system

Abstract

The system according to this embodiment aims to support the creative activities of composers and musicians and to overcome creative stagnation and monotony. [Solution] The system according to the embodiment comprises an analysis unit, a suggestion unit, and a learning unit. The analysis unit analyzes the user's musical preferences, past works, and current emotional state. The suggestion unit proposes new melodies, harmonies, and rhythm patterns based on the analysis results obtained by the analysis unit. The learning unit learns the user's style based on the ideas proposed by the suggestion unit and provides fresh ideas as a collaborator.

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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 conventional technology, there has been a problem that composers and musicians may face depletion of creative ideas and routinization in their creative activities, and there is not enough support to eliminate this problem.

[0005] The system according to the embodiment aims to support the creative activities of composers and musicians and eliminate depletion of creative ideas and routinization.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a suggestion unit, and a learning unit. The analysis unit analyzes the user's musical preferences, past works, and current emotional state. The suggestion unit proposes new melodies, harmonies, and rhythmic patterns based on the analysis results obtained by the analysis unit. The learning unit learns the user's style based on the ideas proposed by the suggestion unit and provides fresh ideas as a collaborator. [Effects of the Invention]

[0007] The system according to this embodiment can support the creative activities of composers and musicians, and can overcome creative stagnation and monotony. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a reception 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 reception 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 AI ​​agent system according to an embodiment of the present invention is a system that innovates the creative activities of composers and musicians. This AI agent system analyzes the user's musical preferences, past works, and current emotional state, and proposes new melodies, harmonies, and rhythm patterns, thereby eliminating creative idea exhaustion and stagnation, and maximizing creativity. This AI agent system not only serves as a source of inspiration but also provides technical support to music creators of all levels, from professional composers to amateur musicians. The AI ​​agent system learns the user's style and constantly supplies fresh ideas as a collaborator. For example, the AI ​​agent system analyzes the user's musical preferences, past works, and current emotional state. For example, the AI ​​agent system analyzes the user's musical preferences, past works, and current emotional state and proposes new melodies, harmonies, and rhythm patterns. Furthermore, the AI ​​agent system learns the user's style and supplies fresh ideas as a collaborator. Through this mechanism, it not only serves as a source of inspiration but also provides technical support to music creators of all levels, from professional composers to amateur musicians. For example, if a user is given new melodies, harmonies, or rhythmic patterns, they can create a new song based on them. Furthermore, the technical advice provided by the AI ​​can help overcome the limitations of music theory and broaden the range of expression. This can resolve challenges such as creative stagnation and creative burnout, maximizing creativity. The AI ​​agent system can then suggest new ideas based on the user's musical preferences and emotional state, thereby maximizing their creativity.

[0029] The AI ​​agent system according to this embodiment comprises an analysis unit, a suggestion unit, and a learning unit. The analysis unit analyzes the user's musical preferences, past works, and current emotional state. The user's musical preferences include, but are not limited to, genres, artists, and musical styles. The analysis unit analyzes, for example, the user's past works and extracts specific patterns and tendencies. Past works include, for example, released songs and unreleased demos. The analysis unit monitors, for example, the user's current emotional state in real time and reflects it in the analysis results. The current emotional state includes, for example, emotion recognition technology and the user's self-reporting. The suggestion unit proposes new melodies, harmonies, and rhythm patterns based on the analysis results obtained by the analysis unit. The suggestion unit generates new melodies, harmonies, and rhythm patterns by methods such as music theory-based generation and random generation. The suggestion unit proposes new melodies and chord progressions according to the user's musical style and goals. The user's musical style includes, but is not limited to, genres and artist influences. The suggestion unit supports a wide range of genres, from rock to classical and electronica. A wide range of genres includes, but is not limited to, rock, classical, and electronica. The suggestion unit allows users to develop ideas while interacting with AI during composition. Interaction with AI includes, but is not limited to, chatbots and voice dialogues. The suggestion unit provides, for example, expert feedback based on music theory. Music theory includes, but is not limited to, harmony theory and rhythm theory. The suggestion unit provides, for example, new perspectives and ideas when users encounter creative obstacles. Creative obstacles include, but are not limited to, idea depletion and decreased motivation. The learning unit learns the user's style based on ideas suggested by the suggestion unit and provides fresh ideas as a collaborator. The user's style includes, but is not limited to, performance style and composition style.The learning unit, for example, estimates the user's emotions and selects training data based on the estimated user emotions. The selection of training data includes, but is not limited to, data quality and relevance. The learning unit optimizes the learning algorithm by referring to past training data. Optimization of the learning algorithm includes, but is not limited to, parameter tuning and model updates. The learning unit reflects changes in the user's musical preferences in real time. Changes in musical preferences include, but are not limited to, the use of real-time data and feedback. As a result, the AI ​​agent system according to this embodiment can propose new ideas based on the user's musical preferences and emotional state, maximizing creativity.

[0030] The analytics department analyzes users' musical preferences, past works, and current emotional states. Users' musical preferences include, but are not limited to, genres, artists, and musical styles. The analytics department also analyzes users' past works to extract specific patterns and tendencies. Past works include, but are not limited to, released songs and unreleased demos. Furthermore, the analytics department monitors users' current emotional states in real time and incorporates this into the analysis results. Current emotional states include, but are not limited to, emotion recognition technology and user self-reporting. Specifically, the analytics department collects data from users' music streaming history and playlists to identify preferred music genres and artists. It also analyzes what kind of music users listen to in what situations, understanding their musical preferences based on time of day and activities. In analyzing past works, the department analyzes melodies, harmonies, rhythm patterns, and lyrics in detail to extract users' composition styles and characteristic patterns. This clarifies what musical elements users prefer. Emotional state monitoring involves acquiring biometric information such as the user's facial expressions, voice tone, and heart rate in real time, and using emotion recognition technology to estimate the user's current emotions. For example, the analysis results might reflect a tendency for users to prefer calm music when relaxed and upbeat music when energetic. This allows the analysis unit to comprehensively understand the user's musical preferences and emotional state, providing a foundation for suggesting music that is optimal for each individual user.

[0031] The suggestion unit proposes new melodies, harmonies, and rhythm patterns based on the analysis results obtained by the analysis unit. The suggestion unit generates new melodies, harmonies, and rhythm patterns using methods such as generation based on music theory or random generation. Specifically, the suggestion unit generates new melodies and chord progressions that match the user's preferred musical style, based on the user's musical preferences and analysis results of past works. For example, if the user prefers jazz, the suggestion unit will propose complex chord progressions and improvisational melodies based on jazz music theory. If the user prefers pop music, it will propose catchy melodies and simple chord progressions. The suggestion unit also takes the user's current emotional state into consideration and proposes music that matches their emotions. For example, when the user is relaxed, it will propose calm melodies and relaxed rhythm patterns, and when the user is energetic, it will propose up-tempo rhythms and powerful harmonies. Furthermore, the suggestion unit provides chatbot and voice interaction functions so that users can refine their ideas while interacting with the AI ​​during composition. Users can concretize their ideas while interacting with the AI ​​and proceed with composition based on feedback from the AI. The suggestion department provides expert feedback based on music theory, helping users create more sophisticated music. For example, it suggests chord progression options based on harmony theory or provides variations in rhythm patterns based on rhythm theory. In this way, the suggestion department can maximize user creativity and provide new musical ideas.

[0032] The learning unit learns the user's style based on ideas proposed by the suggestion unit and provides fresh ideas as a collaborator. The user's style includes, but is not limited to, playing style, composition style, etc. The learning unit, for example, estimates the user's emotions and selects training data based on these estimated emotions. Specifically, the learning unit observes how the user reacts to proposed ideas and learns the user's preferences and style based on those reactions. For example, if a user shows a favorable reaction to a particular melody or rhythm pattern, the learning unit learns that pattern and incorporates it into future suggestions. The learning unit optimizes its learning algorithm by referring to past training data. For example, it adjusts parameters and updates the model to improve the accuracy of suggestions based on the user's past reaction data. This allows the learning unit to respond in real time to changes in the user's musical preferences and always provide suggestions based on the latest information. Furthermore, the learning unit collects user feedback and continuously improves the accuracy and effectiveness of its suggestions. For example, it records how the user evaluates proposed ideas and adjusts the suggestion algorithm based on that evaluation. This allows the learning unit to provide optimal suggestions tailored to the user's needs and support their creativity. The learning unit comprehensively learns the user's musical preferences and emotional state, providing a foundation for making the most effective suggestions. This enables the learning unit to maximize the user's creativity and provide new musical ideas.

[0033] The suggestion function can propose new melodies and chord progressions tailored to the user's musical style and goals. For example, it can suggest new melodies based on the user's musical style. For instance, it can generate melodies considering the user's preferred genres and the influence of their favorite artists. It can also suggest new chord progressions based on the user's goals. For example, if the user is aiming to complete a song, it can suggest chord progressions suitable for that goal. Furthermore, if the user wants to acquire a specific skill, it can suggest chord progressions related to that skill. This allows for suggestions tailored to the user's musical style and goals.

[0034] The suggestion function can handle a wide range of genres, from rock to classical and electronica. For example, it can suggest rock melodies and chord progressions. For instance, it can generate melodies that include characteristic rock rhythm patterns and guitar riffs. It can also suggest classical melodies and harmonies. For example, it can generate melodies based on classical harmony theory. Furthermore, it can suggest electronica rhythm patterns and sound designs. For example, it can generate rhythm patterns that include characteristic electronica synthesizer timbres and beats. This broad range of genres allows it to accommodate various musical styles.

[0035] The suggestion function allows users to refine their ideas while interacting with AI during the composition process. For example, the suggestion function can propose ideas while interacting with the user using a chatbot. For instance, it can suggest new melodies and harmonies based on text entered by the user. It can also propose ideas while interacting with the user using voice dialogue. For example, it can suggest new rhythm patterns based on what the user says. Furthermore, the suggestion function can refine ideas while receiving real-time user feedback. For example, it can provide real-time feedback to the user on the proposed ideas and revise the ideas based on that feedback. This allows users to refine their ideas while interacting with AI during the composition process.

[0036] The suggestion function can provide expert feedback based on music theory. For example, it can provide feedback on melody and harmony based on harmony theory. For instance, it can suggest revisions to a melody created by the user based on harmony theory. It can also provide feedback on rhythm patterns based on rhythm theory. For example, it can suggest revisions to a rhythm pattern created by the user based on rhythm theory. Furthermore, the suggestion function can provide comprehensive feedback based on music theory in general. For example, it can provide advice based on music theory for the entire piece of music created by the user. This enables technical support by providing expert feedback based on music theory.

[0037] The suggestion section can provide new perspectives and ideas when users face creative blocks. For example, it can suggest new melodies or harmonies when users are running out of ideas. For example, it can generate new ideas based on parts of songs created by users. It can also suggest new rhythm patterns when users are experiencing a decline in motivation. For example, it can suggest new variations to rhythm patterns created by users. Furthermore, the suggestion section can provide new perspectives when users are facing creative blocks. For example, it can suggest musical styles or genres that users don't usually use, providing new inspiration. In this way, it can overcome creative blocks by providing new perspectives and ideas when users encounter creative obstacles.

[0038] The analysis unit can analyze the structure of a user's past works in detail and extract specific patterns and trends. For example, the analysis unit can analyze the melody lines of a user's past works and extract frequently used phrases. The analysis unit can also analyze the chord progressions of a user's past works and extract frequently used chord progression patterns. Furthermore, the analysis unit can analyze the rhythm patterns of a user's past works and extract frequently used rhythm patterns. In this way, by analyzing the structure of a user's past works in detail, specific patterns and trends can be extracted.

[0039] The analytics department can take into account users' social media activity when analyzing their musical preferences. For example, it can analyze the music users share on social media and reflect those preferences. It can also analyze the music of artists users follow on social media and reflect those preferences. Furthermore, it can analyze the music users "like" on social media and reflect those preferences. This allows for a more accurate analysis of musical preferences by taking users' social media activity into consideration.

[0040] The analysis unit can take into account the user's geographical location when analyzing their musical preferences. For example, the analysis unit can analyze the musical trends of the area where the user lives and reflect those preferences. The analysis unit can also analyze the music the user listened to while traveling and reflect those preferences. Furthermore, the analysis unit can analyze the music the user listened to when attending a specific event (e.g., a concert) and reflect those preferences. By taking the user's geographical location into account, a more accurate analysis of musical preferences becomes possible.

[0041] The suggestion function can adjust the level of detail of its suggestions based on the user's musical goals. For example, if the user is a professional composer, the suggestion function can provide detailed melody and harmony suggestions. It can also provide simpler melody and harmony suggestions if the user is an amateur musician. Furthermore, if the user is working on a specific project (e.g., film music), the suggestion function can provide suggestions tailored to that project. This allows for more appropriate suggestions by adjusting the level of detail based on the user's musical goals.

[0042] The suggestion function can apply different suggestion algorithms depending on the user's musical preferences when making suggestions. For example, if the user prefers rock music, the suggestion function can apply a suggestion algorithm specifically for rock. Similarly, if the user prefers classical music, the suggestion function can apply a suggestion algorithm specifically for classical music. Furthermore, if the user prefers electronica, the suggestion function can apply a suggestion algorithm specifically for electronica. This allows for more appropriate suggestions by applying different suggestion algorithms according to the user's musical preferences.

[0043] The proposal team can prioritize proposals based on the submission dates of the user's past works. For example, the proposal team can prioritize analyzing the user's most recently submitted works and make suggestions based on those trends. The proposal team can also prioritize proposals from older works to newer ones, taking into account the submission dates of the user's past works. Furthermore, the proposal team can analyze works submitted by the user during a specific period and make suggestions based on the trends during that period. This allows for more appropriate suggestions by prioritizing proposals based on the submission dates of the user's past works.

[0044] The suggestion function can adjust the order of suggestions based on the relevance of the user's musical preferences. For example, the suggestion function can prioritize suggesting songs in genres the user likes. It can also prioritize suggestions that are similar in style to songs the user has previously created. Furthermore, if the user is influenced by a particular artist, the suggestion function can prioritize suggestions that are similar in style to that artist. By adjusting the order of suggestions based on the relevance of the user's musical preferences, more appropriate suggestions can be made.

[0045] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can analyze past learning data and select the most effective learning algorithm. The learning unit can also adjust the parameters of the learning algorithm based on past learning data. For example, the learning unit can adjust the parameters of the learning algorithm based on past learning data. Furthermore, the learning unit can refer to past learning data to identify areas for improvement in the learning algorithm. For example, the learning unit can refer to past learning data to identify areas for improvement in the learning algorithm. In this way, the learning algorithm can be optimized by referring to past learning data.

[0046] The learning unit can reflect changes in the user's musical preferences in real time during training. For example, if the user's musical preferences change, the learning unit can reflect that change in the training data in real time. The learning unit can also add songs from a new genre to the training data if the user becomes interested in that genre. Furthermore, if the user becomes interested in a particular artist, the learning unit can add songs by that artist to the training data. This allows for more appropriate learning by reflecting changes in the user's musical preferences in real time.

[0047] The learning unit can weight the training data based on when the user submitted their musical preferences during training. For example, the learning unit can weight the training data by giving more weight to the user's most recently submitted musical preferences. The learning unit can also consider when the user submitted their musical preferences in the past and give more weight to newer preferences than older ones. Furthermore, the learning unit can analyze the musical preferences the user submitted during a specific period and weight the training data based on the preferences during that period. This allows for more appropriate training by weighting the training data based on when the user submitted their musical preferences.

[0048] The learning unit can perform training while considering the geographical distribution of users' musical preferences. For example, the learning unit can reflect the musical trends of the area where the user lives into the training data. The learning unit can also perform training while considering the geographical distribution of music listened to by users while traveling. For example, the learning unit can perform training while considering the geographical distribution of music listened to by users while traveling. Furthermore, the learning unit can reflect the geographical distribution of music listened to by users at specific events (e.g., concerts) into the training data. For example, the learning unit can reflect the geographical distribution of music listened to by users at specific events (e.g., concerts) into the training data. This allows for more appropriate training by considering the geographical distribution of users' musical preferences.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The suggestion function can adjust the complexity of the melodies and harmonies it suggests based on the user's musical preferences. For example, if the user prefers simple melodies, the suggestion function can suggest simple and easy-to-remember melodies. If the user prefers complex harmonies, the suggestion function can suggest complex and multi-layered harmonies. Furthermore, if the user seeks complexity in a particular genre, it can suggest complex melodies and harmonies specific to that genre. By adjusting the complexity of the suggestions according to the user's musical preferences, more appropriate suggestions can be made.

[0051] The analytics department can take into account users' life events when analyzing their musical preferences. For example, if a user is getting married, the analytics department can suggest songs suitable for a wedding. Similarly, if a user is celebrating a birthday, the analytics department can suggest songs appropriate for that occasion. Furthermore, if a user is approaching a specific anniversary, the analytics department can suggest songs suitable for that anniversary. This allows for a more accurate analysis of musical preferences by taking users' life events into account.

[0052] The suggestion function can adjust the types of instruments it suggests based on the user's musical preferences. For example, if the user prefers the guitar, the suggestion function can suggest songs primarily featuring the guitar. Similarly, if the user prefers the piano, it can suggest songs primarily featuring the piano. Furthermore, if the user prefers electronica, it can suggest songs primarily featuring synthesizers. By adjusting the types of instruments suggested according to the user's musical preferences, more appropriate suggestions can be made.

[0053] The analytics department can take into account a user's physical activity when analyzing their musical preferences. For example, if a user is running, the analytics department can suggest music with a tempo suitable for running. If a user is doing yoga, the analytics department can suggest relaxing music. Furthermore, if a user is doing strength training, the analytics department can suggest music that boosts motivation. By taking the user's physical activity into account, a more appropriate analysis of their musical preferences becomes possible.

[0054] The suggestion function can adjust the length of the suggested songs based on the user's musical preferences. For example, if the user prefers short songs, the suggestion function can suggest short songs. Conversely, if the user prefers long songs, the suggestion function can suggest longer songs. Furthermore, if the user requests a specific song length within a particular genre, the function can suggest song lengths specific to that genre. By adjusting the length of suggested songs according to the user's musical preferences, more appropriate suggestions can be made.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The analysis unit analyzes the user's musical preferences, past works, and current emotional state. The user's musical preferences include genre, artist, and musical style, while past works include released songs and unreleased demos. The current emotional state is monitored in real time through emotion recognition technology and user self-reporting. Step 2: The suggestion unit proposes new melodies, harmonies, and rhythm patterns based on the analysis results obtained by the analysis unit. The suggestion unit generates new melodies, harmonies, and rhythm patterns using methods such as music theory-based generation and random generation, and makes suggestions tailored to the user's musical style and goals. The suggestion unit supports a wide range of genres, and users can refine their ideas while interacting with the AI ​​during the composition process. Step 3: The learning unit learns the user's style based on the ideas proposed by the suggestion unit and provides fresh ideas as a collaborator. The learning unit estimates the user's emotions, selects training data based on the estimated emotions, and optimizes the learning algorithm by referring to past training data. It also reflects changes in the user's musical preferences in real time.

[0057] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that innovates the creative activities of composers and musicians. This AI agent system analyzes the user's musical preferences, past works, and current emotional state, and proposes new melodies, harmonies, and rhythm patterns, thereby eliminating creative idea exhaustion and stagnation, and maximizing creativity. This AI agent system not only serves as a source of inspiration but also provides technical support to music creators of all levels, from professional composers to amateur musicians. The AI ​​agent system learns the user's style and constantly supplies fresh ideas as a collaborator. For example, the AI ​​agent system analyzes the user's musical preferences, past works, and current emotional state. For example, the AI ​​agent system analyzes the user's musical preferences, past works, and current emotional state and proposes new melodies, harmonies, and rhythm patterns. Furthermore, the AI ​​agent system learns the user's style and supplies fresh ideas as a collaborator. Through this mechanism, it not only serves as a source of inspiration but also provides technical support to music creators of all levels, from professional composers to amateur musicians. For example, if a user is given new melodies, harmonies, or rhythmic patterns, they can create a new song based on them. Furthermore, the technical advice provided by the AI ​​can help overcome the limitations of music theory and broaden the range of expression. This can resolve challenges such as creative stagnation and creative burnout, maximizing creativity. The AI ​​agent system can then suggest new ideas based on the user's musical preferences and emotional state, thereby maximizing their creativity.

[0058] The AI ​​agent system according to this embodiment comprises an analysis unit, a suggestion unit, and a learning unit. The analysis unit analyzes the user's musical preferences, past works, and current emotional state. The user's musical preferences include, but are not limited to, genres, artists, and musical styles. The analysis unit analyzes, for example, the user's past works and extracts specific patterns and tendencies. Past works include, for example, released songs and unreleased demos. The analysis unit monitors, for example, the user's current emotional state in real time and reflects it in the analysis results. The current emotional state includes, for example, emotion recognition technology and the user's self-reporting. The suggestion unit proposes new melodies, harmonies, and rhythm patterns based on the analysis results obtained by the analysis unit. The suggestion unit generates new melodies, harmonies, and rhythm patterns by methods such as music theory-based generation and random generation. The suggestion unit proposes new melodies and chord progressions according to the user's musical style and goals. The user's musical style includes, but is not limited to, genres and artist influences. The suggestion unit supports a wide range of genres, from rock to classical and electronica. A wide range of genres includes, but is not limited to, rock, classical, and electronica. The suggestion unit allows users to develop ideas while interacting with AI during composition. Interaction with AI includes, but is not limited to, chatbots and voice dialogues. The suggestion unit provides, for example, expert feedback based on music theory. Music theory includes, but is not limited to, harmony theory and rhythm theory. The suggestion unit provides, for example, new perspectives and ideas when users encounter creative obstacles. Creative obstacles include, but are not limited to, idea depletion and decreased motivation. The learning unit learns the user's style based on ideas suggested by the suggestion unit and provides fresh ideas as a collaborator. The user's style includes, but is not limited to, performance style and composition style.The learning unit, for example, estimates the user's emotions and selects training data based on the estimated user emotions. The selection of training data includes, but is not limited to, data quality and relevance. The learning unit optimizes the learning algorithm by referring to past training data. Optimization of the learning algorithm includes, but is not limited to, parameter tuning and model updates. The learning unit reflects changes in the user's musical preferences in real time. Changes in musical preferences include, but are not limited to, the use of real-time data and feedback. As a result, the AI ​​agent system according to this embodiment can propose new ideas based on the user's musical preferences and emotional state, maximizing creativity.

[0059] The analytics department analyzes users' musical preferences, past works, and current emotional states. Users' musical preferences include, but are not limited to, genres, artists, and musical styles. The analytics department also analyzes users' past works to extract specific patterns and tendencies. Past works include, but are not limited to, released songs and unreleased demos. Furthermore, the analytics department monitors users' current emotional states in real time and incorporates this into the analysis results. Current emotional states include, but are not limited to, emotion recognition technology and user self-reporting. Specifically, the analytics department collects data from users' music streaming history and playlists to identify preferred music genres and artists. It also analyzes what kind of music users listen to in what situations, understanding their musical preferences based on time of day and activities. In analyzing past works, the department analyzes melodies, harmonies, rhythm patterns, and lyrics in detail to extract users' composition styles and characteristic patterns. This clarifies what musical elements users prefer. Emotional state monitoring involves acquiring biometric information such as the user's facial expressions, voice tone, and heart rate in real time, and using emotion recognition technology to estimate the user's current emotions. For example, the analysis results might reflect a tendency for users to prefer calm music when relaxed and upbeat music when energetic. This allows the analysis unit to comprehensively understand the user's musical preferences and emotional state, providing a foundation for suggesting music that is optimal for each individual user.

[0060] The suggestion unit proposes new melodies, harmonies, and rhythm patterns based on the analysis results obtained by the analysis unit. The suggestion unit generates new melodies, harmonies, and rhythm patterns using methods such as generation based on music theory or random generation. Specifically, the suggestion unit generates new melodies and chord progressions that match the user's preferred musical style, based on the user's musical preferences and analysis results of past works. For example, if the user prefers jazz, the suggestion unit will propose complex chord progressions and improvisational melodies based on jazz music theory. If the user prefers pop music, it will propose catchy melodies and simple chord progressions. The suggestion unit also takes the user's current emotional state into consideration and proposes music that matches their emotions. For example, when the user is relaxed, it will propose calm melodies and relaxed rhythm patterns, and when the user is energetic, it will propose up-tempo rhythms and powerful harmonies. Furthermore, the suggestion unit provides chatbot and voice interaction functions so that users can refine their ideas while interacting with the AI ​​during composition. Users can concretize their ideas while interacting with the AI ​​and proceed with composition based on feedback from the AI. The suggestion department provides expert feedback based on music theory, helping users create more sophisticated music. For example, it suggests chord progression options based on harmony theory or provides variations in rhythm patterns based on rhythm theory. In this way, the suggestion department can maximize user creativity and provide new musical ideas.

[0061] The learning unit learns the user's style based on ideas proposed by the suggestion unit and provides fresh ideas as a collaborator. The user's style includes, but is not limited to, playing style, composition style, etc. The learning unit, for example, estimates the user's emotions and selects training data based on these estimated emotions. Specifically, the learning unit observes how the user reacts to proposed ideas and learns the user's preferences and style based on those reactions. For example, if a user shows a favorable reaction to a particular melody or rhythm pattern, the learning unit learns that pattern and incorporates it into future suggestions. The learning unit optimizes its learning algorithm by referring to past training data. For example, it adjusts parameters and updates the model to improve the accuracy of suggestions based on the user's past reaction data. This allows the learning unit to respond in real time to changes in the user's musical preferences and always provide suggestions based on the latest information. Furthermore, the learning unit collects user feedback and continuously improves the accuracy and effectiveness of its suggestions. For example, it records how the user evaluates proposed ideas and adjusts the suggestion algorithm based on that evaluation. This allows the learning unit to provide optimal suggestions tailored to the user's needs and support their creativity. The learning unit comprehensively learns the user's musical preferences and emotional state, providing a foundation for making the most effective suggestions. This enables the learning unit to maximize the user's creativity and provide new musical ideas.

[0062] The suggestion function can propose new melodies and chord progressions tailored to the user's musical style and goals. For example, it can suggest new melodies based on the user's musical style. For instance, it can generate melodies considering the user's preferred genres and the influence of their favorite artists. It can also suggest new chord progressions based on the user's goals. For example, if the user is aiming to complete a song, it can suggest chord progressions suitable for that goal. Furthermore, if the user wants to acquire a specific skill, it can suggest chord progressions related to that skill. This allows for suggestions tailored to the user's musical style and goals.

[0063] The suggestion function can handle a wide range of genres, from rock to classical and electronica. For example, it can suggest rock melodies and chord progressions. For instance, it can generate melodies that include characteristic rock rhythm patterns and guitar riffs. It can also suggest classical melodies and harmonies. For example, it can generate melodies based on classical harmony theory. Furthermore, it can suggest electronica rhythm patterns and sound designs. For example, it can generate rhythm patterns that include characteristic electronica synthesizer timbres and beats. This broad range of genres allows it to accommodate various musical styles.

[0064] The suggestion function allows users to refine their ideas while interacting with AI during the composition process. For example, the suggestion function can propose ideas while interacting with the user using a chatbot. For instance, it can suggest new melodies and harmonies based on text entered by the user. It can also propose ideas while interacting with the user using voice dialogue. For example, it can suggest new rhythm patterns based on what the user says. Furthermore, the suggestion function can refine ideas while receiving real-time user feedback. For example, it can provide real-time feedback to the user on the proposed ideas and revise the ideas based on that feedback. This allows users to refine their ideas while interacting with AI during the composition process.

[0065] The suggestion function can provide expert feedback based on music theory. For example, it can provide feedback on melody and harmony based on harmony theory. For instance, it can suggest revisions to a melody created by the user based on harmony theory. It can also provide feedback on rhythm patterns based on rhythm theory. For example, it can suggest revisions to a rhythm pattern created by the user based on rhythm theory. Furthermore, the suggestion function can provide comprehensive feedback based on music theory in general. For example, it can provide advice based on music theory for the entire piece of music created by the user. This enables technical support by providing expert feedback based on music theory.

[0066] The suggestion section can provide new perspectives and ideas when users face creative blocks. For example, it can suggest new melodies or harmonies when users are running out of ideas. For example, it can generate new ideas based on parts of songs created by users. It can also suggest new rhythm patterns when users are experiencing a decline in motivation. For example, it can suggest new variations to rhythm patterns created by users. Furthermore, the suggestion section can provide new perspectives when users are facing creative blocks. For example, it can suggest musical styles or genres that users don't usually use, providing new inspiration. In this way, it can overcome creative blocks by providing new perspectives and ideas when users encounter creative obstacles.

[0067] The analysis unit can estimate the user's emotions and adjust the analysis method for musical preferences based on the estimated emotions. For example, if the user is feeling sad, the emotion engine can estimate that emotion and adjust the analysis method considering the user's tendency to prefer sad songs. The analysis unit can also estimate the user's emotions if the emotion engine is excited and adjust the analysis method considering the user's tendency to prefer energetic songs. Furthermore, if the user is relaxed, the emotion engine can estimate that emotion and adjust the analysis method considering the user's tendency to prefer calming songs. By adjusting the analysis method for musical preferences based on the user's emotions, a more appropriate analysis becomes possible.

[0068] The analysis unit can analyze the structure of a user's past works in detail and extract specific patterns and trends. For example, the analysis unit can analyze the melody lines of a user's past works and extract frequently used phrases. The analysis unit can also analyze the chord progressions of a user's past works and extract frequently used chord progression patterns. Furthermore, the analysis unit can analyze the rhythm patterns of a user's past works and extract frequently used rhythm patterns. In this way, by analyzing the structure of a user's past works in detail, specific patterns and trends can be extracted.

[0069] The analysis unit can monitor the user's current emotional state in real time and reflect it in the analysis results. For example, the analysis unit can monitor the user's emotions in real time while they are composing music, and update the analysis results according to any changes in their emotions. The analysis unit can also detect changes in the user's emotions (e.g., changing the tempo) and reflect them in the analysis results. Furthermore, the analysis unit can detect changes in the user's emotions (e.g., changing the tempo) and reflect them in the analysis results. In addition, the analysis unit can detect changes in the user's emotions (e.g., "fun") and reflect them in the analysis results. This allows the analysis results to reflect the user's current emotional state by monitoring it in real time.

[0070] The analysis unit can estimate the user's emotions and prioritize analysis results based on those estimated emotions. For example, if the user is feeling sad, the emotion engine can estimate that emotion and prioritize displaying analysis results related to sad songs. Similarly, if the user is excited, the emotion engine can estimate that emotion and prioritize displaying analysis results related to energetic songs. Furthermore, if the user is relaxed, the emotion engine can estimate that emotion and prioritize displaying analysis results related to calming songs. By prioritizing analysis results based on the user's emotions, the system can provide more appropriate analysis results.

[0071] The analytics department can take into account users' social media activity when analyzing their musical preferences. For example, it can analyze the music users share on social media and reflect those preferences. It can also analyze the music of artists users follow on social media and reflect those preferences. Furthermore, it can analyze the music users "like" on social media and reflect those preferences. This allows for a more accurate analysis of musical preferences by taking users' social media activity into consideration.

[0072] The analysis unit can take into account the user's geographical location when analyzing their musical preferences. For example, the analysis unit can analyze the musical trends of the area where the user lives and reflect those preferences. The analysis unit can also analyze the music the user listened to while traveling and reflect those preferences. Furthermore, the analysis unit can analyze the music the user listened to when attending a specific event (e.g., a concert) and reflect those preferences. By taking the user's geographical location into account, a more accurate analysis of musical preferences becomes possible.

[0073] The suggestion unit can estimate the user's emotions and adjust the way it expresses the suggested melody and harmony based on those estimated emotions. For example, if the user is feeling sad, the emotion engine can estimate that emotion and suggest a sad melody or harmony. Similarly, if the user is excited, the emotion engine can estimate that emotion and suggest an energetic melody or harmony. Furthermore, if the user is relaxed, the emotion engine can estimate that emotion and suggest a calm melody or harmony. By adjusting the way the melody and harmony are expressed based on the user's emotions, more appropriate suggestions become possible.

[0074] The suggestion function can adjust the level of detail of its suggestions based on the user's musical goals. For example, if the user is a professional composer, the suggestion function can provide detailed melody and harmony suggestions. It can also provide simpler melody and harmony suggestions if the user is an amateur musician. Furthermore, if the user is working on a specific project (e.g., film music), the suggestion function can provide suggestions tailored to that project. This allows for more appropriate suggestions by adjusting the level of detail based on the user's musical goals.

[0075] The suggestion function can apply different suggestion algorithms depending on the user's musical preferences when making suggestions. For example, if the user prefers rock music, the suggestion function can apply a suggestion algorithm specifically for rock. Similarly, if the user prefers classical music, the suggestion function can apply a suggestion algorithm specifically for classical music. Furthermore, if the user prefers electronica, the suggestion function can apply a suggestion algorithm specifically for electronica. This allows for more appropriate suggestions by applying different suggestion algorithms according to the user's musical preferences.

[0076] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on those emotions. For example, if the user is feeling sad, the emotion engine can estimate that emotion and suggest a short melody or harmony. Similarly, if the user is excited, the emotion engine can estimate that emotion and suggest a longer melody or harmony. Furthermore, if the user is relaxed, the emotion engine can estimate that emotion and suggest a melody or harmony of appropriate length. By adjusting the length of the suggestion based on the user's emotions, more appropriate suggestions become possible.

[0077] The proposal team can prioritize proposals based on the submission dates of the user's past works. For example, the proposal team can prioritize analyzing the user's most recently submitted works and make suggestions based on those trends. The proposal team can also prioritize proposals from older works to newer ones, taking into account the submission dates of the user's past works. Furthermore, the proposal team can analyze works submitted by the user during a specific period and make suggestions based on the trends during that period. This allows for more appropriate suggestions by prioritizing proposals based on the submission dates of the user's past works.

[0078] The suggestion function can adjust the order of suggestions based on the relevance of the user's musical preferences. For example, the suggestion function can prioritize suggesting songs in genres the user likes. It can also prioritize suggestions that are similar in style to songs the user has previously created. Furthermore, if the user is influenced by a particular artist, the suggestion function can prioritize suggestions that are similar in style to that artist. By adjusting the order of suggestions based on the relevance of the user's musical preferences, more appropriate suggestions can be made.

[0079] The learning unit can estimate the user's emotions and select training data based on those estimated emotions. For example, if the user is feeling sad, the emotion engine can estimate that emotion and select sad songs as training data. Similarly, if the user is excited, the emotion engine can estimate that emotion and select energetic songs as training data. Furthermore, if the user is relaxed, the emotion engine can estimate that emotion and select calming songs as training data. This allows for more appropriate learning by selecting training data based on the user's emotions.

[0080] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can analyze past learning data and select the most effective learning algorithm. The learning unit can also adjust the parameters of the learning algorithm based on past learning data. For example, the learning unit can adjust the parameters of the learning algorithm based on past learning data. Furthermore, the learning unit can refer to past learning data to identify areas for improvement in the learning algorithm. For example, the learning unit can refer to past learning data to identify areas for improvement in the learning algorithm. In this way, the learning algorithm can be optimized by referring to past learning data.

[0081] The learning unit can reflect changes in the user's musical preferences in real time during training. For example, if the user's musical preferences change, the learning unit can reflect that change in the training data in real time. The learning unit can also add songs from a new genre to the training data if the user becomes interested in that genre. Furthermore, if the user becomes interested in a particular artist, the learning unit can add songs by that artist to the training data. This allows for more appropriate learning by reflecting changes in the user's musical preferences in real time.

[0082] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is feeling sad, the emotion engine can estimate that emotion and set the learning frequency low. The learning unit can also estimate the user's emotions if the emotion engine is excited and set the learning frequency high. Furthermore, if the user is relaxed, the emotion engine can estimate that emotion and perform learning at an appropriate frequency. By adjusting the learning frequency based on the user's emotions, more appropriate learning becomes possible.

[0083] The learning unit can weight the training data based on when the user submitted their musical preferences during training. For example, the learning unit can weight the training data by giving more weight to the user's most recently submitted musical preferences. The learning unit can also consider when the user submitted their musical preferences in the past and give more weight to newer preferences than older ones. Furthermore, the learning unit can analyze the musical preferences the user submitted during a specific period and weight the training data based on the preferences during that period. This allows for more appropriate training by weighting the training data based on when the user submitted their musical preferences.

[0084] The learning unit can perform training while considering the geographical distribution of users' musical preferences. For example, the learning unit can reflect the musical trends of the area where the user lives into the training data. The learning unit can also perform training while considering the geographical distribution of music listened to by users while traveling. For example, the learning unit can perform training while considering the geographical distribution of music listened to by users while traveling. Furthermore, the learning unit can reflect the geographical distribution of music listened to by users at specific events (e.g., concerts) into the training data. For example, the learning unit can reflect the geographical distribution of music listened to by users at specific events (e.g., concerts) into the training data. This allows for more appropriate training by considering the geographical distribution of users' musical preferences.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The suggestion function can adjust the complexity of the melodies and harmonies it suggests based on the user's musical preferences. For example, if the user prefers simple melodies, the suggestion function can suggest simple and easy-to-remember melodies. If the user prefers complex harmonies, the suggestion function can suggest complex and multi-layered harmonies. Furthermore, if the user seeks complexity in a particular genre, it can suggest complex melodies and harmonies specific to that genre. By adjusting the complexity of the suggestions according to the user's musical preferences, more appropriate suggestions can be made.

[0087] The suggestion unit can estimate the user's emotions and adjust the tempo of the suggested music based on those emotions. For example, if the user is relaxed, the suggestion unit can suggest music with a slow tempo. If the user is excited, the suggestion unit can suggest music with a fast tempo. Furthermore, if the user is feeling sad, the suggestion unit can suggest music with a calm tempo. By adjusting the tempo of the music based on the user's emotions, more appropriate suggestions can be made.

[0088] The analytics department can take into account users' life events when analyzing their musical preferences. For example, if a user is getting married, the analytics department can suggest songs suitable for a wedding. Similarly, if a user is celebrating a birthday, the analytics department can suggest songs appropriate for that occasion. Furthermore, if a user is approaching a specific anniversary, the analytics department can suggest songs suitable for that anniversary. This allows for a more accurate analysis of musical preferences by taking users' life events into account.

[0089] The suggestion unit can estimate the user's emotions and adjust the key of the suggested song based on those emotions. For example, if the user is feeling sad, the suggestion unit can suggest a song in a minor key. If the user is feeling happy, the suggestion unit can suggest a song in a major key. Furthermore, if the user is feeling anxious, the suggestion unit can suggest a song in a diminished key. By adjusting the key of the song based on the user's emotions, more appropriate suggestions can be made.

[0090] The suggestion function can adjust the types of instruments it suggests based on the user's musical preferences. For example, if the user prefers the guitar, the suggestion function can suggest songs primarily featuring the guitar. Similarly, if the user prefers the piano, it can suggest songs primarily featuring the piano. Furthermore, if the user prefers electronica, it can suggest songs primarily featuring synthesizers. By adjusting the types of instruments suggested according to the user's musical preferences, more appropriate suggestions can be made.

[0091] The suggestion unit can estimate the user's emotions and adjust the structure of the suggested music based on those emotions. For example, if the user is feeling calm, the suggestion unit can suggest music with a simple structure. If the user is excited, the suggestion unit can suggest music with a complex structure. Furthermore, if the user is feeling sad, the suggestion unit can suggest music with an emotional structure. By adjusting the music structure based on the user's emotions, more appropriate suggestions become possible.

[0092] The analytics department can take into account a user's physical activity when analyzing their musical preferences. For example, if a user is running, the analytics department can suggest music with a tempo suitable for running. If a user is doing yoga, the analytics department can suggest relaxing music. Furthermore, if a user is doing strength training, the analytics department can suggest music that boosts motivation. By taking the user's physical activity into account, a more appropriate analysis of their musical preferences becomes possible.

[0093] The suggestion unit can estimate the user's emotions and adjust the dynamics of the suggested music based on those emotions. For example, if the user is relaxed, the suggestion unit can suggest music with calm dynamics. If the user is excited, the suggestion unit can suggest music with strong dynamics. Furthermore, if the user is feeling sad, the suggestion unit can suggest music with quiet dynamics. By adjusting the dynamics of the music based on the user's emotions, more appropriate suggestions can be made.

[0094] The suggestion function can adjust the length of the suggested songs based on the user's musical preferences. For example, if the user prefers short songs, the suggestion function can suggest short songs. Conversely, if the user prefers long songs, the suggestion function can suggest longer songs. Furthermore, if the user requests a specific song length within a particular genre, the function can suggest song lengths specific to that genre. By adjusting the length of suggested songs according to the user's musical preferences, more appropriate suggestions can be made.

[0095] The suggestion unit can estimate the user's emotions and adjust the rhythm pattern of the suggested music based on those emotions. For example, if the user is relaxed, the suggestion unit can suggest music with a relaxed rhythm pattern. If the user is excited, the suggestion unit can suggest music with a fast rhythm pattern. Furthermore, if the user is feeling sad, the suggestion unit can suggest music with a calm rhythm pattern. By adjusting the rhythm pattern of the music based on the user's emotions, more appropriate suggestions can be made.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The analysis unit analyzes the user's musical preferences, past works, and current emotional state. The user's musical preferences include genre, artist, and musical style, while past works include released songs and unreleased demos. The current emotional state is monitored in real time through emotion recognition technology and user self-reporting. Step 2: The suggestion unit proposes new melodies, harmonies, and rhythm patterns based on the analysis results obtained by the analysis unit. The suggestion unit generates new melodies, harmonies, and rhythm patterns using methods such as music theory-based generation and random generation, and makes suggestions tailored to the user's musical style and goals. The suggestion unit supports a wide range of genres, and users can refine their ideas while interacting with the AI ​​during the composition process. Step 3: The learning unit learns the user's style based on the ideas proposed by the suggestion unit and provides fresh ideas as a collaborator. The learning unit estimates the user's emotions, selects training data based on the estimated emotions, and optimizes the learning algorithm by referring to past training data. It also reflects changes in the user's musical preferences in real time.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Each of the multiple elements described above, including the analysis unit, proposal unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 and analyzes the user's musical preferences, past works, and current emotional state. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes new melodies, harmonies, and rhythm patterns based on the analysis results. The learning unit is implemented by the control unit 46A of the smart device 14 and learns the user's style based on the proposed ideas and provides fresh ideas. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the analysis unit, suggestion unit, and learning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 and analyzes the user's musical preferences, past works, and current emotional state. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12 and suggests new melodies, harmonies, and rhythm patterns based on the analysis results. The learning unit is implemented by the control unit 46A of the smart glasses 214 and learns the user's style based on the suggested ideas and provides fresh ideas. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the analysis unit, suggestion unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 and analyzes the user's musical preferences, past works, and current emotional state. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes new melodies, harmonies, and rhythm patterns based on the analysis results. The learning unit is implemented by the control unit 46A of the headset terminal 314 and learns the user's style based on the suggested ideas and provides fresh ideas. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the analysis unit, proposal unit, and learning unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 and analyzes the user's musical preferences, past works, and current emotional state. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes new melodies, harmonies, and rhythm patterns based on the analysis results. The learning unit is implemented by the control unit 46A of the robot 414 and learns the user's style based on the proposed ideas and provides fresh ideas. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Note 1) The analysis unit analyzes the user's musical preferences, past works, and current emotional state, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes new melodies, harmonies, and rhythmic patterns. The system includes a learning unit that learns the user's style based on the ideas proposed by the aforementioned proposal unit and supplies fresh ideas as a collaborator. A system characterized by the following features. (Note 2) The aforementioned proposal section is, It suggests new melodies and chord progressions tailored to the user's musical style and goals. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, It caters to a wide range of genres, from rock to classical and electronica. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, It is possible to develop ideas while interacting with AI during the composition process. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We provide expert feedback based on music theory. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Providing new perspectives and ideas when facing creative challenges. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis method of musical preferences based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is The structure of the user's past works is analyzed in detail to extract specific patterns and trends. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is The system monitors the user's current emotional state in real time and incorporates this information into the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is When analyzing users' musical preferences, we take their social media activity into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is When analyzing users' musical preferences, we take their geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way it expresses melodies and harmonies based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the user's musical goals. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the user's musical preferences. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When submitting a proposal, we prioritize proposals based on when the user's previous works were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making suggestions, the order of suggestions is adjusted based on the relevance of the user's musical preferences. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned learning unit, During learning, the system reflects changes in the user's musical preferences in real time. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned learning unit, During training, the training data is weighted based on when the user submitted their musical preferences. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned learning unit, During training, the system takes into account the geographical distribution of users' musical preferences. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0170] 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. The analysis unit analyzes the user's musical preferences, past works, and current emotional state, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes new melodies, harmonies, and rhythmic patterns. The system includes a learning unit that learns the user's style based on the ideas proposed by the aforementioned proposal unit and supplies fresh ideas as a collaborator. A system characterized by the following features.

2. The aforementioned proposal section is, It suggests new melodies and chord progressions tailored to the user's musical style and goals. The system according to feature 1.

3. The aforementioned proposal section is, It caters to a wide range of genres, from rock to classical and electronica. The system according to feature 1.

4. The aforementioned proposal section is, It is possible to develop ideas while interacting with AI during the composition process. The system according to feature 1.

5. The aforementioned proposal section is, We provide expert feedback based on music theory. The system according to feature 1.

6. The aforementioned proposal section is, Providing new perspectives and ideas when facing creative challenges. The system according to feature 1.

7. The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis method of musical preferences based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit is The structure of the user's past works is analyzed in detail to extract specific patterns and trends. The system according to feature 1.

9. The aforementioned analysis unit is The system monitors the user's current emotional state in real time and incorporates this information into the analysis results. The system according to feature 1.

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