system

Abstract

The system according to this embodiment aims to enable artists to efficiently generate ideas for song structure, harmony, and lyrics. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a generation unit, and a provision unit. The collection unit collects information from artists regarding the composition and harmony of songs. The analysis unit analyzes the information collected by the collection unit. The proposal unit makes style suggestions based on the latest music trends based on the analysis results obtained by the analysis unit. The generation unit generates lyric ideas based on the content proposed by the proposal unit. The provision unit provides the ideas generated by the generation unit to the artists.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for an artist to efficiently generate ideas for the composition, harmony, and lyrics of a song, and there is a lack of means to prevent the exhaustion of inspiration.

[0005] The system according to the embodiment aims to enable an artist to efficiently generate ideas for the composition, harmony, and lyrics of a song.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a generation unit, and a provision unit. The collection unit collects information from artists regarding the structure and harmony of songs. The analysis unit analyzes the information collected by the collection unit. The proposal unit makes style suggestions based on the latest music trends based on the analysis results obtained by the analysis unit. The generation unit generates lyric ideas based on the content proposed by the proposal unit. The provision unit provides the ideas generated by the generation unit to the artist. [Effects of the Invention]

[0007] The system according to this embodiment allows artists to efficiently generate ideas for song structure, harmony, and lyrics. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The music creation support system according to an embodiment of the present invention is a system that combines knowledge of music theory with the latest trends to enable artists to efficiently generate song structure proposals, harmonies, and lyric ideas. This music creation support system allows artists to input information regarding song structure and harmony, analyzes the input information using an automatic music theory analysis function, and provides optimal suggestions based on music theory. Furthermore, it offers style suggestions based on the latest music trends and generates lyric ideas using natural language processing. For example, an artist inputs information regarding song structure and harmony. Next, the system analyzes the input information using an automatic music theory analysis function and provides optimal suggestions based on music theory. Furthermore, it offers style suggestions based on the latest music trends and generates lyric ideas using natural language processing. This streamlines the artist's creative process and prevents the depletion of inspiration. For example, it can be expected to shorten the creation time, improve the quality of songs, and increase the artist's productivity. Thus, the music creation support system can streamline the artist's songwriting process and prevent the depletion of inspiration.

[0029] The music creation support system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a generation unit, and a provision unit. The collection unit collects information from artists regarding the structure and harmony of songs. For example, the collection unit collects information regarding the structure and harmony of songs entered by artists. The collection unit can also collect information such as melodies, chord progressions, and rhythm patterns entered by artists. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected information using an automatic music theory analysis function. Based on music theory, the analysis unit can analyze the collected information and make proposals based on optimal music theory. The proposal unit makes style proposals based on the latest music trends based on the analysis results obtained by the analysis unit. For example, the proposal unit makes style proposals to artists based on the latest music trends. The proposal unit can make style proposals based on the latest music trends such as genres, popular artists, and trendy sounds. The generation unit generates lyric ideas based on the content proposed by the proposal unit. For example, the generation unit generates lyric ideas based on the proposed content using natural language processing. The generation unit can generate lyric ideas such as themes, phrases, and word choices. The supply unit provides the ideas generated by the generation unit to the artist. The supply unit provides the generated ideas to the artist, for example. By providing the generated ideas to the artist, the supply unit can support the artist's creative process. In this way, the music creation support system according to the embodiment can streamline the artist's songwriting process and prevent the depletion of inspiration.

[0030] The data collection unit gathers information from artists regarding the structure and harmony of their songs. Specifically, it collects information such as melodies, chord progressions, and rhythm patterns entered by the artist. The data collection unit can acquire this information through the digital audio workstation (DAW) or dedicated input interface used by the artist. For example, it can collect melody lines and chord progressions created by the artist on the DAW in real time and store them in a database. The data collection unit can also collect sheet music data and MIDI data manually entered by the artist. This allows the data collection unit to comprehensively collect various musical elements in the artist's creative process and provide them as input data to the analysis unit. Furthermore, the data collection unit can also collect other songs that the artist is referencing and sound source data that serve as inspiration. This allows the data collection unit to gain a deeper understanding of the artist's creative intentions and style, and to provide foundational data for the analysis and proposal units to make more appropriate suggestions.

[0031] The analysis unit analyzes the information collected by the collection unit. Specifically, it analyzes the collected information using an automated music theory analysis function. For example, it analyzes the collected melodies and chord progressions to reveal the structure of harmony and rhythm based on music theory. The analysis unit utilizes AI to analyze the collected data based on music theory and optimize the composition and harmony of the song. For example, the AI ​​analyzes the pitch and rhythm patterns of the melody line to determine the tonality and tempo of the song. It also reveals the harmonic structure of the song through the analysis of chord progressions and suggests appropriate chord changes and harmonies. Furthermore, based on the collected information, the analysis unit can identify the genre and style of the song and extract musical characteristics that align with the artist's intentions. As a result, the analysis unit can analyze the collected information in detail and provide foundational data to support the artist's creative process.

[0032] The Proposal Department provides style suggestions based on the latest music trends, using the analysis results obtained by the Analysis Department. Specifically, it provides style suggestions to artists based on the latest music trends such as genre, popular artists, and trending sounds. The Proposal Department utilizes AI to match the analysis results with the latest music trends and provide optimal style suggestions to artists. For example, the AI ​​analyzes popular genres and artists' songs in the current music market and suggests styles and sounds suitable for the artist's songs. The Proposal Department can also consider the artist's past songs and creative style to make suggestions that leverage the artist's individuality. In this way, the Proposal Department can provide artists with style suggestions based on the latest music trends and support their creative process. Furthermore, the Proposal Department can collect artist feedback and continuously improve the accuracy and effectiveness of its suggestions. In this way, the Proposal Department can streamline the artist's creative process and prevent a depletion of inspiration.

[0033] The generation unit generates lyric ideas based on the content proposed by the suggestion unit. Specifically, it utilizes natural language processing to generate lyric ideas based on the proposed content. The generation unit uses AI to generate lyric ideas such as themes, phrases, and word choices. For example, the AI ​​generates relevant phrases and words based on the proposed theme and provides them to the artist. The generation unit can also consider the artist's past lyrics and style to generate lyric ideas that reflect the artist's individuality. In this way, the generation unit can support the artist's creative process and prevent a depletion of inspiration. Furthermore, the generation unit can collect feedback from the artist and continuously improve the accuracy and effectiveness of the generated lyric ideas. In this way, the generation unit can streamline the artist's creative process and prevent a depletion of inspiration.

[0034] The provider unit provides the ideas generated by the generator unit to the artist. Specifically, it provides the generated ideas to the artist. By providing the generated ideas to the artist, the provider unit can support the artist's creative process. For example, the provider unit provides an interface for providing the generated ideas to the artist. By providing an interface for providing the generated ideas to the artist, the provider unit can support the artist's creative process. In this way, the provider unit can streamline the artist's creative process and prevent the depletion of inspiration.

[0035] The data collection unit can analyze an artist's past music production history and select the optimal data collection method. For example, the data collection unit can analyze the production history of songs that an artist has previously succeeded with and propose a similar data collection method. The data collection unit can also analyze the production history of songs that an artist has previously failed with and propose a different data collection method. The data collection unit can identify specific patterns from an artist's past production history and select a data collection method based on those patterns. In this way, the optimal data collection method can be selected by analyzing the past production history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the artist's past production history data into a generating AI and have the generating AI select the optimal data collection method.

[0036] The data collection unit can filter information about the composition and harmony of songs based on the artist's current projects and areas of interest. For example, the data collection unit can prioritize collecting information related to the artist's current projects. The data collection unit can filter and collect highly relevant information based on the artist's areas of interest. The data collection unit can collect necessary information at the appropriate time, depending on the progress of the artist's current projects. This allows for the collection of highly relevant information by filtering information based on the current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the artist's current projects and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0037] The data collection unit can prioritize the collection of highly relevant information by considering the artist's geographical location when collecting information on the composition and harmony of songs. For example, if the artist is in a specific region, the data collection unit can prioritize the collection of music information related to that region. If the artist is traveling, the data collection unit can prioritize the collection of information on music trends in the destination. If the artist is participating in a specific event, the data collection unit can prioritize the collection of music information related to that event. In this way, by considering geographical location, highly relevant information can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the artist's geographical location information into a generating AI and have the generating AI perform the information collection.

[0038] The data collection unit can collect relevant information by analyzing the artist's social media activity when collecting information on the composition and harmony of a song. For example, the data collection unit can collect relevant music information based on information shared by the artist on social media. The data collection unit can collect information of high interest by analyzing the reactions of the artist's followers. The data collection unit can prioritize the collection of information related to topics mentioned by the artist on social media. This allows relevant information to be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the artist's social media activity data into a generating AI and have the generating AI perform the information collection.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the song during the analysis. For example, the analysis unit performs a detailed analysis for important songs. For general songs, the analysis unit can perform a standard analysis. For songs in the prototype stage, the analysis unit can perform a simplified analysis. By adjusting the level of detail of the analysis based on the importance of the song, a more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input song importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the music during analysis. For example, in the case of pop music, the analysis unit can apply an analysis algorithm specialized for pop music. In the case of classical music, the analysis unit can apply an analysis algorithm specialized for classical music. In the case of jazz music, the analysis unit can apply an analysis algorithm specialized for jazz. By applying different analysis algorithms depending on the category of the music, more appropriate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input music category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0041] The analysis unit can determine the priority of analysis based on the production date of the songs during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent songs. The analysis unit can postpone the analysis of older songs. The analysis unit can prioritize the analysis of songs currently in production. By determining the priority of analysis based on the production date of the songs, more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input song production date data into a generating AI and have the generating AI determine the priority of analysis.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the songs during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant songs. The analysis unit can postpone the analysis of less relevant songs. The analysis unit can group relevant songs together for analysis. This allows for a more appropriate analysis by adjusting the order of analysis based on the relevance of the songs. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input song relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0043] The proposal unit can adjust the level of detail of its proposals based on the importance of the song. For example, the proposal unit will provide detailed proposals for important songs. For general songs, it can provide standard proposals. For songs in the prototype stage, it can provide simplified proposals. By adjusting the level of detail of the proposals based on the importance of the song, more appropriate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input song importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0044] The suggestion unit can apply different suggestion algorithms depending on the category of the music when making suggestions. For example, in the case of pop music, the suggestion unit can apply a suggestion algorithm specialized for pop music. In the case of classical music, the suggestion unit can apply a suggestion algorithm specialized for classical music. In the case of jazz music, the suggestion unit can apply a suggestion algorithm specialized for jazz. By applying different suggestion algorithms depending on the category of music, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input music category data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0045] The proposal unit can determine the priority of proposals based on the production date of the songs when making a proposal. For example, the proposal unit can prioritize the most recent songs. The proposal unit can postpone older songs. The proposal unit can also prioritize songs currently in production. By determining the priority of proposals based on the production date of the songs, more appropriate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input song production date data into a generating AI and have the generating AI perform the determination of proposal priorities.

[0046] The suggestion unit can adjust the order of suggestions based on the relevance of the songs during the suggestion process. For example, the suggestion unit can prioritize suggesting songs with high relevance. The suggestion unit can postpone suggesting songs with low relevance. The suggestion unit can group related songs together and suggest them. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of the songs. 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 song relevance data into a generating AI and have the generating AI adjust the order of suggestions.

[0047] The generation unit can adjust the level of detail of the generated lyrics based on the importance of the song during the generation process. For example, the generation unit can generate detailed lyrics for important songs. For general songs, the generation unit can generate standard lyrics. For songs in the prototype stage, the generation unit can generate simplified lyrics. By adjusting the level of detail based on the importance of the song, more appropriate lyrics can be generated. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input song importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the generated lyrics.

[0048] The generation unit can apply different generation algorithms depending on the category of the song during generation. For example, in the case of pop music, the generation unit can apply a generation algorithm specialized for pop music. In the case of classical music, the generation unit can apply a generation algorithm specialized for classical music. In the case of jazz music, the generation unit can apply a generation algorithm specialized for jazz. By applying different generation algorithms depending on the category of the song, more appropriate lyrics can be generated. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input song category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0049] The generation unit can determine the generation priority based on the song's production date during the generation process. For example, the generation unit can prioritize generating the most recent song. The generation unit can postpone generating older songs. The generation unit can prioritize generating songs currently in production. By determining the generation priority based on the song's production date, more appropriate lyrics are generated. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input song production date data into a generation AI and have the generation AI determine the generation priority.

[0050] The generation unit can adjust the generation order based on the relevance of the songs during generation. For example, the generation unit can prioritize generating songs with high relevance. The generation unit can postpone generating songs with low relevance. The generation unit can group related songs together for generation. By adjusting the generation order based on the relevance of the songs, more appropriate lyrics can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input song relevance data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0051] The service provider can adjust the level of detail provided based on the importance of the song at the time of provision. For example, the service provider can provide detailed ideas for important songs. For general songs, it can provide standard ideas. For songs in the prototype stage, it can provide simple ideas. By adjusting the level of detail based on the importance of the song, more appropriate ideas can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input song importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the provision.

[0052] The providing unit can apply different providing algorithms depending on the category of the music at the time of provision. For example, in the case of pop music, the providing unit can apply a providing algorithm specialized for pop music. In the case of classical music, the providing unit can apply a providing algorithm specialized for classical music. In the case of jazz music, the providing unit can apply a providing algorithm specialized for jazz. By applying different providing algorithms depending on the category of the music, more appropriate ideas can be provided. Some or all of the above processing in the providing unit may be performed using AI, for example, or without using AI. For example, the providing unit can input music category data into a generating AI and have the generating AI execute the application of the providing algorithm.

[0053] The distribution unit can determine the priority of distribution based on the production date of the songs at the time of distribution. For example, the distribution unit may prioritize the provision of the latest songs. The distribution unit may postpone the provision of older songs. The distribution unit may prioritize the provision of songs that are currently being produced. By determining the priority of distribution based on the production date of the songs, more appropriate ideas can be provided. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit may input song production date data into a generating AI and have the generating AI perform the determination of the distribution priority.

[0054] The distribution unit can adjust the order of distribution based on the relevance of the songs. For example, the distribution unit can prioritize providing songs with high relevance. The distribution unit can postpone providing songs with low relevance. The distribution unit can group related songs together and provide them. By adjusting the order of distribution based on the relevance of the songs, more appropriate ideas can be provided. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input song relevance data into a generating AI and have the generating AI perform the adjustment of the distribution order.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit acquires the artist's biometric data and can adjust the collection timing based on the artist's health condition. For example, if the artist's heart rate and blood pressure are stable, the collection timing can be flexibly set to collect information at the artist's pace. If the artist's health condition is unstable, the collection timing can be shortened to quickly collect the necessary information. This allows for more appropriate information collection by adjusting the collection timing according to the artist's health condition.

[0057] The analysis unit can analyze an artist's past music production history and select the optimal analysis method. For example, it can analyze the production history of successful songs an artist has produced in the past and suggest a similar analysis method. It can also analyze the production history of unsuccessful songs an artist has produced in the past and suggest a different analysis method. In this way, the optimal analysis method can be selected by analyzing past production history.

[0058] The proposal department can analyze an artist's past music production history and select the most suitable proposal method. For example, it can analyze the production history of successful songs an artist has produced in the past and propose a similar proposal method. Conversely, it can analyze the production history of unsuccessful songs an artist has produced in the past and propose a different proposal method. In this way, by analyzing past production history, the most suitable proposal method can be selected.

[0059] The generation unit can analyze an artist's past music production history and select the optimal generation method. For example, it can analyze the production history of successful songs an artist has produced in the past and suggest a similar generation method. It can also analyze the production history of unsuccessful songs an artist has produced in the past and suggest a different generation method. In this way, the optimal generation method can be selected by analyzing past production history.

[0060] The distribution department can analyze an artist's past music production history and select the optimal distribution method. For example, it can analyze the production history of successful songs an artist has produced in the past and propose a similar distribution method. It can also analyze the production history of unsuccessful songs an artist has produced in the past and propose a different distribution method. In this way, the optimal distribution method can be selected by analyzing past production history.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit gathers information from the artist regarding the song's structure and harmony. For example, it can collect information such as melodies, chord progressions, and rhythm patterns entered by the artist. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it can use an automated music theory analysis function to analyze the collected information and make suggestions based on the most suitable music theory. Step 3: The proposal unit makes style suggestions based on the latest music trends, using the analysis results obtained by the analysis unit. For example, it can make style suggestions to artists based on the latest music trends such as genre, popular artists, and trendy sounds. Step 4: The generation unit generates lyric ideas based on the content proposed by the proposal unit. For example, natural language processing can be used to generate lyric ideas such as themes, phrases, and word choices. Step 5: The providing unit provides the artist with the ideas generated by the generating unit. For example, by providing the generated ideas to the artist, the artist's creative process can be supported.

[0063] (Example of form 2) The music creation support system according to an embodiment of the present invention is a system that combines knowledge of music theory with the latest trends to enable artists to efficiently generate song structure proposals, harmonies, and lyric ideas. This music creation support system allows artists to input information regarding song structure and harmony, analyzes the input information using an automatic music theory analysis function, and provides optimal suggestions based on music theory. Furthermore, it offers style suggestions based on the latest music trends and generates lyric ideas using natural language processing. For example, an artist inputs information regarding song structure and harmony. Next, the system analyzes the input information using an automatic music theory analysis function and provides optimal suggestions based on music theory. Furthermore, it offers style suggestions based on the latest music trends and generates lyric ideas using natural language processing. This streamlines the artist's creative process and prevents the depletion of inspiration. For example, it can be expected to shorten the creation time, improve the quality of songs, and increase the artist's productivity. Thus, the music creation support system can streamline the artist's songwriting process and prevent the depletion of inspiration.

[0064] The music creation support system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a generation unit, and a provision unit. The collection unit collects information from artists regarding the structure and harmony of songs. For example, the collection unit collects information regarding the structure and harmony of songs entered by artists. The collection unit can also collect information such as melodies, chord progressions, and rhythm patterns entered by artists. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected information using an automatic music theory analysis function. Based on music theory, the analysis unit can analyze the collected information and make proposals based on optimal music theory. The proposal unit makes style proposals based on the latest music trends based on the analysis results obtained by the analysis unit. For example, the proposal unit makes style proposals to artists based on the latest music trends. The proposal unit can make style proposals based on the latest music trends such as genres, popular artists, and trendy sounds. The generation unit generates lyric ideas based on the content proposed by the proposal unit. For example, the generation unit generates lyric ideas based on the proposed content using natural language processing. The generation unit can generate lyric ideas such as themes, phrases, and word choices. The supply unit provides the ideas generated by the generation unit to the artist. The supply unit provides the generated ideas to the artist, for example. By providing the generated ideas to the artist, the supply unit can support the artist's creative process. In this way, the music creation support system according to the embodiment can streamline the artist's songwriting process and prevent the depletion of inspiration.

[0065] The data collection unit gathers information from artists regarding the structure and harmony of their songs. Specifically, it collects information such as melodies, chord progressions, and rhythm patterns entered by the artist. The data collection unit can acquire this information through the digital audio workstation (DAW) or dedicated input interface used by the artist. For example, it can collect melody lines and chord progressions created by the artist on the DAW in real time and store them in a database. The data collection unit can also collect sheet music data and MIDI data manually entered by the artist. This allows the data collection unit to comprehensively collect various musical elements in the artist's creative process and provide them as input data to the analysis unit. Furthermore, the data collection unit can also collect other songs that the artist is referencing and sound source data that serve as inspiration. This allows the data collection unit to gain a deeper understanding of the artist's creative intentions and style, and to provide foundational data for the analysis and proposal units to make more appropriate suggestions.

[0066] The analysis unit analyzes the information collected by the collection unit. Specifically, it analyzes the collected information using an automated music theory analysis function. For example, it analyzes the collected melodies and chord progressions to reveal the structure of harmony and rhythm based on music theory. The analysis unit utilizes AI to analyze the collected data based on music theory and optimize the composition and harmony of the song. For example, the AI ​​analyzes the pitch and rhythm patterns of the melody line to determine the tonality and tempo of the song. It also reveals the harmonic structure of the song through the analysis of chord progressions and suggests appropriate chord changes and harmonies. Furthermore, based on the collected information, the analysis unit can identify the genre and style of the song and extract musical characteristics that align with the artist's intentions. As a result, the analysis unit can analyze the collected information in detail and provide foundational data to support the artist's creative process.

[0067] The Proposal Department provides style suggestions based on the latest music trends, using the analysis results obtained by the Analysis Department. Specifically, it provides style suggestions to artists based on the latest music trends such as genre, popular artists, and trending sounds. The Proposal Department utilizes AI to match the analysis results with the latest music trends and provide optimal style suggestions to artists. For example, the AI ​​analyzes popular genres and artists' songs in the current music market and suggests styles and sounds suitable for the artist's songs. The Proposal Department can also consider the artist's past songs and creative style to make suggestions that leverage the artist's individuality. In this way, the Proposal Department can provide artists with style suggestions based on the latest music trends and support their creative process. Furthermore, the Proposal Department can collect artist feedback and continuously improve the accuracy and effectiveness of its suggestions. In this way, the Proposal Department can streamline the artist's creative process and prevent a depletion of inspiration.

[0068] The generation unit generates lyric ideas based on the content proposed by the suggestion unit. Specifically, it utilizes natural language processing to generate lyric ideas based on the proposed content. The generation unit uses AI to generate lyric ideas such as themes, phrases, and word choices. For example, the AI ​​generates relevant phrases and words based on the proposed theme and provides them to the artist. The generation unit can also consider the artist's past lyrics and style to generate lyric ideas that reflect the artist's individuality. In this way, the generation unit can support the artist's creative process and prevent a depletion of inspiration. Furthermore, the generation unit can collect feedback from the artist and continuously improve the accuracy and effectiveness of the generated lyric ideas. In this way, the generation unit can streamline the artist's creative process and prevent a depletion of inspiration.

[0069] The provider unit provides the ideas generated by the generator unit to the artist. Specifically, it provides the generated ideas to the artist. By providing the generated ideas to the artist, the provider unit can support the artist's creative process. For example, the provider unit provides an interface for providing the generated ideas to the artist. By providing an interface for providing the generated ideas to the artist, the provider unit can support the artist's creative process. In this way, the provider unit can streamline the artist's creative process and prevent the depletion of inspiration.

[0070] The data collection unit can estimate the artist's emotions and adjust the timing of collecting information about the song's structure and harmony based on the estimated emotions. For example, if the artist is relaxed, the data collection unit can flexibly set the collection timing and collect information at the artist's pace. If the artist is stressed, the data collection unit can shorten the collection timing and quickly collect the necessary information. If the artist is focused, the data collection unit can extend the collection timing and collect more in-depth information. By adjusting the collection timing according to the artist's emotions, more appropriate information can be collected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the artist's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0071] The data collection unit can analyze an artist's past music production history and select the optimal data collection method. For example, the data collection unit can analyze the production history of songs that an artist has previously succeeded with and propose a similar data collection method. The data collection unit can also analyze the production history of songs that an artist has previously failed with and propose a different data collection method. The data collection unit can identify specific patterns from an artist's past production history and select a data collection method based on those patterns. In this way, the optimal data collection method can be selected by analyzing the past production history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the artist's past production history data into a generating AI and have the generating AI select the optimal data collection method.

[0072] The data collection unit can filter information about the composition and harmony of songs based on the artist's current projects and areas of interest. For example, the data collection unit can prioritize collecting information related to the artist's current projects. The data collection unit can filter and collect highly relevant information based on the artist's areas of interest. The data collection unit can collect necessary information at the appropriate time, depending on the progress of the artist's current projects. This allows for the collection of highly relevant information by filtering information based on the current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the artist's current projects and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0073] The data collection unit can estimate the artist's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the artist is relaxed, the data collection unit can prioritize collecting detailed information. If the artist is stressed, the data collection unit can prioritize collecting important information. If the artist is focused, the data collection unit can prioritize collecting in-depth information. This allows for more appropriate information collection by prioritizing information according to the artist's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the artist's emotion data into a generative AI and have the generative AI determine the priority of information.

[0074] The data collection unit can prioritize the collection of highly relevant information by considering the artist's geographical location when collecting information on the composition and harmony of songs. For example, if the artist is in a specific region, the data collection unit can prioritize the collection of music information related to that region. If the artist is traveling, the data collection unit can prioritize the collection of information on music trends in the destination. If the artist is participating in a specific event, the data collection unit can prioritize the collection of music information related to that event. In this way, by considering geographical location, highly relevant information can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the artist's geographical location information into a generating AI and have the generating AI perform the information collection.

[0075] The data collection unit can collect relevant information by analyzing the artist's social media activity when collecting information on the composition and harmony of a song. For example, the data collection unit can collect relevant music information based on information shared by the artist on social media. The data collection unit can collect information of high interest by analyzing the reactions of the artist's followers. The data collection unit can prioritize the collection of information related to topics mentioned by the artist on social media. This allows relevant information to be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the artist's social media activity data into a generating AI and have the generating AI perform the information collection.

[0076] The analysis unit can estimate the artist's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the artist is relaxed, the analysis unit can provide detailed analysis results. If the artist is stressed, the analysis unit can provide concise analysis results. If the artist is focused, the analysis unit can provide in-depth analysis results. By adjusting the presentation of the analysis according to the artist's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the artist's emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the song during the analysis. For example, the analysis unit performs a detailed analysis for important songs. For general songs, the analysis unit can perform a standard analysis. For songs in the prototype stage, the analysis unit can perform a simplified analysis. By adjusting the level of detail of the analysis based on the importance of the song, a more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input song importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the category of the music during analysis. For example, in the case of pop music, the analysis unit can apply an analysis algorithm specialized for pop music. In the case of classical music, the analysis unit can apply an analysis algorithm specialized for classical music. In the case of jazz music, the analysis unit can apply an analysis algorithm specialized for jazz. By applying different analysis algorithms depending on the category of the music, more appropriate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input music category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0079] The analysis unit can estimate the artist's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the artist is relaxed, the analysis unit can perform a detailed analysis. If the artist is stressed, the analysis unit can perform a concise analysis. If the artist is focused, the analysis unit can perform a deep, in-depth analysis. By adjusting the length of the analysis according to the artist's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the artist's emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0080] The analysis unit can determine the priority of analysis based on the production date of the songs during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent songs. The analysis unit can postpone the analysis of older songs. The analysis unit can prioritize the analysis of songs currently in production. By determining the priority of analysis based on the production date of the songs, more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input song production date data into a generating AI and have the generating AI determine the priority of analysis.

[0081] The analysis unit can adjust the order of analysis based on the relevance of the songs during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant songs. The analysis unit can postpone the analysis of less relevant songs. The analysis unit can group relevant songs together for analysis. This allows for a more appropriate analysis by adjusting the order of analysis based on the relevance of the songs. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input song relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0082] The suggestion unit can estimate the artist's emotions and adjust the way the suggestion is expressed based on the estimated emotions. For example, if the artist is relaxed, the suggestion unit can provide detailed suggestions. If the artist is stressed, the suggestion unit can provide concise suggestions. If the artist is focused, the suggestion unit can provide in-depth suggestions. By adjusting the way the suggestion is expressed according to the artist's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input the artist's emotion data into the generative AI and have the generative AI adjust the way the suggestion is expressed.

[0083] The proposal unit can adjust the level of detail of its proposals based on the importance of the song. For example, the proposal unit will provide detailed proposals for important songs. For general songs, it can provide standard proposals. For songs in the prototype stage, it can provide simplified proposals. By adjusting the level of detail of the proposals based on the importance of the song, more appropriate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input song importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0084] The suggestion unit can apply different suggestion algorithms depending on the category of the music when making suggestions. For example, in the case of pop music, the suggestion unit can apply a suggestion algorithm specialized for pop music. In the case of classical music, the suggestion unit can apply a suggestion algorithm specialized for classical music. In the case of jazz music, the suggestion unit can apply a suggestion algorithm specialized for jazz. By applying different suggestion algorithms depending on the category of music, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input music category data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0085] The suggestion unit can estimate the artist's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the artist is relaxed, the suggestion unit can provide a detailed suggestion. If the artist is stressed, the suggestion unit can provide a concise suggestion. If the artist is focused, the suggestion unit can provide a more in-depth suggestion. By adjusting the length of the suggestion according to the artist's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input the artist's emotion data into a generative AI and have the generative AI adjust the length of the suggestion.

[0086] The proposal unit can determine the priority of proposals based on the production date of the songs when making a proposal. For example, the proposal unit can prioritize the most recent songs. The proposal unit can postpone older songs. The proposal unit can also prioritize songs currently in production. By determining the priority of proposals based on the production date of the songs, more appropriate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input song production date data into a generating AI and have the generating AI perform the determination of proposal priorities.

[0087] The suggestion unit can adjust the order of suggestions based on the relevance of the songs during the suggestion process. For example, the suggestion unit can prioritize suggesting songs with high relevance. The suggestion unit can postpone suggesting songs with low relevance. The suggestion unit can group related songs together and suggest them. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of the songs. 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 song relevance data into a generating AI and have the generating AI adjust the order of suggestions.

[0088] The generation unit can estimate the artist's emotions and adjust the expression of the generated lyrics based on the estimated emotions. For example, if the artist is relaxed, the generation unit can generate soft lyrics. If the artist is stressed, the generation unit can generate powerful lyrics. If the artist is focused, the generation unit can generate deeply introspective lyrics. By adjusting the expression of the lyrics according to the artist's emotions, more appropriate lyrics are generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the artist's emotion data into the generation AI and have the generation AI adjust the expression of the lyrics.

[0089] The generation unit can adjust the level of detail of the generated lyrics based on the importance of the song during the generation process. For example, the generation unit can generate detailed lyrics for important songs. For general songs, the generation unit can generate standard lyrics. For songs in the prototype stage, the generation unit can generate simplified lyrics. By adjusting the level of detail based on the importance of the song, more appropriate lyrics can be generated. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input song importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the generated lyrics.

[0090] The generation unit can apply different generation algorithms depending on the category of the song during generation. For example, in the case of pop music, the generation unit can apply a generation algorithm specialized for pop music. In the case of classical music, the generation unit can apply a generation algorithm specialized for classical music. In the case of jazz music, the generation unit can apply a generation algorithm specialized for jazz. By applying different generation algorithms depending on the category of the song, more appropriate lyrics can be generated. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input song category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0091] The generation unit can estimate the artist's emotions and adjust the length of the generated lyrics based on the estimated emotions. For example, if the artist is relaxed, the generation unit can generate longer lyrics. If the artist is stressed, the generation unit can generate shorter lyrics. If the artist is focused, the generation unit can generate lyrics of an appropriate length. By adjusting the length of the lyrics according to the artist's emotions, more appropriate lyrics can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the artist's emotion data into the generation AI and have the generation AI adjust the length of the lyrics.

[0092] The generation unit can determine the generation priority based on the song's production date during the generation process. For example, the generation unit can prioritize generating the most recent song. The generation unit can postpone generating older songs. The generation unit can prioritize generating songs currently in production. By determining the generation priority based on the song's production date, more appropriate lyrics are generated. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input song production date data into a generation AI and have the generation AI determine the generation priority.

[0093] The generation unit can adjust the generation order based on the relevance of the songs during generation. For example, the generation unit can prioritize generating songs with high relevance. The generation unit can postpone generating songs with low relevance. The generation unit can group related songs together for generation. By adjusting the generation order based on the relevance of the songs, more appropriate lyrics can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input song relevance data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0094] The service provider can estimate the artist's emotions and adjust the way ideas are presented based on the estimated emotions. For example, if the artist is relaxed, the service provider can provide detailed ideas. If the artist is stressed, the service provider can provide concise ideas. If the artist is focused, the service provider can provide in-depth ideas. By adjusting the way ideas are presented according to the artist's emotions, more appropriate ideas are provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input the artist's emotion data into a generative AI and have the generative AI adjust the way ideas are presented.

[0095] The service provider can adjust the level of detail provided based on the importance of the song at the time of provision. For example, the service provider can provide detailed ideas for important songs. For general songs, it can provide standard ideas. For songs in the prototype stage, it can provide simple ideas. By adjusting the level of detail based on the importance of the song, more appropriate ideas can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input song importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the provision.

[0096] The providing unit can apply different providing algorithms depending on the category of the music at the time of provision. For example, in the case of pop music, the providing unit can apply a providing algorithm specialized for pop music. In the case of classical music, the providing unit can apply a providing algorithm specialized for classical music. In the case of jazz music, the providing unit can apply a providing algorithm specialized for jazz. By applying different providing algorithms depending on the category of the music, more appropriate ideas can be provided. Some or all of the above processing in the providing unit may be performed using AI, for example, or without using AI. For example, the providing unit can input music category data into a generating AI and have the generating AI execute the application of the providing algorithm.

[0097] The service provider can estimate the artist's emotions and adjust the length of the ideas it provides based on the estimated emotions. For example, if the artist is relaxed, the service provider can provide detailed ideas. If the artist is stressed, the service provider can provide concise ideas. If the artist is focused, the service provider can provide in-depth ideas. By adjusting the length of ideas according to the artist's emotions, more appropriate ideas are provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input the artist's emotion data into a generative AI and have the generative AI adjust the length of the ideas.

[0098] The distribution unit can determine the priority of distribution based on the production date of the songs at the time of distribution. For example, the distribution unit may prioritize the provision of the latest songs. The distribution unit may postpone the provision of older songs. The distribution unit may prioritize the provision of songs that are currently being produced. By determining the priority of distribution based on the production date of the songs, more appropriate ideas can be provided. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit may input song production date data into a generating AI and have the generating AI perform the determination of the distribution priority.

[0099] The distribution unit can adjust the order of distribution based on the relevance of the songs. For example, the distribution unit can prioritize providing songs with high relevance. The distribution unit can postpone providing songs with low relevance. The distribution unit can group related songs together and provide them. By adjusting the order of distribution based on the relevance of the songs, more appropriate ideas can be provided. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input song relevance data into a generating AI and have the generating AI perform the adjustment of the distribution order.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The data collection unit acquires the artist's biometric data and can adjust the collection timing based on the artist's health condition. For example, if the artist's heart rate and blood pressure are stable, the collection timing can be flexibly set to collect information at the artist's pace. If the artist's health condition is unstable, the collection timing can be shortened to quickly collect the necessary information. This allows for more appropriate information collection by adjusting the collection timing according to the artist's health condition.

[0102] The data collection unit can estimate the artist's emotions and adjust the type of information collected based on that estimation. For example, if the artist is relaxed, it can prioritize collecting creative ideas. If the artist is stressed, it can prioritize collecting practical information. If the artist is focused, it can prioritize collecting technical information. By adjusting the type of information according to the artist's emotions, more appropriate information collection becomes possible.

[0103] The analysis unit can analyze an artist's past music production history and select the optimal analysis method. For example, it can analyze the production history of successful songs an artist has produced in the past and suggest a similar analysis method. It can also analyze the production history of unsuccessful songs an artist has produced in the past and suggest a different analysis method. In this way, the optimal analysis method can be selected by analyzing past production history.

[0104] The analysis unit can estimate the artist's emotions and adjust the depth of the analysis based on the estimated emotions. For example, if the artist is relaxed, a detailed analysis can be performed. If the artist is stressed, a concise analysis can be performed. If the artist is focused, a deep analysis can be performed. By adjusting the depth of the analysis according to the artist's emotions, more appropriate analysis results can be provided.

[0105] The proposal system can estimate the artist's emotions and adjust the timing of proposals based on those estimates. For example, if the artist is relaxed, the timing of proposals can be set flexibly. If the artist is stressed, proposals can be made quickly. If the artist is focused, proposals can be made in-depth. By adjusting the timing of proposals according to the artist's emotions, more appropriate proposals can be made.

[0106] The proposal department can analyze an artist's past music production history and select the most suitable proposal method. For example, it can analyze the production history of successful songs an artist has produced in the past and propose a similar proposal method. Conversely, it can analyze the production history of unsuccessful songs an artist has produced in the past and propose a different proposal method. In this way, by analyzing past production history, the most suitable proposal method can be selected.

[0107] The generation unit can estimate the artist's emotions and adjust the tempo of the generated melody based on those emotions. For example, if the artist is relaxed, it can generate a slow-tempo melody. If the artist is stressed, it can generate a fast-tempo melody. If the artist is focused, it can generate a moderate-tempo melody. By adjusting the tempo of the melody according to the artist's emotions, a more appropriate melody can be generated.

[0108] The generation unit can analyze an artist's past music production history and select the optimal generation method. For example, it can analyze the production history of successful songs an artist has produced in the past and suggest a similar generation method. It can also analyze the production history of unsuccessful songs an artist has produced in the past and suggest a different generation method. In this way, the optimal generation method can be selected by analyzing past production history.

[0109] The service provider can estimate the artist's emotions and adjust the level of detail of the ideas provided based on that estimation. For example, if the artist is relaxed, detailed ideas can be provided. If the artist is stressed, concise ideas can be provided. If the artist is focused, deeply explored ideas can be provided. By adjusting the level of detail of ideas according to the artist's emotions, more appropriate ideas can be provided.

[0110] The distribution department can analyze an artist's past music production history and select the optimal distribution method. For example, it can analyze the production history of successful songs an artist has produced in the past and propose a similar distribution method. It can also analyze the production history of unsuccessful songs an artist has produced in the past and propose a different distribution method. In this way, the optimal distribution method can be selected by analyzing past production history.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The data collection unit gathers information from the artist regarding the song's structure and harmony. For example, it can collect information such as melodies, chord progressions, and rhythm patterns entered by the artist. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it can use an automated music theory analysis function to analyze the collected information and make suggestions based on the most suitable music theory. Step 3: The proposal unit makes style suggestions based on the latest music trends, using the analysis results obtained by the analysis unit. For example, it can make style suggestions to artists based on the latest music trends such as genre, popular artists, and trendy sounds. Step 4: The generation unit generates lyric ideas based on the content proposed by the proposal unit. For example, natural language processing can be used to generate lyric ideas such as themes, phrases, and word choices. Step 5: The providing unit provides the artist with the ideas generated by the generating unit. For example, by providing the generated ideas to the artist, the artist's creative process can be supported.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart device 14 and collects information on the composition and harmony of songs entered by the artist. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information based on music theory. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes style suggestions based on the latest music trends. The generation unit is implemented by the control unit 46A of the smart device 14 and generates lyric ideas using natural language processing. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated ideas to the artist. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart glasses 214 and collects information on the composition and harmony of songs entered by the artist. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information based on music theory. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes style suggestions based on the latest music trends. The generation unit is implemented by the control unit 46A of the smart glasses 214 and generates lyric ideas using natural language processing. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated ideas to the artist. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the headset terminal 314 and collects information on the composition and harmony of songs entered by the artist. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information based on music theory. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes style suggestions based on the latest music trends. The generation unit is implemented by the control unit 46A of the headset terminal 314 and generates lyric ideas using natural language processing. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated ideas to the artist. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, generation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the robot 414 and collects information on the composition and harmony of songs input by the artist. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected information based on music theory. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes style suggestions based on the latest music trends. The generation unit is implemented by, for example, the control unit 46A of the robot 414 and generates lyric ideas using natural language processing. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides the generated ideas to the artist. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A collection department that gathers information from artists regarding the structure and harmony of songs, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit makes style suggestions based on the latest music trends, A generation unit that generates lyric ideas based on the content proposed by the aforementioned proposal unit, The system comprises a providing unit that provides the ideas generated by the generation unit to the artist. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system estimates the artist's emotions and adjusts the timing of information gathering regarding the song's structure and harmonies based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is We analyze the artist's past music production history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When gathering information about song structure and harmony, filter the results based on the artist's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is We estimate the artist's emotions and prioritize the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting information about song structure and harmony, the geographical location of the artist is taken into consideration to prioritize the collection of highly relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When gathering information about song structure and harmony, we analyze the artist's social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, We estimate the artist's emotions and adjust the method of expression of the analysis based on the estimated emotions of the artist. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of each song. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the song category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system estimates the artist's emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on the production date of the songs. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the songs. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, It estimates the artist's emotions and adjusts the expression of the proposal based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the song. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the song category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, Estimate the artist's emotions and adjust the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When submitting proposals, prioritize them based on the production timeline of the songs. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the songs. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates the artist's emotions and adjusts the way lyrics are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the level of detail is adjusted based on the importance of the songs. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, different generation algorithms are applied depending on the song category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is It estimates the artist's emotions and adjusts the length of the generated lyrics based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the generation priority is determined based on the song's production date. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is During generation, the generation order is adjusted based on the relevance of the songs. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the artist's emotions and adjusts the way the ideas are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the music, adjust the level of detail based on the importance of the song. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing music, different distribution algorithms are applied depending on the music category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, It estimates the artist's emotions and adjusts the length of the ideas provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing songs, we will determine the priority of which songs to provide based on their production dates. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing the songs, we adjust the order of provision based on their relevance. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that gathers information from artists regarding the structure and harmony of songs, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit makes style suggestions based on the latest music trends, A generation unit that generates lyric ideas based on the content proposed by the aforementioned proposal unit, The system comprises a providing unit that provides the ideas generated by the generation unit to the artist. A system characterized by the following features.

2. The aforementioned collection unit is The system estimates the artist's emotions and adjusts the timing of information gathering regarding the song's structure and harmonies based on those estimated emotions. The system according to feature 1.

3. The aforementioned collection unit is We analyze the artist's past music production history and select the optimal collection method. The system according to feature 1.

4. The aforementioned collection unit is When gathering information about song structure and harmony, filter the results based on the artist's current projects and areas of interest. The system according to feature 1.

5. The aforementioned collection unit is We estimate the artist's emotions and prioritize the information to collect based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting information about song structure and harmony, the geographical location of the artist is taken into consideration to prioritize the collection of highly relevant information. The system according to feature 1.

7. The aforementioned collection unit is When gathering information about song structure and harmony, we analyze the artist's social media activity and collect relevant information. The system according to feature 1.

8. The aforementioned analysis unit, We estimate the artist's emotions and adjust the method of expression of the analysis based on the estimated emotions of the artist. The system according to feature 1.

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