system

Abstract

The system according to this embodiment aims to enable users to efficiently suggest and select melodies, chords, and arrangements. [Solution] The system according to the embodiment comprises a reception unit, a suggestion unit, an arrangement unit, and a matching unit. The reception unit receives input from the user. The suggestion unit suggests a melody or chord based on the input received by the reception unit. The arrangement unit suggests instrument selection or arrangement based on the melody or chord suggested by the suggestion unit. The matching unit matches the arranged song or rhythm with an existing song or rhythm that is similar to the instrument selection or arrangement suggested by the arrangement unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document Ⅰ

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for a user to efficiently propose and select melodies, chords, and arrangements in music composition, and the freedom of creation is restricted.

[0005] The system according to the embodiment aims to enable a user to efficiently propose and select melodies, chords, and arrangements.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a suggestion unit, an arrangement unit, and a matching unit. The reception unit receives input from the user. The suggestion unit suggests a melody or chord based on the input received by the reception unit. The arrangement unit suggests instrument selection or arrangement based on the melody or chord suggested by the suggestion unit. The matching unit matches the arranged song or rhythm with an existing song or rhythm that is similar to the instrument selection or arrangement suggested by the arrangement unit. [Effects of the Invention]

[0007] The system according to this embodiment can enable the user to efficiently suggest and select melodies, chords, and arrangements. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The music composition support AI assistant according to an embodiment of the present invention is a system that proposes melodies and chords based on user input, suggests instrument selections and arrangements according to the genre and style of music, and matches them with existing songs and rhythm patterns. The music composition support AI assistant proposes melodies or chords based on user input, suggests instrument selections and arrangements, and matches them with existing songs and rhythm patterns. For example, if the user inputs "I want to create a cheerful melody," the AI ​​will propose a melody based on that input. Next, based on the proposed melody, the AI ​​will suggest instrument selections and arrangements. For example, the AI ​​will suggest guitar or piano as instruments that suit a "cheerful melody," and will also suggest changes to the rhythm and tempo. Finally, it will search for existing songs similar to the arranged song and propose them to the user. This mechanism allows the user to easily receive suggestions for melodies and chords, as well as suggestions for instrument selections and arrangements. Furthermore, by matching with existing songs and rhythm patterns, new ideas can be obtained. For example, if the user inputs "I want to create a sad melody," the AI ​​will generate a "sad melody" and propose it to the user. The AI ​​then suggests instruments like the violin or piano as suitable for a "sad-sounding melody," and proposes changes to the rhythm and tempo. Finally, the AI ​​searches for existing songs similar to the arranged song and suggests them to the user. This allows the music composition support AI assistant to suggest melodies and chords, select instruments and arrangements, and match them with existing songs and rhythm patterns based on the user's input.

[0029] The music composition support AI assistant according to this embodiment comprises a reception unit, a suggestion unit, an arrangement unit, and a matching unit. The reception unit receives input from the user. User input includes, but is not limited to, requests for melodies or chords, or specifications of musical genres and styles. The reception unit can receive requests from the user using, for example, text input or voice input. The suggestion unit suggests melodies or chords based on the input received by the reception unit. The suggestion unit generates melodies based on the input requests using, for example, AI. The suggestion unit can also suggest chord progressions. For example, if the user requests a "cheerful melody," the suggestion unit generates a cheerful melody and suggests it to the user. The arrangement unit suggests instrument selection or arrangement based on the melody or chords suggested by the suggestion unit. The arrangement unit selects instruments that suit the suggested melody using, for example, AI. The arrangement unit can also suggest changes to rhythm and tempo. For example, the arrangement unit suggests guitar or piano as instruments that suit a cheerful melody and suggests changes to rhythm and tempo. The matching unit matches the arranged song or rhythm with existing songs or rhythms that are similar, based on the instrument selection or arrangement proposed by the arrangement unit. The matching unit, for example, uses AI to search for existing songs similar to the arranged song and proposes them to the user. As a result, the music composition support AI assistant according to the embodiment can propose melodies and chords, suggest instrument selections and arrangements, and match with existing songs and rhythm patterns based on the user's input.

[0030] The reception desk receives input from the user. User input includes, but is not limited to, requests for melodies and chords, or specifications for musical genres and styles. The reception desk can receive user requests using, for example, text input or voice input. Specifically, in the case of text input, the user uses a keyboard to input the desired melody, chords, genre, style, etc. In the case of voice input, the user dictates their requests through a microphone, and speech recognition technology converts them into text. Furthermore, the reception desk can analyze the user's input and request additional information as needed. For example, if the user inputs "I want to make a rock-style song," the reception desk can ask additional questions such as "Please tell me some specific artists or song examples" to gather more detailed requests. The reception desk can also refer to the user's past input history to understand the user's preferences and tendencies. This allows the reception desk to accurately understand the user's requests and provide them as input data to the next step, the suggestion desk. Furthermore, the reception desk can use multiple input methods in combination. For example, it can use text input and voice input together, allowing the user to choose the method that is easiest for them to use. This allows the reception desk to respond to the diverse needs of users and provide a smooth input experience.

[0031] The suggestion unit proposes melodies or chords based on input received by the reception unit. For example, the suggestion unit generates melodies based on input preferences using AI. The suggestion unit can also propose chord progressions. Specifically, the suggestion unit uses a generation AI to generate melodies and chord progressions that meet the user's preferences. For example, if the user requests a "cheerful melody," the generation AI will generate a cheerful melody and propose it to the user. In this case, the generation AI uses an algorithm based on music theory to generate harmonious melodies and chord progressions. Furthermore, the suggestion unit can provide more personalized suggestions by considering the user's past input history and preferences. For example, if the user previously preferred "jazz-style chord progressions," the suggestion unit can propose a jazz-style chord progression that meets the current preference. The suggestion unit also visually displays the generated melodies and chord progressions to make them easily understandable to the user. For example, they can be displayed in sheet music format or piano roll format. This allows the suggestion unit to quickly and accurately propose melodies and chord progressions that meet the user's preferences, supporting the user's creative activities. Furthermore, the suggestion unit provides a function to preview the generated melodies and chord progressions, making it easier for the user to review the suggestions. This allows the suggestion unit to facilitate the user's creative process.

[0032] The arrangement unit proposes instrument selections or arrangements based on the melody or chords suggested by the proposal unit. For example, the arrangement unit uses AI to select instruments that suit the proposed melody. Specifically, the arrangement unit uses generative AI to select the most suitable instruments for the proposed melody and chord progression. For example, it can suggest guitar or piano as instruments that suit a bright-sounding melody. The arrangement unit can also suggest changes to rhythm and tempo. For example, it can suggest an up-tempo or slow-tempo rhythm for the proposed melody, allowing the user to choose. Furthermore, the arrangement unit can also suggest settings for instrument timbre and effects. For example, it can suggest detailed settings such as whether to make the guitar timbre clean or distorted, or whether to make the piano timbre acoustic or electric. This allows the arrangement unit to propose a variety of arrangements according to the user's wishes, thereby improving the quality of the song. The arrangement unit also provides a function to preview the proposed arrangements, making it easier for the user to check the arrangement content. This allows the arrangement unit to smoothly advance the user's creative process. Furthermore, the arrangement unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the arrangement unit to provide optimal arrangements tailored to user needs, thereby enhancing the overall quality of the music.

[0033] The matching unit matches the arranged song or rhythm with existing songs or rhythms that are similar, based on the instrument selection or arrangement proposed by the arrangement unit. For example, the matching unit uses AI to search for existing songs similar to the arranged song and suggests them to the user. Specifically, the matching unit uses generative AI to analyze the characteristics of the arranged song and searches for similar songs in the music database. For example, if the arranged song is pop-style, it can search for existing pop songs and suggest them to the user. Furthermore, the matching unit can analyze the rhythm patterns and chord progressions of similar songs, providing information that the user can use as a reference. This allows the matching unit to provide inspiration for the user to further develop the arranged song. The matching unit can also provide more personalized suggestions by considering the user's preferences and past selection history. For example, it can suggest similar songs based on artists and musical styles the user has previously liked. This allows the matching unit to support the user's creative activities and improve the quality of the music. Additionally, the matching unit provides a function to preview the suggested similar songs, making it easier for the user to review the suggestions. This allows the matching unit to smoothly advance the user's creative process.

[0034] The suggestion unit can propose melodies or chords based on information about the user's preferences. For example, the suggestion unit can use AI to generate melodies and chords based on the user's input preferences. For instance, if the user requests a "jazz-style melody," the suggestion unit will generate a jazz-style melody and propose it to the user. The suggestion unit can also analyze the user's past music selection history and propose melodies and chords that match their preferences. For example, the suggestion unit can generate similar melodies and chords based on data of songs the user has enjoyed listening to in the past. This makes it possible to propose melodies and chords based on the user's preferences.

[0035] The arrangement section can suggest instrument selections or arrangements according to the genre or style of music. For example, the arrangement section uses AI to select instruments that match the genre and style of music. For instance, for classical music, the arrangement section might suggest violins and pianos, and for jazz, saxophones and trumpets. The arrangement section can also suggest changes in rhythm and tempo according to the style of music. For example, for rock music, the arrangement section might suggest a fast tempo rhythm, and for ballads, a slow tempo rhythm. This makes it possible to suggest instrument selections and arrangements that are appropriate for the genre and style of music.

[0036] The reception desk can analyze the user's past input history and select the optimal reception method. For example, the reception desk can use AI to analyze the user's past input history. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the reception desk can suggest similar input methods by referring to content the user has entered in the past. This improves input efficiency by selecting the optimal reception method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI select the optimal reception method.

[0037] The reception unit can filter input based on the user's current projects and areas of interest when receiving it. For example, the reception unit can use AI to analyze the user's current projects and areas of interest. For example, the reception unit can only accept input related to the project the user is currently working on. The reception unit can also prioritize accepting relevant input based on the user's areas of interest. For example, the reception unit can filter input based on topics the user has shown interest in in the past. This allows for the priority acceptance of highly relevant input by filtering input based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's project data and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0038] The reception unit can prioritize receiving inputs that are highly relevant, taking into account the user's geographical location information. For example, the reception unit can analyze the user's geographical location information using AI. For instance, if the user is in a specific region, the reception unit can prioritize receiving inputs related to that region. Similarly, if the user is traveling, the reception unit can prioritize receiving inputs related to their travel destination. For example, if the user is at home, the reception unit can prioritize receiving inputs related to their home. This allows for the reception of more appropriate inputs by prioritizing highly relevant inputs based on the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI prioritize receiving highly relevant inputs.

[0039] The reception unit can analyze the user's social media activity and accept relevant inputs when receiving input. For example, the reception unit can use AI to analyze the user's social media activity. For example, the reception unit can accept relevant inputs based on information shared by the user on social media. The reception unit can also accept relevant inputs based on accounts followed by the user on social media. For example, the reception unit can accept inputs based on topics the user has shown interest in on social media. This allows for more appropriate inputs to be received by accepting relevant inputs based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI perform the acceptance of relevant inputs.

[0040] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the melody and chords. For example, the suggestion unit can use AI to evaluate the importance of the melody and chords. For example, the suggestion unit can provide detailed suggestions for important melodies and chords. Conversely, it can provide concise suggestions for less important melodies and chords. For example, the suggestion unit can provide detailed suggestions for melodies and chords that the user particularly values. By adjusting the level of detail in suggestions based on the importance of the melody and chords, more appropriate suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input melody and chord importance data into a generating AI and have the generating AI adjust the level of detail in suggestions based on importance.

[0041] The suggestion unit can apply different suggestion algorithms depending on the genre and style of music during the suggestion process. For example, the suggestion unit can use AI to analyze the genre and style of music and select an appropriate suggestion algorithm. For instance, in the case of jazz, the suggestion unit can suggest melodies and chords suitable for improvisation. In the case of classical music, the suggestion unit can also suggest melodies and chords based on traditional harmonic progressions. For example, in the case of pop music, the suggestion unit can suggest catchy melodies and chords. This enables suggestions tailored to the genre and style of music. Some or all of the above-described processes in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input music genre and style data into a generating AI and have the generating AI select an appropriate suggestion algorithm.

[0042] The proposal department can determine the priority of proposals based on the submission timing of melodies and chords. For example, the proposal department may use AI to evaluate the submission timing of melodies and chords. For example, the proposal department may prioritize melodies and chords submitted earlier. It may also postpone melodies and chords submitted later. For example, the proposal department may adjust the order of proposals based on the submission timing. This allows for more appropriate proposals by determining the priority of proposals based on the submission timing of melodies and chords. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department may input melody and chord submission timing data into a generating AI and have the generating AI determine the priority of proposals based on the submission timing.

[0043] The suggestion unit can adjust the order of suggestions based on the relationships between melodies and chords during the suggestion process. For example, the suggestion unit can use AI to evaluate the relationships between melodies and chords. For example, the suggestion unit can prioritize suggesting melodies and chords with high relevance. It can also postpone suggesting melodies and chords with low relevance. For example, the suggestion unit can adjust the order of suggestions based on relevance. By adjusting the order of suggestions based on the relationships between melodies and chords, more appropriate suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input relationship data between melodies and chords into a generating AI and have the generating AI perform the adjustment of the suggestion order based on relevance.

[0044] The arrangement unit can analyze the user's past arrangement history to select the optimal arrangement method during the arrangement process. For example, the arrangement unit can use AI to analyze the user's past arrangement history. For example, the arrangement unit can prioritize suggesting arrangement methods that the user has preferred to use in the past. The arrangement unit can also suggest arrangement methods suitable for a specific style based on the user's past arrangement history. For example, the arrangement unit can suggest the optimal arrangement method by referring to arrangement methods that the user has successfully used in the past. This allows for more appropriate arrangements by selecting the optimal arrangement method based on the user's past arrangement history. Some or all of the above processing in the arrangement unit may be performed using AI, for example, or without AI. For example, the arrangement unit can input the user's past arrangement data into a generating AI and have the generating AI select the optimal arrangement method.

[0045] The arrangement unit can customize the arrangement methods based on the user's current music project during the arrangement process. For example, the arrangement unit can use AI to analyze the user's current music project. For example, the arrangement unit can suggest an arrangement method suitable for the project the user is currently working on. The arrangement unit can also adjust the arrangement methods based on the progress of the user's project. For example, the arrangement unit can customize the arrangement methods to match the goals of the user's project. This allows for more appropriate arrangements by customizing the arrangement methods based on the user's current music project. Some or all of the above processes in the arrangement unit may be performed using AI, for example, or without AI. For example, the arrangement unit can input the user's project data into a generating AI and have the generating AI perform the customization of the arrangement methods.

[0046] The arrangement unit can select the optimal arrangement method by considering the user's geographical location information during the arrangement process. For example, the arrangement unit can analyze the user's geographical location information using AI. For example, if the user is in a specific region, the arrangement unit can suggest an arrangement method related to that region. Also, if the user is traveling, the arrangement unit can suggest an arrangement method related to the travel destination. For example, if the user is at home, the arrangement unit can suggest an arrangement method related to home. This allows for more appropriate arrangements by selecting the optimal arrangement method based on the user's geographical location information. Some or all of the above processing in the arrangement unit may be performed using AI, for example, or without AI. For example, the arrangement unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal arrangement method.

[0047] The arrangement unit can analyze the user's social media activity and propose arrangement methods during the arrangement process. For example, the arrangement unit can use AI to analyze the user's social media activity. For example, the arrangement unit can propose relevant arrangement methods based on information shared by the user on social media. The arrangement unit can also propose relevant arrangement methods based on accounts followed by the user on social media. For example, the arrangement unit can propose arrangement methods based on topics the user has shown interest in on social media. This allows for more appropriate arrangements by proposing arrangement methods based on the user's social media activity. Some or all of the above processing in the arrangement unit may be performed using AI, for example, or without AI. For example, the arrangement unit can input the user's social media data into a generating AI and have the generating AI propose arrangement methods.

[0048] The matching unit can improve the accuracy of matching by considering the interrelationships between the arranged songs and rhythms during the matching process. For example, the matching unit can use AI to analyze the interrelationships between the arranged songs and rhythms. For example, the matching unit can perform optimal matching by considering the harmony between the arranged songs and rhythms. The matching unit can also perform optimal matching by considering the tempo of the arranged songs and rhythms. For example, the matching unit can perform optimal matching by considering the key of the arranged songs and rhythms. This improves the accuracy of matching by considering the interrelationships between the arranged songs and rhythms. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input data on the arranged songs and rhythms into a generating AI and have the generating AI perform accuracy improvements for matching that consider the interrelationships.

[0049] The matching unit can perform matching while considering the user's music history and preferences. For example, the matching unit can use AI to analyze the user's music history and preferences. For example, the matching unit can perform optimal matching based on songs the user has enjoyed listening to in the past. The matching unit can also perform matching suitable for specific genres or styles based on the user's music history. For example, the matching unit can perform optimal matching based on the user's preferences. This makes it possible to perform more appropriate matching by performing matching based on the user's music history and preferences. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the user's music history and preference data into a generating AI and have the generating AI perform optimal matching.

[0050] The matching unit can perform matching while considering the geographical distribution of the arranged songs and rhythms. For example, the matching unit can use AI to analyze the geographical distribution of the arranged songs and rhythms. For example, the matching unit can prioritize matching songs and rhythms that are popular in a particular region. The matching unit can also prioritize matching songs and rhythms related to the region where the user is currently located. For example, the matching unit performs optimal matching based on geographical distribution. This makes it possible to perform more appropriate matching by considering the geographical distribution of the arranged songs and rhythms. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input geographical distribution data of the arranged songs and rhythms into a generating AI and have the generating AI perform matching that takes geographical distribution into account.

[0051] The matching unit can improve the accuracy of matching by referring to relevant literature on the arranged song or rhythm during the matching process. For example, the matching unit can use AI to analyze relevant literature on the arranged song or rhythm. For example, the matching unit can refer to academic papers related to the arranged song or rhythm to perform the optimal matching. The matching unit can also refer to music theory books related to the arranged song or rhythm to perform the optimal matching. For example, the matching unit can refer to review articles related to the arranged song or rhythm to perform the optimal matching. This improves the accuracy of matching by referring to relevant literature on the arranged song or rhythm. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input data on relevant literature on the arranged song or rhythm into a generating AI and have the generating AI perform the accuracy improvement of matching by referring to relevant literature.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The music composition support AI assistant can also be equipped with a feedback unit. The feedback unit can collect feedback from the user and use it to improve the algorithms of the suggestion and arrangement units. For example, if the user inputs a rating of "good" or "bad" for a suggested melody or chord, the feedback unit will record that rating and provide feedback to the suggestion unit. The feedback unit can also receive specific comments from the user on the suggested arrangement. For example, it can collect comments such as "This rhythm is too fast" or "I don't like the sound of this instrument" and provide them to the arrangement unit. This allows the feedback unit to make suggestions and arrangements that reflect the user's preferences and opinions. Furthermore, the feedback unit can analyze the user's feedback and automatically adjust the algorithms of the suggestion and arrangement units. For example, the feedback unit can improve the melody generation algorithm of the suggestion unit based on the user's evaluation data.

[0054] The suggestion unit can analyze the user's past music selection history and suggest melodies and chords that match their preferences. For example, the suggestion unit can generate similar melodies and chords based on data of songs the user has enjoyed listening to in the past. It can also suggest new melodies and chords that match the user's preferences based on data of melodies and chords the user has created in the past. Furthermore, the suggestion unit can analyze the user's past music selection history and suggest melodies and chords suitable for specific genres or styles. This allows the suggestion unit to suggest more appropriate melodies and chords based on the user's preferences.

[0055] The styling function can analyze the user's past styling history and select the optimal styling method. For example, the styling function will prioritize suggesting styling methods that the user has preferred in the past. It can also suggest styling methods suitable for specific styles based on the user's past styling history. Furthermore, the styling function can suggest the optimal styling method by referencing successful styling methods used by the user in the past. This allows the styling function to suggest more appropriate styling based on the user's past styling history.

[0056] The matching unit can perform matching based on the user's music history and preferences. For example, the matching unit can perform optimal matching based on songs the user has enjoyed listening to in the past. It can also perform matching based on the user's music history to find songs suitable for specific genres or styles. Furthermore, the matching unit can perform optimal matching based on the user's preferences. This allows the matching unit to suggest more appropriate matches based on the user's music history and preferences.

[0057] The reception desk can prioritize receiving input that is highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving input related to that region. Furthermore, if the user is traveling, the reception desk can prioritize receiving input related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize receiving input related to their home. This allows the reception desk to receive more appropriate input based on the user's geographical location.

[0058] The suggestion function can adjust the level of detail in its suggestions based on the importance of the melody and chords. For example, it will provide detailed suggestions for important melodies and chords, and concise suggestions for less important ones. Furthermore, it can provide detailed suggestions for melodies and chords that the user particularly values. This allows the suggestion function to provide more appropriate suggestions based on the importance of the melodies and chords.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk receives input from the user. User input includes preferences for melodies and chords, as well as specifications for music genre and style. The reception desk can receive user preferences via text input or voice input. Step 2: The suggestion unit proposes a melody or chord based on the input received by the reception unit. The suggestion unit can also use AI to generate a melody and propose a chord progression based on the input request. For example, if the user requests a "cheerful melody," the suggestion unit will generate a cheerful melody and propose it to the user. Step 3: The arrangement unit proposes instrument selections or arrangements based on the melody or chords suggested by the proposal unit. The arrangement unit can also use AI to select instruments that suit the proposed melody and suggest changes to the rhythm and tempo. For example, it might suggest guitar or piano as instruments that suit a cheerful melody and suggest changes to the rhythm and tempo. Step 4: The matching unit matches the arranged song or rhythm with existing songs or rhythms that are similar to the instrument selection or arrangement proposed by the arrangement unit. The matching unit uses AI to search for existing songs similar to the arranged song and suggests them to the user.

[0061] (Example of form 2) The music composition support AI assistant according to an embodiment of the present invention is a system that proposes melodies and chords based on user input, suggests instrument selections and arrangements according to the genre and style of music, and matches them with existing songs and rhythm patterns. The music composition support AI assistant proposes melodies or chords based on user input, suggests instrument selections and arrangements, and matches them with existing songs and rhythm patterns. For example, if the user inputs "I want to create a cheerful melody," the AI ​​will propose a melody based on that input. Next, based on the proposed melody, the AI ​​will suggest instrument selections and arrangements. For example, the AI ​​will suggest guitar or piano as instruments that suit a "cheerful melody," and will also suggest changes to the rhythm and tempo. Finally, it will search for existing songs similar to the arranged song and propose them to the user. This mechanism allows the user to easily receive suggestions for melodies and chords, as well as suggestions for instrument selections and arrangements. Furthermore, by matching with existing songs and rhythm patterns, new ideas can be obtained. For example, if the user inputs "I want to create a sad melody," the AI ​​will generate a "sad melody" and propose it to the user. The AI ​​then suggests instruments like the violin or piano as suitable for a "sad-sounding melody," and proposes changes to the rhythm and tempo. Finally, the AI ​​searches for existing songs similar to the arranged song and suggests them to the user. This allows the music composition support AI assistant to suggest melodies and chords, select instruments and arrangements, and match them with existing songs and rhythm patterns based on the user's input.

[0062] The music composition support AI assistant according to this embodiment comprises a reception unit, a suggestion unit, an arrangement unit, and a matching unit. The reception unit receives input from the user. User input includes, but is not limited to, requests for melodies or chords, or specifications of musical genres and styles. The reception unit can receive requests from the user using, for example, text input or voice input. The suggestion unit suggests melodies or chords based on the input received by the reception unit. The suggestion unit generates melodies based on the input requests using, for example, AI. The suggestion unit can also suggest chord progressions. For example, if the user requests a "cheerful melody," the suggestion unit generates a cheerful melody and suggests it to the user. The arrangement unit suggests instrument selection or arrangement based on the melody or chords suggested by the suggestion unit. The arrangement unit selects instruments that suit the suggested melody using, for example, AI. The arrangement unit can also suggest changes to rhythm and tempo. For example, the arrangement unit suggests guitar or piano as instruments that suit a cheerful melody and suggests changes to rhythm and tempo. The matching unit matches the arranged song or rhythm with existing songs or rhythms that are similar, based on the instrument selection or arrangement proposed by the arrangement unit. The matching unit, for example, uses AI to search for existing songs similar to the arranged song and proposes them to the user. As a result, the music composition support AI assistant according to the embodiment can propose melodies and chords, suggest instrument selections and arrangements, and match with existing songs and rhythm patterns based on the user's input.

[0063] The reception desk receives input from the user. User input includes, but is not limited to, requests for melodies and chords, or specifications for musical genres and styles. The reception desk can receive user requests using, for example, text input or voice input. Specifically, in the case of text input, the user uses a keyboard to input the desired melody, chords, genre, style, etc. In the case of voice input, the user dictates their requests through a microphone, and speech recognition technology converts them into text. Furthermore, the reception desk can analyze the user's input and request additional information as needed. For example, if the user inputs "I want to make a rock-style song," the reception desk can ask additional questions such as "Please tell me some specific artists or song examples" to gather more detailed requests. The reception desk can also refer to the user's past input history to understand the user's preferences and tendencies. This allows the reception desk to accurately understand the user's requests and provide them as input data to the next step, the suggestion desk. Furthermore, the reception desk can use multiple input methods in combination. For example, it can use text input and voice input together, allowing the user to choose the method that is easiest for them to use. This allows the reception desk to respond to the diverse needs of users and provide a smooth input experience.

[0064] The suggestion unit proposes melodies or chords based on input received by the reception unit. For example, the suggestion unit generates melodies based on input preferences using AI. The suggestion unit can also propose chord progressions. Specifically, the suggestion unit uses a generation AI to generate melodies and chord progressions that meet the user's preferences. For example, if the user requests a "cheerful melody," the generation AI will generate a cheerful melody and propose it to the user. In this case, the generation AI uses an algorithm based on music theory to generate harmonious melodies and chord progressions. Furthermore, the suggestion unit can provide more personalized suggestions by considering the user's past input history and preferences. For example, if the user previously preferred "jazz-style chord progressions," the suggestion unit can propose a jazz-style chord progression that meets the current preference. The suggestion unit also visually displays the generated melodies and chord progressions to make them easily understandable to the user. For example, they can be displayed in sheet music format or piano roll format. This allows the suggestion unit to quickly and accurately propose melodies and chord progressions that meet the user's preferences, supporting the user's creative activities. Furthermore, the suggestion unit provides a function to preview the generated melodies and chord progressions, making it easier for the user to review the suggestions. This allows the suggestion unit to facilitate the user's creative process.

[0065] The arrangement unit proposes instrument selections or arrangements based on the melody or chords suggested by the proposal unit. For example, the arrangement unit uses AI to select instruments that suit the proposed melody. Specifically, the arrangement unit uses generative AI to select the most suitable instruments for the proposed melody and chord progression. For example, it can suggest guitar or piano as instruments that suit a bright-sounding melody. The arrangement unit can also suggest changes to rhythm and tempo. For example, it can suggest an up-tempo or slow-tempo rhythm for the proposed melody, allowing the user to choose. Furthermore, the arrangement unit can also suggest settings for instrument timbre and effects. For example, it can suggest detailed settings such as whether to make the guitar timbre clean or distorted, or whether to make the piano timbre acoustic or electric. This allows the arrangement unit to propose a variety of arrangements according to the user's wishes, thereby improving the quality of the song. The arrangement unit also provides a function to preview the proposed arrangements, making it easier for the user to check the arrangement content. This allows the arrangement unit to smoothly advance the user's creative process. Furthermore, the arrangement unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the arrangement unit to provide optimal arrangements tailored to user needs, thereby enhancing the overall quality of the music.

[0066] The matching unit matches the arranged song or rhythm with existing songs or rhythms that are similar, based on the instrument selection or arrangement proposed by the arrangement unit. For example, the matching unit uses AI to search for existing songs similar to the arranged song and suggests them to the user. Specifically, the matching unit uses generative AI to analyze the characteristics of the arranged song and searches for similar songs in the music database. For example, if the arranged song is pop-style, it can search for existing pop songs and suggest them to the user. Furthermore, the matching unit can analyze the rhythm patterns and chord progressions of similar songs, providing information that the user can use as a reference. This allows the matching unit to provide inspiration for the user to further develop the arranged song. The matching unit can also provide more personalized suggestions by considering the user's preferences and past selection history. For example, it can suggest similar songs based on artists and musical styles the user has previously liked. This allows the matching unit to support the user's creative activities and improve the quality of the music. Additionally, the matching unit provides a function to preview the suggested similar songs, making it easier for the user to review the suggestions. This allows the matching unit to smoothly advance the user's creative process.

[0067] The suggestion unit can propose melodies or chords based on information about the user's preferences. For example, the suggestion unit can use AI to generate melodies and chords based on the user's input preferences. For instance, if the user requests a "jazz-style melody," the suggestion unit will generate a jazz-style melody and propose it to the user. The suggestion unit can also analyze the user's past music selection history and propose melodies and chords that match their preferences. For example, the suggestion unit can generate similar melodies and chords based on data of songs the user has enjoyed listening to in the past. This makes it possible to propose melodies and chords based on the user's preferences.

[0068] The arrangement section can suggest instrument selections or arrangements according to the genre or style of music. For example, the arrangement section uses AI to select instruments that match the genre and style of music. For instance, for classical music, the arrangement section might suggest violins and pianos, and for jazz, saxophones and trumpets. The arrangement section can also suggest changes in rhythm and tempo according to the style of music. For example, for rock music, the arrangement section might suggest a fast tempo rhythm, and for ballads, a slow tempo rhythm. This makes it possible to suggest instrument selections and arrangements that are appropriate for the genre and style of music.

[0069] The suggestion unit can estimate the user's emotions and suggest a melody or chord based on the estimated emotions. The suggestion unit can estimate the user's emotions using, for example, an emotion engine or a generative AI. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The suggestion unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the suggestion unit can analyze the tone and speed of the user's voice and estimate the emotions. Furthermore, the suggestion unit can analyze the user's text input and estimate the emotions. For example, the suggestion unit can analyze the content of the text entered by the user and estimate the emotions. This makes it possible to suggest melodies and chords based on the user's emotions. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input the user's emotion data into a generative AI and have the generative AI generate melodies and chords based on the emotions.

[0070] The arrangement unit can estimate the user's emotions and suggest instrument selections or arrangements based on the estimated emotions. The arrangement unit can estimate the user's emotions using, for example, an emotion engine or generative AI. For example, the arrangement unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. The arrangement unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the arrangement unit can analyze the tone and speed of the user's voice and estimate emotions. Furthermore, the arrangement unit can analyze the user's text input and estimate emotions. For example, the arrangement unit can analyze the content of the text entered by the user and estimate emotions. This makes it possible to suggest instrument selections and arrangements based on the user's emotions. Some or all of the above processing in the arrangement unit may be performed using, for example, AI, or not using AI. For example, the arrangement unit can input the user's emotion data into a generative AI and have the generative AI perform emotion-based instrument selections and arrangement generation.

[0071] The matching unit can estimate the user's emotions and match them with existing songs or rhythms based on the estimated emotions. The matching unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the matching unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The matching unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the matching unit can analyze the tone and speed of the user's voice and estimate the emotions. Furthermore, the matching unit can analyze the user's text input and estimate the emotions. For example, the matching unit can analyze the content of the text entered by the user and estimate the emotions. This makes it possible to match existing songs or rhythms based on the user's emotions. Some or all of the above processing in the matching unit may be performed using, for example, AI, or not using AI. For example, the matching unit can input the user's emotion data into a generative AI and have the generative AI perform emotion-based matching.

[0072] The reception unit can estimate the user's emotions and adjust the timing of input reception based on the estimated emotions. The reception unit can estimate the user's emotions using, for example, an emotion engine or generative AI. For example, the reception unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the user's voice and estimate the emotions. Furthermore, the reception unit can analyze the user's text input and estimate the emotions. For example, the reception unit can analyze the content of the text entered by the user and estimate the emotions. This allows for input to be received at a more appropriate time by adjusting the timing of input reception based on the user's emotions. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's emotion data into a generative AI and have the generative AI perform the adjustment of the timing of input reception based on emotions.

[0073] The reception desk can analyze the user's past input history and select the optimal reception method. For example, the reception desk can use AI to analyze the user's past input history. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the reception desk can suggest similar input methods by referring to content the user has entered in the past. This improves input efficiency by selecting the optimal reception method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI select the optimal reception method.

[0074] The reception unit can filter input based on the user's current projects and areas of interest when receiving it. For example, the reception unit can use AI to analyze the user's current projects and areas of interest. For example, the reception unit can only accept input related to the project the user is currently working on. The reception unit can also prioritize accepting relevant input based on the user's areas of interest. For example, the reception unit can filter input based on topics the user has shown interest in in the past. This allows for the priority acceptance of highly relevant input by filtering input based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's project data and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0075] The reception unit can estimate the user's emotions and determine the priority of inputs to be received based on the estimated emotions. The reception unit can estimate the user's emotions using, for example, an emotion engine or generative AI. For example, the reception unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the user's voice and estimate their emotions. Furthermore, the reception unit can analyze the user's text input and estimate their emotions. For example, the reception unit can analyze the content of the text entered by the user and estimate their emotions. This allows for prioritizing inputs based on the user's emotions, thereby prioritizing the acceptance of more appropriate inputs. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's emotion data into a generative AI and have the generative AI perform the determination of input priorities based on emotions.

[0076] The reception unit can prioritize receiving inputs that are highly relevant, taking into account the user's geographical location information. For example, the reception unit can analyze the user's geographical location information using AI. For instance, if the user is in a specific region, the reception unit can prioritize receiving inputs related to that region. Similarly, if the user is traveling, the reception unit can prioritize receiving inputs related to their travel destination. For example, if the user is at home, the reception unit can prioritize receiving inputs related to their home. This allows for the reception of more appropriate inputs by prioritizing highly relevant inputs based on the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI prioritize receiving highly relevant inputs.

[0077] The reception unit can analyze the user's social media activity and accept relevant inputs when receiving input. For example, the reception unit can use AI to analyze the user's social media activity. For example, the reception unit can accept relevant inputs based on information shared by the user on social media. The reception unit can also accept relevant inputs based on accounts followed by the user on social media. For example, the reception unit can accept inputs based on topics the user has shown interest in on social media. This allows for more appropriate inputs to be received by accepting relevant inputs based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI perform the acceptance of relevant inputs.

[0078] The suggestion unit can estimate the user's emotions and adjust the expression of melodies and chords based on the estimated emotions. The suggestion unit can estimate the user's emotions using, for example, an emotion engine or generative AI. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The suggestion unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the suggestion unit can analyze the tone and speed of the user's voice and estimate the emotions. Furthermore, the suggestion unit can analyze the user's text input and estimate the emotions. For example, the suggestion unit can analyze the content of the text entered by the user and estimate the emotions. This allows for more appropriate suggestions by adjusting the expression of melodies and chords based on the user's emotions. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input the user's emotion data into a generative AI and have the generative AI perform adjustments to the expression of melodies and chords based on the emotions.

[0079] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the melody and chords. For example, the suggestion unit can use AI to evaluate the importance of the melody and chords. For example, the suggestion unit can provide detailed suggestions for important melodies and chords. Conversely, it can provide concise suggestions for less important melodies and chords. For example, the suggestion unit can provide detailed suggestions for melodies and chords that the user particularly values. By adjusting the level of detail in suggestions based on the importance of the melody and chords, more appropriate suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input melody and chord importance data into a generating AI and have the generating AI adjust the level of detail in suggestions based on importance.

[0080] The suggestion unit can apply different suggestion algorithms depending on the genre and style of music during the suggestion process. For example, the suggestion unit can use AI to analyze the genre and style of music and select an appropriate suggestion algorithm. For instance, in the case of jazz, the suggestion unit can suggest melodies and chords suitable for improvisation. In the case of classical music, the suggestion unit can also suggest melodies and chords based on traditional harmonic progressions. For example, in the case of pop music, the suggestion unit can suggest catchy melodies and chords. This enables suggestions tailored to the genre and style of music. Some or all of the above-described processes in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input music genre and style data into a generating AI and have the generating AI select an appropriate suggestion algorithm.

[0081] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. The suggestion unit can estimate the user's emotions using, for example, an emotion engine or generative AI. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The suggestion unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the suggestion unit can analyze the tone and speed of the user's voice and estimate the emotions. Furthermore, the suggestion unit can analyze the user's text input and estimate the emotions. For example, the suggestion unit can analyze the content of the text entered by the user and estimate the emotions. This allows for more appropriate suggestions by adjusting the length of the suggestion based on the user's emotions. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input the user's emotion data into a generative AI and have the generative AI perform the adjustment of the suggestion length based on the emotions.

[0082] The proposal department can determine the priority of proposals based on the submission timing of melodies and chords. For example, the proposal department may use AI to evaluate the submission timing of melodies and chords. For example, the proposal department may prioritize melodies and chords submitted earlier. It may also postpone melodies and chords submitted later. For example, the proposal department may adjust the order of proposals based on the submission timing. This allows for more appropriate proposals by determining the priority of proposals based on the submission timing of melodies and chords. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department may input melody and chord submission timing data into a generating AI and have the generating AI determine the priority of proposals based on the submission timing.

[0083] The suggestion unit can adjust the order of suggestions based on the relationships between melodies and chords during the suggestion process. For example, the suggestion unit can use AI to evaluate the relationships between melodies and chords. For example, the suggestion unit can prioritize suggesting melodies and chords with high relevance. It can also postpone suggesting melodies and chords with low relevance. For example, the suggestion unit can adjust the order of suggestions based on relevance. By adjusting the order of suggestions based on the relationships between melodies and chords, more appropriate suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input relationship data between melodies and chords into a generating AI and have the generating AI perform the adjustment of the suggestion order based on relevance.

[0084] The arrangement unit can estimate the user's emotions and adjust the selection of instruments and arrangement methods based on the estimated emotions. The arrangement unit can estimate the user's emotions using, for example, an emotion engine or generative AI. For example, the arrangement unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The arrangement unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the arrangement unit can analyze the tone and speed of the user's voice and estimate the emotions. Furthermore, the arrangement unit can analyze the user's text input and estimate the emotions. For example, the arrangement unit can analyze the content of the text entered by the user and estimate the emotions. This allows for more appropriate suggestions by adjusting the selection of instruments and arrangement methods based on the user's emotions. Some or all of the above processing in the arrangement unit may be performed using, for example, AI, or not using AI. For example, the arrangement unit can input the user's emotion data into a generative AI and have the generative AI perform the selection of instruments and adjustment of arrangement methods based on emotions.

[0085] The arrangement unit can analyze the user's past arrangement history to select the optimal arrangement method during the arrangement process. For example, the arrangement unit can use AI to analyze the user's past arrangement history. For example, the arrangement unit can prioritize suggesting arrangement methods that the user has preferred to use in the past. The arrangement unit can also suggest arrangement methods suitable for a specific style based on the user's past arrangement history. For example, the arrangement unit can suggest the optimal arrangement method by referring to arrangement methods that the user has successfully used in the past. This allows for more appropriate arrangements by selecting the optimal arrangement method based on the user's past arrangement history. Some or all of the above processing in the arrangement unit may be performed using AI, for example, or without AI. For example, the arrangement unit can input the user's past arrangement data into a generating AI and have the generating AI select the optimal arrangement method.

[0086] The arrangement unit can customize the arrangement methods based on the user's current music project during the arrangement process. For example, the arrangement unit can use AI to analyze the user's current music project. For example, the arrangement unit can suggest an arrangement method suitable for the project the user is currently working on. The arrangement unit can also adjust the arrangement methods based on the progress of the user's project. For example, the arrangement unit can customize the arrangement methods to match the goals of the user's project. This allows for more appropriate arrangements by customizing the arrangement methods based on the user's current music project. Some or all of the above processes in the arrangement unit may be performed using AI, for example, or without AI. For example, the arrangement unit can input the user's project data into a generating AI and have the generating AI perform the customization of the arrangement methods.

[0087] The arrangement unit can estimate the user's emotions and determine the priority of arrangements based on the estimated emotions. The arrangement unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the arrangement unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The arrangement unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the arrangement unit can analyze the tone and speed of the user's voice and estimate the emotions. Furthermore, the arrangement unit can analyze the user's text input and estimate the emotions. For example, the arrangement unit can analyze the content of the text entered by the user and estimate the emotions. This allows for more appropriate arrangements by determining the priority of arrangements based on the user's emotions. Some or all of the above processing in the arrangement unit may be performed using, for example, AI, or not using AI. For example, the arrangement unit can input the user's emotion data into a generative AI and have the generative AI perform the determination of arrangement priorities based on emotions.

[0088] The arrangement unit can select the optimal arrangement method by considering the user's geographical location information during the arrangement process. For example, the arrangement unit can analyze the user's geographical location information using AI. For example, if the user is in a specific region, the arrangement unit can suggest an arrangement method related to that region. Also, if the user is traveling, the arrangement unit can suggest an arrangement method related to the travel destination. For example, if the user is at home, the arrangement unit can suggest an arrangement method related to home. This allows for more appropriate arrangements by selecting the optimal arrangement method based on the user's geographical location information. Some or all of the above processing in the arrangement unit may be performed using AI, for example, or without AI. For example, the arrangement unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal arrangement method.

[0089] The arrangement unit can analyze the user's social media activity and propose arrangement methods during the arrangement process. For example, the arrangement unit can use AI to analyze the user's social media activity. For example, the arrangement unit can propose relevant arrangement methods based on information shared by the user on social media. The arrangement unit can also propose relevant arrangement methods based on accounts followed by the user on social media. For example, the arrangement unit can propose arrangement methods based on topics the user has shown interest in on social media. This allows for more appropriate arrangements by proposing arrangement methods based on the user's social media activity. Some or all of the above processing in the arrangement unit may be performed using AI, for example, or without AI. For example, the arrangement unit can input the user's social media data into a generating AI and have the generating AI propose arrangement methods.

[0090] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, the matching unit estimates the user's emotions using an emotion engine or generative AI. For example, the matching unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The matching unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the matching unit can analyze the tone and speed of the user's voice and estimate the emotions. Furthermore, the matching unit can analyze the user's text input and estimate the emotions. For example, the matching unit can analyze the content of the text entered by the user and estimate the emotions. This allows for more appropriate matching by adjusting the matching criteria based on the user's emotions. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the user's emotion data into a generative AI and have the generative AI perform the adjustment of the matching criteria based on the emotions.

[0091] The matching unit can improve the accuracy of matching by considering the interrelationships between the arranged songs and rhythms during the matching process. For example, the matching unit can use AI to analyze the interrelationships between the arranged songs and rhythms. For example, the matching unit can perform optimal matching by considering the harmony between the arranged songs and rhythms. The matching unit can also perform optimal matching by considering the tempo of the arranged songs and rhythms. For example, the matching unit can perform optimal matching by considering the key of the arranged songs and rhythms. This improves the accuracy of matching by considering the interrelationships between the arranged songs and rhythms. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input data on the arranged songs and rhythms into a generating AI and have the generating AI perform accuracy improvements for matching that consider the interrelationships.

[0092] The matching unit can perform matching while considering the user's music history and preferences. For example, the matching unit can use AI to analyze the user's music history and preferences. For example, the matching unit can perform optimal matching based on songs the user has enjoyed listening to in the past. The matching unit can also perform matching suitable for specific genres or styles based on the user's music history. For example, the matching unit can perform optimal matching based on the user's preferences. This makes it possible to perform more appropriate matching by performing matching based on the user's music history and preferences. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the user's music history and preference data into a generating AI and have the generating AI perform optimal matching.

[0093] The matching unit can estimate the user's emotions and adjust the order in which matching results are displayed based on the estimated emotions. The matching unit estimates the user's emotions using, for example, an emotion engine or a generative AI. For example, the matching unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The matching unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the matching unit can analyze the tone and speed of the user's voice and estimate the emotions. Furthermore, the matching unit can analyze the user's text input and estimate the emotions. For example, the matching unit can analyze the content of the text entered by the user and estimate the emotions. By doing so, more appropriate matching results can be obtained by adjusting the order in which matching results are displayed based on the user's emotions. Some or all of the above processing in the matching unit may be performed using, for example, AI, or not using AI. For example, the matching unit can input the user's emotion data into a generative AI and have the generative AI perform the adjustment of the display order of matching results based on emotions.

[0094] The matching unit can perform matching while considering the geographical distribution of the arranged songs and rhythms. For example, the matching unit can use AI to analyze the geographical distribution of the arranged songs and rhythms. For example, the matching unit can prioritize matching songs and rhythms that are popular in a particular region. The matching unit can also prioritize matching songs and rhythms related to the region where the user is currently located. For example, the matching unit performs optimal matching based on geographical distribution. This makes it possible to perform more appropriate matching by considering the geographical distribution of the arranged songs and rhythms. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input geographical distribution data of the arranged songs and rhythms into a generating AI and have the generating AI perform matching that takes geographical distribution into account.

[0095] The matching unit can improve the accuracy of matching by referring to relevant literature on the arranged song or rhythm during the matching process. For example, the matching unit can use AI to analyze relevant literature on the arranged song or rhythm. For example, the matching unit can refer to academic papers related to the arranged song or rhythm to perform the optimal matching. The matching unit can also refer to music theory books related to the arranged song or rhythm to perform the optimal matching. For example, the matching unit can refer to review articles related to the arranged song or rhythm to perform the optimal matching. This improves the accuracy of matching by referring to relevant literature on the arranged song or rhythm. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input data on relevant literature on the arranged song or rhythm into a generating AI and have the generating AI perform the accuracy improvement of matching by referring to relevant literature.

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

[0097] The music composition support AI assistant can also be equipped with a feedback unit. The feedback unit can collect feedback from the user and use it to improve the algorithms of the suggestion and arrangement units. For example, if the user inputs a rating of "good" or "bad" for a suggested melody or chord, the feedback unit will record that rating and provide feedback to the suggestion unit. The feedback unit can also receive specific comments from the user on the suggested arrangement. For example, it can collect comments such as "This rhythm is too fast" or "I don't like the sound of this instrument" and provide them to the arrangement unit. This allows the feedback unit to make suggestions and arrangements that reflect the user's preferences and opinions. Furthermore, the feedback unit can analyze the user's feedback and automatically adjust the algorithms of the suggestion and arrangement units. For example, the feedback unit can improve the melody generation algorithm of the suggestion unit based on the user's evaluation data.

[0098] The suggestion unit can estimate the user's emotions and adjust the tension of the melody and chords based on those estimates. For example, if the suggestion unit estimates that the user is relaxed, it can suggest melodies and chords with a calm tension. Conversely, if it estimates that the user is excited, it can suggest melodies and chords with an energetic tension. Furthermore, the suggestion unit can adjust the tension of the melody and chords in real time in response to changes in the user's emotions. For example, if the user changes from relaxed to excited, the suggestion unit can change the tension of the melody and chords accordingly. This allows the suggestion unit to suggest more appropriate melodies and chords based on the user's emotions.

[0099] The arrangement unit can estimate the user's emotions and adjust the instrument volume balance based on those emotions. For example, if the arrangement unit estimates the user is relaxed, it can suggest a gentle volume balance. Conversely, if it estimates the user is excited, it can suggest a dynamic volume balance. Furthermore, the arrangement unit can adjust the instrument volume balance in real time in response to changes in the user's emotions. For example, if the user changes from relaxed to excited, the arrangement unit can change the instrument volume balance accordingly. This allows the arrangement unit to suggest a more appropriate instrument volume balance based on the user's emotions.

[0100] The matching unit can estimate the user's emotions and filter the matching results based on those estimates. For example, if the matching unit estimates the user is relaxed, it can prioritize suggesting calm songs and rhythms. Conversely, if it estimates the user is excited, it can prioritize suggesting energetic songs and rhythms. Furthermore, the matching unit can filter the matching results in real time in response to changes in the user's emotions. For example, if the user changes from relaxed to excited, the matching unit can change the suggested songs and rhythms accordingly. This allows the matching unit to suggest more appropriate matching results based on the user's emotions.

[0101] The reception unit can estimate the user's emotions and adjust the input reception method based on the estimated emotions. For example, if the reception unit estimates that the user is relaxed, it can prioritize calm voice input. If it estimates that the user is excited, it can prioritize rapid text input. Furthermore, the reception unit can adjust the input reception method in real time in response to changes in the user's emotions. For example, if the user changes from relaxed to excited, the reception unit can change the input reception method accordingly. This allows the reception unit to receive more appropriate input based on the user's emotions.

[0102] The suggestion unit can analyze the user's past music selection history and suggest melodies and chords that match their preferences. For example, the suggestion unit can generate similar melodies and chords based on data of songs the user has enjoyed listening to in the past. It can also suggest new melodies and chords that match the user's preferences based on data of melodies and chords the user has created in the past. Furthermore, the suggestion unit can analyze the user's past music selection history and suggest melodies and chords suitable for specific genres or styles. This allows the suggestion unit to suggest more appropriate melodies and chords based on the user's preferences.

[0103] The styling function can analyze the user's past styling history and select the optimal styling method. For example, the styling function will prioritize suggesting styling methods that the user has preferred in the past. It can also suggest styling methods suitable for specific styles based on the user's past styling history. Furthermore, the styling function can suggest the optimal styling method by referencing successful styling methods used by the user in the past. This allows the styling function to suggest more appropriate styling based on the user's past styling history.

[0104] The matching unit can perform matching based on the user's music history and preferences. For example, the matching unit can perform optimal matching based on songs the user has enjoyed listening to in the past. It can also perform matching based on the user's music history to find songs suitable for specific genres or styles. Furthermore, the matching unit can perform optimal matching based on the user's preferences. This allows the matching unit to suggest more appropriate matches based on the user's music history and preferences.

[0105] The reception desk can prioritize receiving input that is highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving input related to that region. Furthermore, if the user is traveling, the reception desk can prioritize receiving input related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize receiving input related to their home. This allows the reception desk to receive more appropriate input based on the user's geographical location.

[0106] The suggestion function can adjust the level of detail in its suggestions based on the importance of the melody and chords. For example, it will provide detailed suggestions for important melodies and chords, and concise suggestions for less important ones. Furthermore, it can provide detailed suggestions for melodies and chords that the user particularly values. This allows the suggestion function to provide more appropriate suggestions based on the importance of the melodies and chords.

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

[0108] Step 1: The reception desk receives input from the user. User input includes preferences for melodies and chords, as well as specifications for music genre and style. The reception desk can receive user preferences via text input or voice input. Step 2: The suggestion unit proposes a melody or chord based on the input received by the reception unit. The suggestion unit can also use AI to generate a melody and propose a chord progression based on the input request. For example, if the user requests a "cheerful melody," the suggestion unit will generate a cheerful melody and propose it to the user. Step 3: The arrangement unit proposes instrument selections or arrangements based on the melody or chords suggested by the proposal unit. The arrangement unit can also use AI to select instruments that suit the proposed melody and suggest changes to the rhythm and tempo. For example, it might suggest guitar or piano as instruments that suit a cheerful melody and suggest changes to the rhythm and tempo. Step 4: The matching unit matches the arranged song or rhythm with existing songs or rhythms that are similar to the instrument selection or arrangement proposed by the arrangement unit. The matching unit uses AI to search for existing songs similar to the arranged song and suggests them to the user.

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

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

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

[0112] For example, the reception unit is implemented by the reception device 38 of the smart device 14, and accepts user input using a touch panel 38A and a microphone 38B. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12, and generates melodies and chords using AI. The arrangement unit is implemented by the control unit 46A of the smart device 14, and suggests instrument selections and rhythm changes that match the proposed melody. The matching unit is implemented by the specific processing unit 290 of the data processing device 12, and searches for existing songs similar to the arranged song and suggests them to the user. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0128] For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and accepts the user's requests using voice input. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12 and generates melodies and chords using AI. The arrangement unit is implemented by the control unit 46A of the smart glasses 214 and suggests instrument selections and rhythm changes that match the suggested melody. The matching unit is implemented by the specific processing unit 290 of the data processing device 12 and searches for existing songs similar to the arranged song and suggests them to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0144] For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and accepts the user's requests using voice input. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12 and generates melodies and chords using AI. The arrangement unit is implemented by the control unit 46A of the headset terminal 314 and suggests instrument selections and rhythm changes that match the proposed melody. The matching unit is implemented by the specific processing unit 290 of the data processing device 12 and searches for existing songs similar to the arranged song and suggests them to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0161] For example, the reception unit is implemented by the microphone 238 of the robot 414, and accepts the user's requests using voice input. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12, and generates melodies and chords using AI. The arrangement unit is implemented by the control unit 46A of the robot 414, and suggests instrument selections and rhythm changes that match the proposed melody. The matching unit is implemented by the specific processing unit 290 of the data processing device 12, and searches for existing songs similar to the arranged song and suggests them to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0180] (Note 1) A reception desk that receives user input, A suggestion unit proposes a melody or chord based on the input received by the aforementioned reception unit, An arrangement unit proposes instrument selection or arrangement based on the melody or chords proposed by the proposal unit, The system includes a matching unit that, based on the instrument selection or arrangement proposed by the arrangement unit, matches the arranged song or rhythm with existing songs or rhythms that are similar. A system characterized by the following features. (Note 2) The aforementioned proposal section is, It suggests melodies or chords based on information about the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned arrangement section is, We will suggest instrument selections or arrangements based on the genre or style of music. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, It estimates the user's emotions and suggests a melody or chord based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned arrangement section is, It estimates the user's emotions and suggests instrument selections or arrangements based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The matching unit is It estimates the user's emotions and matches them with existing songs or rhythms based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past input history and select the appropriate reception method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving input, filtering is performed based on the user's current projects or areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input to be received based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving input, the system prioritizes accepting input that is highly relevant based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving input, the system analyzes the user's social media activity and accepts relevant input. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way the melody or chord is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the melody or chords. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the music genre or style. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the length of the suggestion based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When submitting a proposal, we will prioritize it based on when the melody or chords were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relationship between melodies or chords. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned arrangement section is, It estimates the user's emotions and adjusts the selection or arrangement of instruments based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned arrangement section is, During the arrangement process, the system analyzes the user's past arrangement history to select the most appropriate arrangement method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned arrangement section is, During the arrangement process, the arrangement method is customized based on the user's current music project. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned arrangement section is, The system estimates the user's emotions and determines the priority of arrangements based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned arrangement section is, During the arrangement process, the appropriate arrangement method is selected based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned arrangement section is, During the arrangement process, we analyze the user's social media activity and suggest arrangement methods. The system described in Appendix 1, characterized by the features described herein. (Note 25) The matching unit is The system estimates the user's emotions and adjusts the matching criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The matching unit is During the matching process, the accuracy of the matching is improved based on the interrelationships between the arranged songs or rhythms. The system described in Appendix 1, characterized by the features described herein. (Note 27) The matching unit is Matching is done based on the user's music history or preferences. The system described in Appendix 1, characterized by the features described herein. (Note 28) The matching unit is The system estimates the user's emotions and adjusts the order in which matching results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The matching unit is During the matching process, matching is performed based on the geographical distribution of the arranged songs or rhythms. The system described in Appendix 1, characterized by the features described herein. (Note 30) The matching unit is During the matching process, the accuracy of the matching is improved based on relevant literature for the arranged song or rhythm. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. A reception desk that receives user input, A suggestion unit proposes a melody or chord based on the input received by the aforementioned reception unit, An arrangement unit proposes instrument selection or arrangement based on the melody or chords proposed by the proposal unit, The system includes a matching unit that, based on the instrument selection or arrangement proposed by the arrangement unit, matches the arranged song or rhythm with existing songs or rhythms that are similar. A system characterized by the following features.

2. The aforementioned proposal section is, It suggests melodies or chords based on information about the user's preferences. The system according to feature 1.

3. The aforementioned arrangement section is, We will suggest instrument selections or arrangements based on the genre or style of music. The system according to feature 1.

4. The aforementioned proposal section is, It estimates the user's emotions and suggests a melody or chord based on those estimated emotions. The system according to feature 1.

5. The aforementioned arrangement section is, It estimates the user's emotions and suggests instrument selections or arrangements based on those estimated emotions. The system according to feature 1.

6. The matching unit is It estimates the user's emotions and matches them with existing songs or rhythms based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past input history and select the appropriate reception method. The system according to feature 1.

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