, instrument changes, and added effects. The receiving unit, for example, provides an interface for the user to select a song they want to arrange and input the desired arrangement parameters. The generating unit uses a generation AI to arrange the song based on the arrangement parameters received by the receiving unit. The generating unit, for example, analyzes the input parameters and adjusts each element of the song (e.g., melody, rhythm, harmony, etc.). The generating unit can, for example, speed up the tempo, change the style to a pop style, or add effects such as reverb or delay. Some or all of the above-described processing in the generating unit is performed using the generation AI. The generation AI can, for example, be a text generation AI (e.g., LLM) or a multimodal generation AI. The providing unit provides the song arranged by the generating unit to the user. The providing unit can, for example, download the arranged song to the user's device or play it via streaming. As a result, the music arrangement system according to the embodiment allows the user to automatically arrange music to suit his or her preferences.

[0030] The reception unit can receive arrangement parameters, including tempo, melody, instrument changes, and effect additions. The reception unit, for example, provides an interface through which a user can select a song to arrange and input desired arrangement parameters. Tempo changes can be made, for example, within a BPM (beats per minute) range. Melody changes can be made to a genre, such as pop, rock, or classical. Instrument changes can include, for example, changing from piano to guitar. Effects can be added, such as reverb, delay, and chorus. This allows users to input a variety of arrangement parameters. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the arrangement parameters entered by the user into AI, which then evaluates the appropriateness of the parameters.

[0031] The generation unit can adjust each element of the song, including melody, rhythm, and harmony, based on the input arrangement parameters. The generation unit, for example, analyzes the input arrangement parameters and adjusts each element of the song. Melody adjustments include, for example, changing pitch or adding phrases. Rhythm adjustments include, for example, changing beats or rhythm patterns. Harmony adjustments include, for example, changing chords or chord progressions. The generation unit analyzes these elements and performs an optimal arrangement. This allows for adjustment of each element of the song to be arranged in accordance with the user's wishes. Some or all of the above-described processing in the generation unit is performed using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit can input the input arrangement parameters into the generation AI, which then adjusts the melody, rhythm, and harmony.

[0032] The generation unit can speed up the tempo, change the style to a pop style, and add effects including reverb and delay. The generation unit, for example, changes the tempo. The tempo change is made, for example, based on an increase in BPM. The change to a pop style is made, for example, based on a change in the instruments used or the rhythm pattern. The addition of reverb and delay is made, for example, based on effect parameter settings. This enables a variety of arrangements. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit inputs the arrangement parameters into the generation AI, which can then change the tempo, change the style to a pop style, and add reverb and delay.

[0033] The providing unit can provide the arranged song to the user. For example, the providing unit can download the arranged song to the user's device. The providing unit can also stream the arranged song, allowing the user to enjoy the arranged song. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the arranged song to AI, which can select the optimal method of providing the song.

[0034] The reception unit can analyze the user's past arrangement history and present suitable arrangement parameter candidates. The reception unit, for example, analyzes the user's past arrangement history. The past arrangement history includes, for example, records of arrange parameters selected in the past. The reception unit presents optimal arrange parameter candidates based on the past arrangement history. For example, arrange parameters frequently used by the user in the past can be automatically displayed as candidates. The reception unit can also predict and suggest arrange parameters that the user will use during a specific time period. Furthermore, the reception unit can analyze the user's past arrangement history and suggest the most preferred parameters. This allows optimal arrange parameters to be proposed based on the user's past history. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's past arrangement history into AI, which then presents optimal arrange parameter candidates.

[0035] When inputting arrangement parameters, the reception unit can perform filtering based on the user's music preferences and past playback history. The reception unit, for example, analyzes the user's music preferences and past playback history. The music preferences are identified, for example, based on the user's playback history and survey results. The playback history includes, for example, the number of plays and playback time. The reception unit filters the arrangement parameters based on this information. For example, the reception unit can filter the arrangement parameters based on the characteristics of songs the user has liked to play in the past. Furthermore, the reception unit can prioritize and display highly relevant arrangement parameters based on the user's music preferences. Furthermore, the reception unit can analyze the user's playback history and suggest optimal arrangement parameters. This allows the reception unit to provide arrangement parameters based on the user's preferences. Some or all of the above-described processing in the reception unit can be performed using AI, or can be performed without AI. For example, the reception unit can input the user's music preferences and playback history into AI, which can then perform filtering.

[0036] When inputting arrangement parameters, the reception unit can prioritize presenting highly relevant parameters taking into account the user's geographical location information. The reception unit, for example, acquires the user's geographical location information. The geographical location information is acquired, for example, based on GPS data or location information services. The reception unit filters arrangement parameters based on the user's geographical location information and prioritizes presenting highly relevant parameters. For example, if the user is in a specific area, arrangement parameters based on the music style of that area can be suggested. Also, if the user is traveling, arrangement parameters based on the music style of the travel destination can be suggested. Furthermore, if the user is at home, arrangement parameters with a relaxing effect can be suggested. This makes it possible to provide arrangement parameters based on the user's location information. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to AI, which can then suggest optimal arrangement parameters.

[0037] When inputting arrangement parameters, the reception unit can analyze the user's social media activity and suggest related parameters. The reception unit, for example, analyzes the user's social media activity. Social media activity includes, for example, the content of posts, the number of likes, and the number of followers. The reception unit suggests arrangement parameters based on this information. For example, the reception unit can analyze the trends in music shared by the user on social media and suggest arrangement parameters. The reception unit can also suggest arrangement parameters based on the musical style of artists the user follows. Furthermore, the reception unit can analyze the user's music-related posts on social media and suggest optimal arrangement parameters. This makes it possible to provide arrangement parameters based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity into AI, which then suggests optimal arrangement parameters.

[0038] When generating an arrangement, the generation unit can adjust the level of detail of the arrangement based on the importance of the song. The generation unit, for example, evaluates the importance of the song. The importance of the song is evaluated based on, for example, a user's rating or the number of plays. The generation unit adjusts the level of detail of the arrangement based on the importance of the song. For example, a detailed arrangement can be generated for a song that the user considers particularly important. A simplified arrangement can also be generated for a song that the user considers less important. Furthermore, a detailed arrangement can be generated for a song that the user frequently plays in a playlist. This makes it possible to provide an arrangement according to the importance of the song. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit can input song importance data into the generation AI, which can then adjust the level of detail of the arrangement based on the importance.

[0039] When generating an arrangement, the generation unit can apply different arrangement algorithms depending on the song category. The generation unit, for example, identifies the song category. Song categories are classified based on, for example, genre or theme. The generation unit applies different arrangement algorithms depending on the song category. For example, an arrangement algorithm specialized for pop music can be applied to a pop song. Furthermore, an arrangement algorithm specialized for classical music can be applied to a classical song. Furthermore, an arrangement algorithm specialized for jazz can be applied to a jazz song. This makes it possible to provide an arrangement according to the song category. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit can input song category data into the generation AI, which can then apply an arrangement algorithm according to the category.

[0040] When generating arrangements, the generation unit can determine the priority of arrangements based on the creation dates of the songs. The generation unit, for example, acquires the creation dates of the songs. The creation dates of the songs are acquired based on, for example, the creation date or the release date. The generation unit determines the priority of arrangements based on the creation dates of the songs. For example, recently created songs can be arranged with priority. Also, older songs can be arranged later. Furthermore, songs that the user particularly likes can be arranged with priority. This makes it possible to provide arrangements according to the creation dates of the songs. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit can input song creation date data into the generation AI, which can then determine the priority of arrangements based on the creation dates.

[0041] When generating an arrangement, the generation unit can adjust the order of the arrangement based on the relevance of the songs. The generation unit, for example, evaluates the relevance of the songs. The relevance of the songs is evaluated based on, for example, the same artist or the same album. The generation unit adjusts the order of the arrangement based on the relevance of the songs. For example, songs in the same album can be arranged in descending order of relevance. Furthermore, songs by the same artist can be arranged in descending order of relevance. Furthermore, songs in a user's playlist can be arranged in descending order of relevance. This makes it possible to provide an arrangement based on the relevance of the songs. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit can input song relevance data into the generation AI, which can then adjust the order of the arrangement based on the relevance.

[0042] The providing unit can select the optimal providing method by referring to the user's past playback history when providing the music. The providing unit, for example, analyzes the user's past playback history. The playback history includes, for example, the number of plays and the playback time. The providing unit selects the optimal providing method based on this information. For example, the providing unit can provide songs arranged based on the style of songs the user frequently played in the past. Also, the providing unit can provide songs arranged based on the style of songs the user plays during a specific time period. Furthermore, the user's playback history can be analyzed to provide arrangements in the most preferred style. This makes it possible to provide a providing method based on the user's playback history. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's playback history data into AI, which can select the optimal providing method.

[0043] The providing unit can adjust the timing of providing the music based on the user's current activity status when providing the music. The providing unit, for example, acquires the user's current activity status. The current activity status is acquired based on, for example, whether the user is exercising, relaxing, working, etc. The providing unit adjusts the timing of providing the music based on the user's current activity status. For example, if the user is exercising, an energetic song can be provided. Also, if the user is relaxing, a relaxed song can be provided. Furthermore, if the user is working, a song that increases concentration can be provided. This makes it possible to provide the music at a timing that suits the user's activity status. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's activity status data into AI, which can then adjust the optimal timing of providing the music.

[0044] The providing unit can select the optimal providing method by taking into consideration the user's geographical location information when providing the content. The providing unit, for example, acquires the user's geographical location information. The geographical location information is acquired, for example, based on GPS data or a location information service. The providing unit selects the optimal providing method based on the user's geographical location information. For example, if the user is in a specific area, songs based on the music style of that area can be provided. Also, if the user is traveling, songs based on the music style of the travel destination can be provided. Furthermore, if the user is at home, songs with a relaxing effect can be provided. This makes it possible to provide a providing method based on the user's location information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into AI, which can select the optimal providing method.

[0045] The providing unit can analyze the user's social media activity and suggest a means of provision at the time of provision. The providing unit, for example, analyzes the user's social media activity. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc. The providing unit suggests a means of provision based on this information. For example, the providing unit can analyze trends in music shared by the user on social media and provide arranged songs. The providing unit can also provide songs arranged based on the musical style of artists the user follows. Furthermore, the providing unit can analyze the user's music-related posts on social media and provide optimally arranged songs. This makes it possible to provide a means of provision based on the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity into AI, which can then suggest the optimal means of provision.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The reception unit can analyze the user's voice input and automatically set arrangement parameters based on the voice command. For example, if the user says "Speed ​​up the tempo," the reception unit can analyze this command using voice recognition technology and automatically set the tempo parameter. Also, if the user says "Add guitar," the instrument change parameter can be automatically set. Furthermore, if the user says "Increase the reverb," ​​the effect parameter can be adjusted. This allows the user to intuitively set arrangement parameters using voice commands.

[0048] The generation unit can learn the user's past arrangement history and suggest arrangements based on the user's preferences. For example, if the user has previously preferred pop arrangements, the generation unit can suggest pop arrangements for new songs. Also, if the user frequently uses a particular effect, the generation unit can suggest arrangements that include that effect. Furthermore, if the user has a preference for a particular instrument, the generation unit can suggest arrangements that include that instrument. This makes it possible to automatically suggest arrangements based on the user's preferences.

[0049] When generating an arrangement, the generation unit can apply different arrangement algorithms depending on the song category. For example, an arrangement algorithm specialized for pop music can be applied to a pop song. Furthermore, an arrangement algorithm specialized for classical music can be applied to a classical song. Furthermore, an arrangement algorithm specialized for jazz music can be applied to a jazz song. This makes it possible to provide an arrangement that suits the song category. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit can input song category data into the generation AI, which can then apply an arrangement algorithm that suits the category.

[0050] The providing unit can prioritize the presentation of highly relevant arrange parameters taking into account the user's geographical location information. For example, if the user is in a specific area, arrange parameters based on the music style of that area can be suggested. Also, if the user is traveling, arrange parameters based on the music style of the travel destination can be suggested. Furthermore, if the user is at home, arrange parameters with a relaxing effect can be suggested. This makes it possible to provide arrange parameters based on the user's location information. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into AI, which can then present the optimal arrange parameters.

[0051] The reception unit can analyze the user's social media activity and suggest relevant arrangement parameters. For example, it can analyze the trends in music shared by the user on social media and suggest arrangement parameters. It can also suggest arrangement parameters based on the musical style of artists the user follows. It can also analyze the user's music-related posts on social media and suggest optimal arrangement parameters. This makes it possible to provide arrangement parameters based on the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity into AI, which then suggests optimal arrangement parameters.

[0052] When generating arrangements, the generation unit can determine the priority of arrangements based on the creation dates of the songs. For example, recently created songs can be given priority in arrangement. Also, older songs can be arranged later. Furthermore, songs that the user particularly likes can be given priority in arrangement. This makes it possible to provide arrangements according to the creation dates of the songs. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit can input data on the creation dates of the songs into the generation AI, which can then determine the priority of arrangements based on the creation dates.

[0053] The providing unit can adjust the timing of providing music based on the user's current activity status. For example, if the user is exercising, energetic music can be provided. If the user is relaxing, slow music can be provided. Furthermore, if the user is working, music that increases concentration can be provided. This makes it possible to provide music at a timing that suits the user's activity status. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's activity status data into AI, which can then adjust the optimal timing of providing music.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The reception unit receives arrangement parameter input from the user. The arrangement parameters from the user include, for example, tempo, melody, instrument changes, and the addition of effects. The reception unit provides an interface for the user to select the song they want to arrange and input the desired arrangement parameters. Step 2: The generation unit uses the generation AI to arrange the song based on the arrangement parameters received by the reception unit. The generation unit analyzes the input parameters and adjusts each element of the song (melody, rhythm, harmony, etc.). For example, it can speed up the tempo, change to a pop style, or add effects such as reverb or delay. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. Step 3: The providing unit provides the song arranged by the generating unit to the user. The providing unit can allow the user to download the arranged song to their device or play it via streaming.

[0056] (Example 2) A music arranging system according to an embodiment of the present invention is a system that automatically arranges songs to suit a user's preferences. In this music arranging system, the user selects a song they want to arrange and inputs the desired arrangement parameters (e.g., tempo, melody, instrument changes, added effects, etc.). Next, a generation AI arranges the song based on these parameters. The generation AI analyzes the input parameters and adjusts each element of the song (e.g., melody, rhythm, harmony, etc.). For example, it can speed up the tempo, change to a pop style, or add effects such as reverb or delay. The arranged song is provided to the user, who can enjoy songs that suit their preferences. This system allows users to try out infinite arrangements and discover and enjoy more songs that suit their preferences. For example, if a user wants to arrange a specific song in a pop style, the generation AI arranges the song in a pop style and provides it to the user. This allows the user to enjoy songs that suit their preferences. In this way, the music arranging system allows users to automatically arrange songs to suit their preferences.

[0057] A music arrangement system according to an embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives input of arrangement parameters from a user. The arrangement parameters from the user include, but are not limited to, tempo, melody, instrument changes, and added effects. The receiving unit, for example, provides an interface for the user to select a song they want to arrange and input the desired arrangement parameters. The generating unit uses a generation AI to arrange the song based on the arrangement parameters received by the receiving unit. The generating unit, for example, analyzes the input parameters and adjusts each element of the song (e.g., melody, rhythm, harmony, etc.). The generating unit can, for example, speed up the tempo, change the style to a pop style, or add effects such as reverb or delay. Some or all of the above-described processing in the generating unit is performed using the generation AI. The generation AI can, for example, be a text generation AI (e.g., LLM) or a multimodal generation AI. The providing unit provides the song arranged by the generating unit to the user. The providing unit can, for example, download the arranged song to the user's device or play it via streaming. As a result, the music arrangement system according to the embodiment allows the user to automatically arrange music to suit his or her preferences.

[0058] The reception unit can receive arrangement parameters, including tempo, melody, instrument changes, and effect additions. The reception unit, for example, provides an interface through which a user can select a song to arrange and input desired arrangement parameters. Tempo changes can be made, for example, within a BPM (beats per minute) range. Melody changes can be made to a genre, such as pop, rock, or classical. Instrument changes can include, for example, changing from piano to guitar. Effects can be added, such as reverb, delay, and chorus. This allows users to input a variety of arrangement parameters. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the arrangement parameters entered by the user into AI, which then evaluates the appropriateness of the parameters.

[0059] The generation unit can adjust each element of the song, including melody, rhythm, and harmony, based on the input arrangement parameters. The generation unit, for example, analyzes the input arrangement parameters and adjusts each element of the song. Melody adjustments include, for example, changing pitch or adding phrases. Rhythm adjustments include, for example, changing beats or rhythm patterns. Harmony adjustments include, for example, changing chords or chord progressions. The generation unit analyzes these elements and performs an optimal arrangement. This allows for adjustment of each element of the song to be arranged in accordance with the user's wishes. Some or all of the above-described processing in the generation unit is performed using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit can input the input arrangement parameters into the generation AI, which then adjusts the melody, rhythm, and harmony.

[0060] The generation unit can speed up the tempo, change the style to a pop style, and add effects including reverb and delay. The generation unit, for example, changes the tempo. The tempo change is made, for example, based on an increase in BPM. The change to a pop style is made, for example, based on a change in the instruments used or the rhythm pattern. The addition of reverb and delay is made, for example, based on effect parameter settings. This enables a variety of arrangements. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit inputs the arrangement parameters into the generation AI, which can then change the tempo, change the style to a pop style, and add reverb and delay.

[0061] The providing unit can provide the arranged song to the user. For example, the providing unit can download the arranged song to the user's device. The providing unit can also stream the arranged song, allowing the user to enjoy the arranged song. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the arranged song to AI, which can select the optimal method of providing the song.

[0062] The reception unit can estimate the user's emotion and customize the input interface for the arrangement parameters based on the estimated user emotion. The reception unit, for example, estimates the user's emotion. Emotion estimation is performed, for example, based on facial expression recognition, voice analysis, survey results, etc. The reception unit customizes the input interface for the arrangement parameters based on the estimated user emotion. For example, if the user is stressed, a simple interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to enable quick input of arrangement parameters. This facilitates input by providing an interface that corresponds to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's emotion data into a generation AI, which can then customize the interface based on the emotion.

[0063] The reception unit can analyze the user's past arrangement history and present suitable arrangement parameter candidates. The reception unit, for example, analyzes the user's past arrangement history. The past arrangement history includes, for example, records of arrange parameters selected in the past. The reception unit presents optimal arrange parameter candidates based on the past arrangement history. For example, arrange parameters frequently used by the user in the past can be automatically displayed as candidates. The reception unit can also predict and suggest arrange parameters that the user will use during a specific time period. Furthermore, the reception unit can analyze the user's past arrangement history and suggest the most preferred parameters. This allows optimal arrange parameters to be proposed based on the user's past history. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's past arrangement history into AI, which then presents optimal arrange parameter candidates.

[0064] When inputting arrangement parameters, the reception unit can perform filtering based on the user's music preferences and past playback history. The reception unit, for example, analyzes the user's music preferences and past playback history. The music preferences are identified, for example, based on the user's playback history and survey results. The playback history includes, for example, the number of plays and playback time. The reception unit filters the arrangement parameters based on this information. For example, the reception unit can filter the arrangement parameters based on the characteristics of songs the user has liked to play in the past. Furthermore, the reception unit can prioritize and display highly relevant arrangement parameters based on the user's music preferences. Furthermore, the reception unit can analyze the user's playback history and suggest optimal arrangement parameters. This allows the reception unit to provide arrangement parameters based on the user's preferences. Some or all of the above-described processing in the reception unit can be performed using AI, or can be performed without AI. For example, the reception unit can input the user's music preferences and playback history into AI, which can then perform filtering.

[0065] The reception unit can estimate the user's emotion and determine the priority of arrangement parameters based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion. Emotion estimation is performed, for example, based on facial expression recognition, voice analysis, questionnaire results, etc. The reception unit determines the priority of arrangement parameters based on the estimated user's emotion. For example, if the user is relaxed, arrangement parameters with a relaxing effect can be preferentially displayed. Also, if the user is excited, arrangement parameters with an energetic effect can be preferentially displayed. Furthermore, if the user is sad, arrangement parameters with a soothing effect can be preferentially displayed. This allows arrangement parameters corresponding to the user's emotion to be preferentially provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's emotion data into a generation AI, which can then determine the priority of arrangement parameters based on the emotion.

[0066] When inputting arrangement parameters, the reception unit can prioritize presenting highly relevant parameters taking into account the user's geographical location information. The reception unit, for example, acquires the user's geographical location information. The geographical location information is acquired, for example, based on GPS data or location information services. The reception unit filters arrangement parameters based on the user's geographical location information and prioritizes presenting highly relevant parameters. For example, if the user is in a specific area, arrangement parameters based on the music style of that area can be suggested. Also, if the user is traveling, arrangement parameters based on the music style of the travel destination can be suggested. Furthermore, if the user is at home, arrangement parameters with a relaxing effect can be suggested. This makes it possible to provide arrangement parameters based on the user's location information. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to AI, which can then suggest optimal arrangement parameters.

[0067] When inputting arrangement parameters, the reception unit can analyze the user's social media activity and suggest related parameters. The reception unit, for example, analyzes the user's social media activity. Social media activity includes, for example, the content of posts, the number of likes, and the number of followers. The reception unit suggests arrangement parameters based on this information. For example, the reception unit can analyze the trends in music shared by the user on social media and suggest arrangement parameters. The reception unit can also suggest arrangement parameters based on the musical style of artists the user follows. Furthermore, the reception unit can analyze the user's music-related posts on social media and suggest optimal arrangement parameters. This makes it possible to provide arrangement parameters based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity into AI, which then suggests optimal arrangement parameters.

[0068] The generation unit can estimate the user's emotions and adjust the expression method of the arrangement based on the estimated user's emotions. The generation unit, for example, estimates the user's emotions. Emotion estimation is performed, for example, based on facial expression recognition, voice analysis, survey results, etc. The generation unit adjusts the expression method of the arrangement based on the estimated user's emotions. For example, if the user is relaxed, an arrangement with a slow tempo can be generated. On the other hand, if the user is excited, an arrangement with a fast tempo can be generated. Furthermore, if the user is sad, an arrangement with a soothing effect can be generated. This allows an arrangement to be provided that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI, which can then adjust the expression method of the arrangement based on the emotion.

[0069] When generating an arrangement, the generation unit can adjust the level of detail of the arrangement based on the importance of the song. The generation unit, for example, evaluates the importance of the song. The importance of the song is evaluated based on, for example, a user's rating or the number of plays. The generation unit adjusts the level of detail of the arrangement based on the importance of the song. For example, a detailed arrangement can be generated for a song that the user considers particularly important. A simplified arrangement can also be generated for a song that the user considers less important. Furthermore, a detailed arrangement can be generated for a song that the user frequently plays in a playlist. This makes it possible to provide an arrangement according to the importance of the song. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit can input song importance data into the generation AI, which can then adjust the level of detail of the arrangement based on the importance.

[0070] When generating an arrangement, the generation unit can apply different arrangement algorithms depending on the song category. The generation unit, for example, identifies the song category. Song categories are classified based on, for example, genre or theme. The generation unit applies different arrangement algorithms depending on the song category. For example, an arrangement algorithm specialized for pop music can be applied to a pop song. Furthermore, an arrangement algorithm specialized for classical music can be applied to a classical song. Furthermore, an arrangement algorithm specialized for jazz can be applied to a jazz song. This makes it possible to provide an arrangement according to the song category. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit can input song category data into the generation AI, which can then apply an arrangement algorithm according to the category.

[0071] The generation unit can estimate the user's emotions and adjust the length of the arrangement based on the estimated user emotions. The generation unit, for example, estimates the user's emotions. Emotion estimation is performed, for example, based on facial expression recognition, voice analysis, survey results, etc. The generation unit adjusts the length of the arrangement based on the estimated user emotions. For example, if the user is relaxed, a longer arrangement can be generated. On the other hand, if the user is in a hurry, a shorter arrangement can be generated. Furthermore, if the user is enjoying themselves, a normal-length arrangement can be generated. This allows the length of the arrangement to be provided according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI, which can then adjust the length of the arrangement based on the emotion.

[0072] When generating arrangements, the generation unit can determine the priority of arrangements based on the creation dates of the songs. The generation unit, for example, acquires the creation dates of the songs. The creation dates of the songs are acquired based on, for example, the creation date or the release date. The generation unit determines the priority of arrangements based on the creation dates of the songs. For example, recently created songs can be arranged with priority. Also, older songs can be arranged later. Furthermore, songs that the user particularly likes can be arranged with priority. This makes it possible to provide arrangements according to the creation dates of the songs. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit can input song creation date data into the generation AI, which can then determine the priority of arrangements based on the creation dates.

[0073] When generating an arrangement, the generation unit can adjust the order of the arrangement based on the relevance of the songs. The generation unit, for example, evaluates the relevance of the songs. The relevance of the songs is evaluated based on, for example, the same artist or the same album. The generation unit adjusts the order of the arrangement based on the relevance of the songs. For example, songs in the same album can be arranged in descending order of relevance. Furthermore, songs by the same artist can be arranged in descending order of relevance. Furthermore, songs in a user's playlist can be arranged in descending order of relevance. This makes it possible to provide an arrangement based on the relevance of the songs. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit can input song relevance data into the generation AI, which can then adjust the order of the arrangement based on the relevance.

[0074] The providing unit can estimate the user's emotion and adjust the method of providing the arranged song based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. Emotion estimation is performed, for example, based on facial expression recognition, voice analysis, survey results, etc. The providing unit adjusts the method of providing the arranged song based on the estimated user's emotion. For example, if the user is relaxed, a song with a slow tempo can be provided. On the other hand, if the user is excited, a song with a fast tempo can be provided. Furthermore, if the user is sad, a song with a soothing effect can be provided. This makes it possible to provide a delivery method that corresponds to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can input the user's emotion data into the generation AI, and the generation AI can adjust the delivery method based on the emotion.

[0075] The providing unit can select the optimal providing method by referring to the user's past playback history when providing the music. The providing unit, for example, analyzes the user's past playback history. The playback history includes, for example, the number of plays and the playback time. The providing unit selects the optimal providing method based on this information. For example, the providing unit can provide songs arranged based on the style of songs the user frequently played in the past. Also, the providing unit can provide songs arranged based on the style of songs the user plays during a specific time period. Furthermore, the user's playback history can be analyzed to provide arrangements in the most preferred style. This makes it possible to provide a providing method based on the user's playback history. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's playback history data into AI, which can select the optimal providing method.

[0076] The providing unit can adjust the timing of providing the music based on the user's current activity status when providing the music. The providing unit, for example, acquires the user's current activity status. The current activity status is acquired based on, for example, whether the user is exercising, relaxing, working, etc. The providing unit adjusts the timing of providing the music based on the user's current activity status. For example, if the user is exercising, an energetic song can be provided. Also, if the user is relaxing, a relaxed song can be provided. Furthermore, if the user is working, a song that increases concentration can be provided. This makes it possible to provide the music at a timing that suits the user's activity status. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's activity status data into AI, which can then adjust the optimal timing of providing the music.

[0077] The providing unit can estimate the user's emotions and determine the priority of arranged songs based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions. Emotion estimation is performed, for example, based on facial expression recognition, voice analysis, survey results, etc. The providing unit determines the priority of arranged songs based on the estimated user's emotions. For example, if the user is relaxed, songs with a relaxing effect can be preferentially provided. Also, if the user is excited, energetic songs can be preferentially provided. Furthermore, if the user is sad, songs with a soothing effect can be preferentially provided. This allows songs to be prioritized according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can input the user's emotion data into a generation AI, which can then determine the priority of songs based on the emotion.

[0078] The providing unit can select the optimal providing method by taking into consideration the user's geographical location information when providing the content. The providing unit, for example, acquires the user's geographical location information. The geographical location information is acquired, for example, based on GPS data or a location information service. The providing unit selects the optimal providing method based on the user's geographical location information. For example, if the user is in a specific area, songs based on the music style of that area can be provided. Also, if the user is traveling, songs based on the music style of the travel destination can be provided. Furthermore, if the user is at home, songs with a relaxing effect can be provided. This makes it possible to provide a providing method based on the user's location information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into AI, which can select the optimal providing method.

[0079] The providing unit can analyze the user's social media activity and suggest a means of provision at the time of provision. The providing unit, for example, analyzes the user's social media activity. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc. The providing unit suggests a means of provision based on this information. For example, the providing unit can analyze trends in music shared by the user on social media and provide arranged songs. The providing unit can also provide songs arranged based on the musical style of artists the user follows. Furthermore, the providing unit can analyze the user's music-related posts on social media and provide optimally arranged songs. This makes it possible to provide a means of provision based on the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity into AI, which can then suggest the optimal means of provision. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and provides an interface for the user to select a song to arrange and input the desired arrangement parameters. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and arranges a song based on the arrangement parameters using a generation AI. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the arranged song to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and provides an interface for the user to select a song to arrange and input the desired arrangement parameters. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and arranges a song based on the arrangement parameters using a generation AI. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the arranged song to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and provides an interface for the user to select a song to arrange and input the desired arrangement parameters. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and arranges a song based on the arrangement parameters using a generation AI. The provision unit is realized, for example, by the speaker 240 of the headset-type terminal 314 and provides the arranged song to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and provides an interface for the user to select a song to arrange and input the desired arrangement parameters. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and arranges a song based on the arrangement parameters using a generation AI. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the arranged song to the user.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The reception unit can analyze the user's voice input and automatically set arrangement parameters based on the voice command. For example, if the user says "Speed ​​up the tempo," the reception unit can analyze this command using voice recognition technology and automatically set the tempo parameter. Also, if the user says "Add guitar," the instrument change parameter can be automatically set. Furthermore, if the user says "Increase the reverb," ​​the effect parameter can be adjusted. This allows the user to intuitively set arrangement parameters using voice commands.

[0082] The generation unit can learn the user's past arrangement history and suggest arrangements based on the user's preferences. For example, if the user has previously preferred pop arrangements, the generation unit can suggest pop arrangements for new songs. Also, if the user frequently uses a particular effect, the generation unit can suggest arrangements that include that effect. Furthermore, if the user has a preference for a particular instrument, the generation unit can suggest arrangements that include that instrument. This makes it possible to automatically suggest arrangements based on the user's preferences.

[0083] The providing unit can estimate the user's emotions and adjust the method of providing the arranged music based on the estimated user's emotions. For example, if the user is relaxed, a music with a slow tempo can be provided. If the user is excited, a music with a fast tempo can be provided. Furthermore, if the user is sad, a music with a soothing effect can be provided. This makes it possible to provide a music providing method that suits the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's emotion data into the generation AI, which can then adjust the music providing method based on the emotion.

[0084] When generating an arrangement, the generation unit can apply different arrangement algorithms depending on the song category. For example, an arrangement algorithm specialized for pop music can be applied to a pop song. Furthermore, an arrangement algorithm specialized for classical music can be applied to a classical song. Furthermore, an arrangement algorithm specialized for jazz music can be applied to a jazz song. This makes it possible to provide an arrangement that suits the song category. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit can input song category data into the generation AI, which can then apply an arrangement algorithm that suits the category.

[0085] The providing unit can prioritize the presentation of highly relevant arrange parameters taking into account the user's geographical location information. For example, if the user is in a specific area, arrange parameters based on the music style of that area can be suggested. Also, if the user is traveling, arrange parameters based on the music style of the travel destination can be suggested. Furthermore, if the user is at home, arrange parameters with a relaxing effect can be suggested. This makes it possible to provide arrange parameters based on the user's location information. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into AI, which can then present the optimal arrange parameters.

[0086] The generation unit can estimate the user's emotions and adjust the way the arrangement is expressed based on the estimated user's emotions. For example, if the user is relaxed, an arrangement with a slow tempo can be generated. If the user is excited, an arrangement with a fast tempo can be generated. Furthermore, if the user is sad, an arrangement with a soothing effect can be generated. This makes it possible to provide an arrangement that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI, which can then adjust the way the arrangement is expressed based on the emotion.

[0087] The reception unit can analyze the user's social media activity and suggest relevant arrangement parameters. For example, it can analyze the trends in music shared by the user on social media and suggest arrangement parameters. It can also suggest arrangement parameters based on the musical style of artists the user follows. It can also analyze the user's music-related posts on social media and suggest optimal arrangement parameters. This makes it possible to provide arrangement parameters based on the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity into AI, which then suggests optimal arrangement parameters.

[0088] The providing unit can estimate the user's emotions and prioritize the arranged songs based on the estimated user's emotions. For example, if the user is relaxed, songs with a relaxing effect can be provided preferentially. If the user is excited, energetic songs can be provided preferentially. If the user is sad, songs with a soothing effect can be provided preferentially. This allows songs to be prioritized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the providing unit can be performed using AI, or can be performed without using AI. For example, the providing unit can input the user's emotion data into the generation AI, which can then prioritize the songs based on the emotions.

[0089] When generating arrangements, the generation unit can determine the priority of arrangements based on the creation dates of the songs. For example, recently created songs can be given priority in arrangement. Also, older songs can be arranged later. Furthermore, songs that the user particularly likes can be given priority in arrangement. This makes it possible to provide arrangements according to the creation dates of the songs. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit can input data on the creation dates of the songs into the generation AI, which can then determine the priority of arrangements based on the creation dates.

[0090] The providing unit can adjust the timing of providing music based on the user's current activity status. For example, if the user is exercising, energetic music can be provided. If the user is relaxing, slow music can be provided. Furthermore, if the user is working, music that increases concentration can be provided. This makes it possible to provide music at a timing that suits the user's activity status. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's activity status data into AI, which can then adjust the optimal timing of providing music.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The reception unit receives arrangement parameter input from the user. The arrangement parameters from the user include, for example, tempo, melody, instrument changes, and the addition of effects. The reception unit provides an interface for the user to select the song they want to arrange and input the desired arrangement parameters. Step 2: The generation unit uses the generation AI to arrange the song based on the arrangement parameters received by the reception unit. The generation unit analyzes the input parameters and adjusts each element of the song (melody, rhythm, harmony, etc.). For example, it can speed up the tempo, change to a pop style, or add effects such as reverb or delay. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. Step 3: The providing unit provides the song arranged by the generating unit to the user. The providing unit can allow the user to download the arranged song to their device or play it via streaming.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0116] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0132] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0137] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0148] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0149] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0153] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0154] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0155] 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.

[0156] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0157] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0158] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0159] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0161] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0162] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0163] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0164] [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of arrangement parameters from a user; a generation unit that arranges a piece of music based on the arrangement parameters received by the reception unit; a providing unit that provides the music arranged by the generating unit to a user. A system characterized by:

2. The reception unit Accepts arrangement parameters including tempo, melody, instrument changes, and added effects 2. The system of claim 1.

3. The generation unit Adjust each element of the song, including melody, rhythm, and harmony, based on the arrangement parameters you enter 2. The system of claim 1.

4. The generation unit Speed ​​up the tempo, change it to a pop style, and add effects including reverb and delay.

2. The system of claim 1.

5. The providing unit Providing arranged songs to users 2. The system of claim 1.

6. The reception unit Estimating a user's emotion and customizing an input interface for arrangement parameters based on the estimated user's emotion 2. The system of claim 1.

7. The reception unit Analyzes the user's past arrangement history and presents suitable arrangement parameter candidates 2. The system of claim 1.

8. The reception unit When entering arrangement parameters, filtering is performed based on the user's musical tastes and past playback history.

2. The system of claim 1.

Images