System

Abstract

An object of a system according to an embodiment is to generate individualized music and lyrics on the basis of a user's emotion and photograph and create a music history according to the life of the user.SOLUTION: A system includes an emotion analysis unit, a lyrics generation unit, a music generation unit, and a music history generation unit. The emotion analysis unit analyzes an emotion or a photograph input by a user. The lyrics generation unit generates lyrics based on the emotion or the photograph analyzed by the emotion analysis unit. The music generation unit generates music that matches the lyrics generated by the lyrics generation unit. A music history generation part generates a music history according to the life of the user on the basis of the plurality of emotions or photographs input by the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to generate personalized music and lyrics based on a user's emotions and photos, making it impossible to create a music history that follows the user's life.

[0005] The system according to the embodiment aims to generate personalized music and lyrics based on the user's emotions and photos, and to create a music history that follows the user's life. [Means for solving the problem]

[0006] The system according to the embodiment includes an emotion analysis unit, a lyrics generation unit, a music generation unit, and a music history generation unit. The emotion analysis unit analyzes emotions or photos input by a user. The lyrics generation unit generates lyrics based on the emotions or photos analyzed by the emotion analysis unit. The music generation unit creates music that matches the lyrics generated by the lyrics generation unit. The music history generation unit creates a music history that follows the user's life based on multiple emotions or photos input by the user. [Effects of the Invention]

[0007] The system according to the embodiment can generate personalized music and lyrics based on the user's emotions and photos, creating a music history that follows the user's life. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The lyrics generation unit enables lyrics to be generated in different languages, thereby supporting international users. The lyrics generation unit, for example, enables lyrics to be generated in different languages, thereby building a system that supports international users. For example, the lyrics generation unit supports multiple languages, such as English, Japanese, and Spanish. The lyrics generation unit also develops a multilingual lyrics generation system and generates lyrics in a language selected by a user. For example, the lyrics generation unit automatically translates lyrics based on the language selected by the user. The lyrics generation unit also generates lyrics in different languages, thereby providing a variety of lyrics for international users. For example, the lyrics generation unit generates lyrics that take into account the culture and expressions of each language. This enables lyrics to be generated in different languages, thereby supporting international users.

[0029] The lyrics generation unit adds a function to emphasize specific keywords or phrases selected by the user. For example, the lyrics generation unit adds a function to emphasize specific keywords or phrases selected by the user and generates lyrics centered around those keywords. For example, the lyrics generation unit repeatedly uses keywords entered by the user. Furthermore, to emphasize specific keywords or phrases, the lyrics generation unit uses expressions that make those parts stand out in the lyrics. For example, the lyrics generation unit uses refrains or emphasized expressions. Furthermore, the lyrics generation unit generates lyrics with related themes or stories based on keywords or phrases selected by the user. For example, the lyrics generation unit incorporates episodes related to the keywords into the lyrics. This allows the addition of a function to emphasize specific keywords or phrases selected by the user.

[0030] The music generation unit references the user's past music generation history to maintain a consistent musical style. The music generation unit, for example, references the user's past music generation history to build a system that maintains a consistent musical style. For example, the music generation unit creates new music based on the style of music previously generated. The music generation unit also analyzes the past music generation history to generate music that reflects the user's preferences and tendencies. For example, the music generation unit uses instruments and tempos that were previously preferred. The music generation unit also develops an algorithm for maintaining a consistent musical style based on the user's past music generation history. For example, the music generation unit extracts features of past music and reflects them in new music. In this way, a consistent musical style can be maintained by referring to the past music generation history.

[0031] The music generation unit incorporates different music theories and composition techniques to create more complex and appealing music. For example, the music generation unit incorporates different music theories and composition techniques to build a system that generates more complex and appealing music. For example, the music generation unit uses jazz improvisation techniques and classical counterpoint. The music generation unit also automatically generates complex music using generative AI that has learned music theory and composition techniques. For example, the music generation unit creates music based on harmonic theory and rhythmic patterns. The music generation unit also combines different music theories and composition techniques to generate a variety of musical styles. For example, the music generation unit creates music using polyrhythms and modal interchange. This makes it possible to incorporate different music theories and composition techniques to create more complex and appealing music.

[0032] The music generation unit provides a customization function that reflects a specific instrument or tempo selected by the user. The music generation unit provides a customization function that reflects, for example, a specific instrument or tempo selected by the user, and generates music based on the settings. For example, the music generation unit creates a piece of music centered around an instrument selected by the user. The music generation unit also uses the customization function to generate music that reflects a tempo or rhythm pattern specified by the user. For example, the music generation unit creates a piece of music that matches a tempo specified by the user. The music generation unit also builds a system that generates music that reflects a specific instrument or tempo based on the user's customization settings. For example, the music generation unit emphasizes the tone of an instrument selected by the user. This makes it possible to provide a customization function that reflects a specific instrument or tempo selected by the user.

[0033] The music generation unit incorporates musical styles from different cultural spheres to create music from a global perspective. For example, the music generation unit builds a system that incorporates musical styles from different cultural spheres to generate music from a global perspective. For example, the music generation unit uses African rhythms and Indian melodies. The music generation unit also automatically generates diverse musical styles using generative AI that has learned musical data from different cultural spheres. For example, the music generation unit incorporates elements of Latin music and Celtic music. The music generation unit also generates music that combines musical theories and instruments from different cultural spheres to create music from a global perspective. For example, the music generation unit creates music that combines Japanese instruments with Western instruments. This makes it possible to incorporate musical styles from different cultural spheres to create music from a global perspective.

[0034] The music history generation unit organizes the user's past input data in chronological order and visualizes changes in emotions. The music history generation unit, for example, organizes the user's past input data in chronological order and builds a system that visualizes changes in emotions. For example, the music history generation unit displays changes in emotions in a graph or chart. The music history generation unit also analyzes the past input data and displays changes in emotions in chronological order. For example, the music history generation unit visualizes changes in emotions over a specific period of time. The music history generation unit also visualizes changes in emotions based on the user's past input data and creates a music history based on that. For example, the music history generation unit arranges songs corresponding to changes in emotions in chronological order. This allows the past input data to be organized in chronological order and changes in emotions to be visualized.

[0035] The music history generation unit highlights important events and occurrences in the user's life and emphasizes the songs corresponding to them. The music history generation unit, for example, builds a system that highlights important events and occurrences in the user's life and emphasizes the songs corresponding to them. For example, the music history generation unit highlights special moments such as weddings and graduation ceremonies. The music history generation unit also automatically generates songs corresponding to important events and occurrences and incorporates them into the music history. For example, the music history generation unit creates theme songs for specific events. The music history generation unit also provides an interface for highlighting important events in the user's life and highlighting the songs corresponding to them. For example, the music history generation unit prominently displays songs related to important events. This makes it possible to highlight important events and occurrences in life and emphasize the songs corresponding to them.

[0036] The music history generation unit provides a function for creating a music history based on different themes or topics. For example, the music history generation unit provides a function for creating a music history based on different themes or topics, and generates music that matches a theme selected by a user. For example, the music history generation unit creates a history based on a theme such as love, friendship, or adventure. The music history generation unit also automatically generates related music based on a topic selected by a user and incorporates it into the music history. For example, the music history generation unit creates music that reflects episodes related to a specific topic. The music history generation unit also provides an interface for creating a music history based on different themes or topics. For example, the music history generation unit allows a user to select a theme and generates music based on that theme. This provides a function for creating a music history based on different themes or topics.

[0037] The music history generation unit adds a function that allows the music history to be shared with other users, thereby gaining sympathy and feedback. The music history generation unit, for example, adds a function that allows the music history to be shared with other users, thereby building a system that gains sympathy and feedback. For example, the music history generation unit shares the music history through social networking sites or messaging apps. The music history generation unit also provides a function that collects comments and ratings from other users for the shared music history. For example, the music history generation unit displays feedback on the shared songs. The music history generation unit also builds a system that encourages sympathy with other users by sharing the music history, and makes improvements based on the feedback. For example, the music history generation unit reflects opinions on the shared songs. This allows the music history to be shared with other users, thereby gaining sympathy and feedback.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The music history generation system may further include a health analysis unit that acquires the user's health data and generates music based on the user's health condition. For example, the health analysis unit may analyze the user's heart rate and sleep data to generate music with a relaxing effect. The health analysis unit may also provide upbeat music to motivate a user who is exercising. Furthermore, the health analysis unit may analyze the user's stress level and generate music that helps reduce stress. This allows the system to provide music tailored to the user's health condition, resulting in a more personalized music experience.

[0040] The music history generation system may further include a hobby analysis unit that generates music based on the user's hobbies and interests. For example, the hobby analysis unit may analyze information about the user's favorite movies, books, sports, etc., and generate music based on that information. The hobby analysis unit may also provide music tailored to events or activities related to the user's hobbies. Furthermore, the hobby analysis unit may create personalized playlists based on the user's hobbies and interests. This allows the user to enjoy a richer music experience by providing music tailored to their hobbies and interests.

[0041] The music history generation system may further include a location information analysis unit that acquires the user's geographical location information and generates music based on the region. For example, the location information analysis unit may analyze the culture and musical style of the user's current location and provide music based on that. The location information analysis unit may also analyze the user's movement history and generate music related to the places the user has visited. Furthermore, the location information analysis unit may provide music tailored to local events or festivals based on the user's location information. This allows the system to provide music based on the user's geographical location information, resulting in a more personalized music experience.

[0042] The music history generation system may further include a lifestyle analysis unit that acquires lifestyle data of the user and generates music based on the user's lifestyle. For example, the lifestyle analysis unit may analyze the user's eating, exercise, and sleep patterns and provide music based on the analysis. The lifestyle analysis unit may also generate music that matches the user's work or hobby time. Furthermore, the lifestyle analysis unit may provide a personalized music experience based on the user's lifestyle data. This allows the system to provide music that matches the user's lifestyle, resulting in a richer music experience.

[0043] The music history generation system may further include a learning analysis unit that acquires the user's learning data and generates music based on the user's learning status. For example, the learning analysis unit may analyze the subjects the user is studying and their progress, and provide music based on that analysis. The learning analysis unit may also generate music to enhance the user's concentration. Furthermore, the learning analysis unit may provide music with a relaxing effect based on the user's learning data. This allows the system to provide music that matches the user's learning status, creating a more effective learning environment.

[0044] The music history generation system may further include a feedback analysis unit that collects user feedback and generates music based on the feedback. For example, the feedback analysis unit may analyze ratings and comments on music provided by users and provide music based on the analysis. The feedback analysis unit may also generate music that reflects the user's preferences and tendencies. Furthermore, the feedback analysis unit may improve the music generation algorithm based on the user's feedback. This allows the system to provide music that reflects the user's feedback, resulting in a more personalized music experience.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The emotion analysis unit analyzes the emotion or photo entered by the user. For example, the emotion analysis unit analyzes the text data entered by the user using natural language processing technology to identify the type and intensity of emotion. The emotion analysis unit can also use image recognition technology to identify objects and scenes in the photo and infer emotion based on that. For example, the emotion analysis unit analyzes the text entered by the user, "I'm feeling very happy today," to identify a positive emotion. The emotion analysis unit also analyzes smiling faces and bright colors in the photo to infer a positive emotion. Step 2: The lyrics generation unit generates lyrics based on the emotions and photos analyzed by the emotion analysis unit. For example, the lyrics generation unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to generate lyrics incorporating poetic expressions and metaphors that match the emotions. The lyrics generation unit can also refer to the user's past input data to create lyrics with a consistent theme or story. For example, the lyrics generation unit generates positive lyrics based on the emotion input by the user, such as "I'm feeling very happy today." The lyrics generation unit also creates lyrics with continuity based on the user's past input data. Step 3: The music generation unit creates music that matches the lyrics generated by the lyrics generation unit. For example, the music generation unit uses the emotion estimation function to select instruments and tones that best suit the user's emotions and generate music. The music generation unit can also refer to the user's past music generation history to maintain a consistent musical style. For example, the music generation unit creates a bright melody that matches positive lyrics. The music generation unit also creates a new song based on the user's past music generation history. Step 4: The music history generation unit creates a music history that aligns with the user's life based on the multiple emotions and photos input by the user. For example, the music history generation unit uses an emotion estimation function to analyze the user's emotional transitions in detail and create a music history that aligns with the flow of emotions. The music history generation unit can also organize the user's past input data in chronological order to visualize emotional changes. For example, the music history generation unit chronologically arranges the user's past input emotions and photos and combines the corresponding songs to create an album. In this way, the music history generation system according to the embodiment records the user's emotions and memories as music, allowing the user to look back on important moments in life. For example, the user can record special moments such as weddings, graduations, and trips as music. Furthermore, by recording the small joys and sorrows of everyday life as music, a richer record of life can be created.

[0047] (Example 2) A music history generation system according to an embodiment of the present invention analyzes emotions and photos input by a user, and a generation AI automatically creates lyrics and music to generate a music history that aligns with the user's life. This allows the music history generation system to record the user's emotions and memories as music and look back on important moments in their life.

[0048] A music history generation system according to an embodiment includes an emotion analysis unit, a lyrics generation unit, a music generation unit, and a music history generation unit. The emotion analysis unit analyzes emotions or photos input by a user. For example, the emotion analysis unit analyzes text data input by a user using natural language processing technology to identify the type and intensity of the emotion. The emotion analysis unit can also use image recognition technology to identify objects and scenes in a photo and estimate the emotion based on the identified objects and scenes. For example, the emotion analysis unit analyzes text input by a user, such as "I'm feeling very happy today," to identify a positive emotion. The emotion analysis unit can also analyze smiling faces and bright colors in a photo to estimate a positive emotion. The lyrics generation unit generates lyrics based on the emotions and photos analyzed by the emotion analysis unit. For example, the lyrics generation unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to generate lyrics incorporating poetic expressions and metaphors that match the emotion. The lyrics generation unit can also refer to the user's past input data to create lyrics with a consistent theme or story. For example, the lyrics generation unit generates positive lyrics based on the user's input emotion, such as "I'm feeling very happy today." The lyrics generation unit also generates lyrics with continuity based on the user's past input data. The music generation unit creates music that matches the lyrics generated by the lyrics generation unit. For example, the music generation unit uses an emotion estimation function to select instruments and tones that best suit the user's emotion and generate music. The music generation unit can also refer to the user's past music generation history to maintain a consistent musical style. For example, the music generation unit creates a cheerful melody that matches positive lyrics. The music generation unit also creates a new song based on the user's past music generation history. The music history generation unit creates a music history that follows the user's life based on multiple emotions and photos input by the user. For example, the music history generation unit uses the emotion estimation function to analyze the user's emotional transitions in detail and create a music history that follows the flow of emotions. The music history generation unit can also organize the user's past input data in chronological order to visualize emotional changes.For example, the music history generation unit chronologically arranges emotions and photos previously input by the user and combines the corresponding songs to create a single album. This allows the music history generation system according to the embodiment to record the user's emotions and memories as music, allowing them to look back on important moments in their lives. For example, users can record special moments such as weddings, graduations, and trips as music. Furthermore, by recording the small joys and sorrows of everyday life as music, users can create a richer record of their lives.

[0049] The emotion analysis unit uses the emotion estimation function to analyze in detail the intensity and type of emotion input by the user and capture subtle changes in emotion. For example, the emotion analysis unit analyzes the emotion input by the user and classifies the intensity and type of emotion in detail. For example, the emotion analysis unit quantifies emotions such as joy, sadness, and surprise and captures subtle changes. The emotion analysis unit also uses the emotion estimation function to analyze the intensity of the emotion input by the user in real time and graph changes in emotion. For example, the emotion analysis unit displays the intensity of emotion along a time axis. The emotion analysis unit also refers to the user's emotion input history and analyzes the relevance to the current emotion. For example, the emotion analysis unit predicts the current emotion based on past emotion data and performs a more accurate analysis. This allows for a detailed analysis of the intensity and type of emotion and captures subtle changes.

[0050] The emotion analysis unit uses image recognition technology to identify objects and scenes in a photo and infer emotions based on the identified objects and scenes. The emotion analysis unit uses, for example, image recognition technology to identify objects and scenes in a photo and infer emotions based on the identified objects and scenes. For example, the emotion analysis unit infers the emotion of joy from a photo of a smiling person. The emotion analysis unit also takes into account factors such as the background and color in analyzing the photo to infer emotions. For example, the emotion analysis unit infers a positive emotion from a photo with bright colors. The emotion analysis unit also analyzes the facial expressions and poses of people in a photo and infers emotions based on the analysis. For example, the emotion analysis unit infers a sad emotion from a crying expression. This makes it possible to identify objects and scenes in a photo and infer emotions based on the identified objects and scenes.

[0051] The emotion analysis unit performs more accurate emotion analysis by referring to the user's past emotion input history and analyzing the relevance to the current emotion. The emotion analysis unit, for example, stores the user's past emotion input history in a database and analyzes the relevance to the current emotion. For example, the emotion analysis unit predicts the current emotion based on the past emotion data. The emotion analysis unit also refers to the past emotion input history and analyzes emotion patterns. For example, the emotion analysis unit analyzes changes in emotions in specific events or situations. The emotion analysis unit also compares the current emotion input with the past emotion history and analyzes the consistency and changes in emotions. For example, the emotion analysis unit evaluates the reliability of the current emotion based on the past emotion data. In this way, more accurate emotion analysis can be performed by referring to the past emotion input history and analyzing the relevance to the current emotion.

[0052] The emotion analysis unit allows the input of voice messages and video clips in addition to emotions and photos, and analyzes the multimedia information. For example, the emotion analysis unit allows the user to input a voice message and analyzes the voice data to infer emotions. For example, the emotion analysis unit analyzes the tone and speed of the voice to determine emotions. The emotion analysis unit also allows the input of video clips and analyzes both video and audio to infer emotions. For example, the emotion analysis unit analyzes emotions by combining facial expressions in the video and the tone of the voice. The emotion analysis unit also analyzes multimedia information in an integrated manner and classifies the intensity and type of emotions in detail. For example, the emotion analysis unit infers emotions by combining information on photos, audio, and video. This allows the input of voice messages and video clips, and allows the analysis of multimedia information.

[0053] The emotion analysis unit analyzes emotional expressions from different cultures and languages ​​to understand emotions from a global perspective. For example, the emotion analysis unit registers emotional expressions from different cultures in a database and uses them for emotion analysis. For example, the emotion analysis unit performs analysis taking into account differences in emotional expressions between cultures. The emotion analysis unit also builds a multilingual emotion analysis system and analyzes emotion input in different languages. For example, the emotion analysis unit analyzes emotional expressions in English, Japanese, Chinese, etc. The emotion analysis unit also collects emotional data from different cultures and uses it for analysis in order to understand emotions from a global perspective. For example, the emotion analysis unit builds an international emotion database. This makes it possible to analyze emotional expressions from different cultures and languages ​​and understand emotions from a global perspective.

[0054] The emotion analysis unit uses the emotion estimation function to provide real-time feedback on emotions input by the user, thereby encouraging the expression of emotions. The emotion analysis unit, for example, builds a system that provides feedback in real time when the user inputs emotions. For example, the emotion analysis unit displays a message according to the intensity and type of emotion. The emotion analysis unit also uses the emotion estimation function to provide advice and suggestions in real time on the user's emotion input. For example, the emotion analysis unit makes suggestions to bring out positive emotions. The emotion analysis unit also provides an interface that encourages the expression of emotions based on real-time feedback on the user's emotion input. For example, the emotion analysis unit provides interactive feedback according to changes in emotions. This makes it possible to provide real-time feedback on emotions and encourage the expression of emotions.

[0055] The lyrics generation unit uses an emotion estimation function to generate lyrics incorporating poetic expressions and metaphors based on the user's emotions. The lyrics generation unit, for example, uses the emotion estimation function to generate lyrics incorporating poetic expressions based on the user's emotions. For example, the lyrics generation unit uses a positive metaphor based on the emotion of joy. The lyrics generation unit also analyzes the user's emotion data and automatically generates poetic expressions that match the emotions. For example, the lyrics generation unit uses a moving expression based on the emotion of sadness. The lyrics generation unit also uses the emotion estimation function to generate lyrics incorporating metaphors and symbols based on the user's emotions. For example, the lyrics generation unit uses a unique metaphor based on the emotion of surprise. This makes it possible to generate lyrics incorporating poetic expressions and metaphors based on emotions.

[0056] The lyrics generation unit references the user's past input data to create lyrics with a consistent theme and story. The lyrics generation unit references, for example, the user's past input data to generate lyrics with a consistent theme and story. For example, the lyrics generation unit creates lyrics with continuity based on past emotional data. The lyrics generation unit also analyzes the past input data to generate lyrics that reflect the user's preferences and tendencies. For example, the lyrics generation unit creates lyrics with a bright theme based on past positive emotional data. The lyrics generation unit also generates lyrics with a consistent story based on the user's past input data. For example, the lyrics generation unit creates lyrics that incorporate past events and emotions. This makes it possible to create lyrics with a consistent theme and story by referring to the past input data.

[0057] The lyrics generation unit incorporates different music genres and styles to provide a variety of lyrics tailored to the user's preferences. For example, the lyrics generation unit incorporates different music genres and styles to generate lyrics tailored to the user's preferences. For example, the lyrics generation unit creates lyrics corresponding to genres such as rock, pop, and classical. The lyrics generation unit also analyzes the user's preferences and generates lyrics that reflect a music style that suits those preferences. For example, the lyrics generation unit creates lyrics that imitate the style of an artist the user likes. The lyrics generation unit also combines different music genres and styles to generate a variety of lyrics. For example, the lyrics generation unit creates unique lyrics that combine jazz and hip hop. This makes it possible to incorporate different music genres and styles to provide a variety of lyrics tailored to the user's preferences.

[0058] The lyrics generation unit enables lyrics to be generated in different languages, thereby supporting international users. The lyrics generation unit, for example, enables lyrics to be generated in different languages, thereby building a system that supports international users. For example, the lyrics generation unit supports multiple languages, such as English, Japanese, and Spanish. The lyrics generation unit also develops a multilingual lyrics generation system and generates lyrics in a language selected by a user. For example, the lyrics generation unit automatically translates lyrics based on the language selected by the user. The lyrics generation unit also generates lyrics in different languages, thereby providing a variety of lyrics for international users. For example, the lyrics generation unit generates lyrics that take into account the culture and expressions of each language. This enables lyrics to be generated in different languages, thereby supporting international users.

[0059] The lyrics generation unit adds a function to emphasize specific keywords or phrases selected by the user. For example, the lyrics generation unit adds a function to emphasize specific keywords or phrases selected by the user and generates lyrics centered around those keywords. For example, the lyrics generation unit repeatedly uses keywords entered by the user. Furthermore, to emphasize specific keywords or phrases, the lyrics generation unit uses expressions that make those parts stand out in the lyrics. For example, the lyrics generation unit uses refrains or emphasized expressions. Furthermore, the lyrics generation unit generates lyrics with related themes or stories based on keywords or phrases selected by the user. For example, the lyrics generation unit incorporates episodes related to the keywords into the lyrics. This allows the addition of a function to emphasize specific keywords or phrases selected by the user.

[0060] The lyrics generation unit provides a function that allows interactive editing of a portion of lyrics based on an emotion input by a user using an emotion estimation function. The lyrics generation unit provides a function that allows interactive editing of a portion of lyrics based on an emotion input by a user using, for example, the emotion estimation function. For example, the lyrics generation unit changes lyrics in real time according to the emotion. Furthermore, when a user inputs an emotion, the lyrics generation unit suggests lyrics based on the emotion and allows the user to edit the suggestions. For example, the lyrics generation unit displays candidate lyrics based on positive emotions. Furthermore, the lyrics generation unit uses the emotion estimation function to automatically generate a portion of lyrics based on the emotion input by the user and provides an interface that allows the user to edit that portion. For example, the lyrics generation unit edits a portion of lyrics based on the emotion by drag and drop. This makes it possible to provide a function that allows interactive editing of a portion of lyrics based on the emotion.

[0061] The music generation unit uses the emotion estimation function to select instruments and tones that best suit the user's emotion and generate music. The music generation unit, for example, uses the emotion estimation function to select instruments and tones that best suit the user's emotion and generate music. For example, the music generation unit uses instruments with bright tones for emotions of joy. The music generation unit also analyzes the user's emotion data and automatically selects instruments and tones that match the emotion. For example, the music generation unit uses instruments with soft tones for emotions of sadness. The music generation unit also uses the emotion estimation function to select instruments and tones based on the user's emotion and builds a system for generating music. For example, the music generation unit automatically generates a combination of instruments according to the emotion. This makes it possible to select instruments and tones that best suit the emotion and generate music.

[0062] The music generation unit references the user's past music generation history to maintain a consistent musical style. The music generation unit, for example, references the user's past music generation history to build a system that maintains a consistent musical style. For example, the music generation unit creates new music based on the style of music previously generated. The music generation unit also analyzes the past music generation history to generate music that reflects the user's preferences and tendencies. For example, the music generation unit uses instruments and tempos that were previously preferred. The music generation unit also develops an algorithm for maintaining a consistent musical style based on the user's past music generation history. For example, the music generation unit extracts features of past music and reflects them in new music. In this way, a consistent musical style can be maintained by referring to the past music generation history.

[0063] The music generation unit incorporates different music theories and composition techniques to create more complex and appealing music. For example, the music generation unit incorporates different music theories and composition techniques to build a system that generates more complex and appealing music. For example, the music generation unit uses jazz improvisation techniques and classical counterpoint. The music generation unit also automatically generates complex music using generative AI that has learned music theory and composition techniques. For example, the music generation unit creates music based on harmonic theory and rhythmic patterns. The music generation unit also combines different music theories and composition techniques to generate a variety of musical styles. For example, the music generation unit creates music using polyrhythms and modal interchange. This makes it possible to incorporate different music theories and composition techniques to create more complex and appealing music.

[0064] The music generation unit provides a customization function that reflects a specific instrument or tempo selected by the user. The music generation unit provides a customization function that reflects, for example, a specific instrument or tempo selected by the user, and generates music based on the settings. For example, the music generation unit creates a piece of music centered around an instrument selected by the user. The music generation unit also uses the customization function to generate music that reflects a tempo or rhythm pattern specified by the user. For example, the music generation unit creates a piece of music that matches a tempo specified by the user. The music generation unit also builds a system that generates music that reflects a specific instrument or tempo based on the user's customization settings. For example, the music generation unit emphasizes the tone of an instrument selected by the user. This makes it possible to provide a customization function that reflects a specific instrument or tempo selected by the user.

[0065] The music generation unit incorporates musical styles from different cultural spheres to create music from a global perspective. For example, the music generation unit builds a system that incorporates musical styles from different cultural spheres to generate music from a global perspective. For example, the music generation unit uses African rhythms and Indian melodies. The music generation unit also automatically generates diverse musical styles using generative AI that has learned musical data from different cultural spheres. For example, the music generation unit incorporates elements of Latin music and Celtic music. The music generation unit also generates music that combines musical theories and instruments from different cultural spheres to create music from a global perspective. For example, the music generation unit creates music that combines Japanese instruments with Western instruments. This makes it possible to incorporate musical styles from different cultural spheres to create music from a global perspective.

[0066] The music generation unit provides a function that enables interactive editing of a portion of music based on an emotion input by a user using an emotion estimation function. The music generation unit, for example, uses the emotion estimation function to provide a function that enables interactive editing of a portion of music based on an emotion input by a user. For example, the music generation unit changes a melody or rhythm in real time according to the emotion. Furthermore, when a user inputs an emotion, the music generation unit makes an editing suggestion for music based on the emotion and allows the user to edit the suggestion. For example, the music generation unit displays melodies based on positive emotions. Furthermore, the music generation unit uses the emotion estimation function to automatically generate a portion of music based on the emotion input by the user and provides an interface that enables the user to edit the portion. For example, the music generation unit edits a rhythm pattern based on the emotion by drag and drop. This provides a function that enables interactive editing of a portion of music based on emotion.

[0067] The music history generation unit uses the emotion estimation function to analyze in detail the transitions in the user's emotions and create a music history that follows the flow of the emotions. For example, the music history generation unit uses the emotion estimation function to analyze in detail the transitions in the user's emotions and create a music history that follows the flow of the emotions. For example, the music history generation unit displays the changes in emotions in chronological order. The music history generation unit also analyzes the user's emotion data and automatically generates a music history based on the flow of the emotions. For example, the music history generation unit combines songs that reflect emotional peaks and valleys. The music history generation unit also uses the emotion estimation function to visualize the transitions in the user's emotions and builds a system that creates a music history based on that. For example, the music history generation unit displays the changes in emotions in a graph and generates songs that correspond to them. This makes it possible to analyze the transitions in emotions in detail and create a music history that follows the flow of the emotions.

[0068] The music history generation unit organizes the user's past input data in chronological order and visualizes changes in emotions. The music history generation unit, for example, organizes the user's past input data in chronological order and builds a system that visualizes changes in emotions. For example, the music history generation unit displays changes in emotions in a graph or chart. The music history generation unit also analyzes the past input data and displays changes in emotions in chronological order. For example, the music history generation unit visualizes changes in emotions over a specific period of time. The music history generation unit also visualizes changes in emotions based on the user's past input data and creates a music history based on that. For example, the music history generation unit arranges songs corresponding to changes in emotions in chronological order. This allows the past input data to be organized in chronological order and changes in emotions to be visualized.

[0069] The music history generation unit highlights important events and occurrences in the user's life and emphasizes the songs corresponding to them. The music history generation unit, for example, builds a system that highlights important events and occurrences in the user's life and emphasizes the songs corresponding to them. For example, the music history generation unit highlights special moments such as weddings and graduation ceremonies. The music history generation unit also automatically generates songs corresponding to important events and occurrences and incorporates them into the music history. For example, the music history generation unit creates theme songs for specific events. The music history generation unit also provides an interface for highlighting important events in the user's life and highlighting the songs corresponding to them. For example, the music history generation unit prominently displays songs related to important events. This makes it possible to highlight important events and occurrences in life and emphasize the songs corresponding to them.

[0070] The music history generation unit provides a function for creating a music history based on different themes or topics. For example, the music history generation unit provides a function for creating a music history based on different themes or topics, and generates music that matches a theme selected by a user. For example, the music history generation unit creates a history based on a theme such as love, friendship, or adventure. The music history generation unit also automatically generates related music based on a topic selected by a user and incorporates it into the music history. For example, the music history generation unit creates music that reflects episodes related to a specific topic. The music history generation unit also provides an interface for creating a music history based on different themes or topics. For example, the music history generation unit allows a user to select a theme and generates music based on that theme. This provides a function for creating a music history based on different themes or topics.

[0071] The music history generation unit adds a function that allows the music history to be shared with other users, thereby gaining sympathy and feedback. The music history generation unit, for example, adds a function that allows the music history to be shared with other users, thereby building a system that gains sympathy and feedback. For example, the music history generation unit shares the music history through social networking sites or messaging apps. The music history generation unit also provides a function that collects comments and ratings from other users for the shared music history. For example, the music history generation unit displays feedback on the shared songs. The music history generation unit also builds a system that encourages sympathy with other users by sharing the music history, and makes improvements based on the feedback. For example, the music history generation unit reflects opinions on the shared songs. This allows the music history to be shared with other users, thereby gaining sympathy and feedback.

[0072] The music history generation unit provides a function that allows a user to interactively edit a portion of the music history based on an emotion input by the user using an emotion estimation function. The music history generation unit provides a function that allows a user to interactively edit a portion of the music history based on an emotion input by the user using, for example, the emotion estimation function. For example, the music history generation unit changes songs in real time according to the emotion. Furthermore, when the user inputs an emotion, the music history generation unit makes an editing suggestion for the music history based on the emotion and allows the user to edit the suggestion. For example, the music history generation unit displays song candidates based on positive emotions. Furthermore, the music history generation unit uses the emotion estimation function to automatically generate a portion of the music history based on the emotion input by the user and provides an interface that allows the user to edit the portion. For example, the music history generation unit edits a portion of the music history based on the emotion by drag and drop. This makes it possible to provide a function that allows a user to interactively edit a portion of the music history based on the emotion.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The music history generation system may further include a health analysis unit that acquires the user's health data and generates music based on the user's health condition. For example, the health analysis unit may analyze the user's heart rate and sleep data to generate music with a relaxing effect. The health analysis unit may also provide upbeat music to motivate a user who is exercising. Furthermore, the health analysis unit may analyze the user's stress level and generate music that helps reduce stress. This allows the system to provide music tailored to the user's health condition, resulting in a more personalized music experience.

[0075] The emotion analysis unit can also analyze users' social media posts to capture changes in their emotions. For example, the emotion analysis unit can analyze text and photos posted by users to identify emotional trends. The emotion analysis unit can also analyze interactions with users' friends and followers to evaluate the emotional impact. Furthermore, the emotion analysis unit can predict changes in a user's emotions based on social media data and provide appropriate music. This enables emotion analysis based on a user's social media activity, resulting in more accurate music generation.

[0076] The emotion analysis unit can also acquire the user's biometric information in real time and capture changes in their emotions. For example, the emotion analysis unit can analyze the user's heart rate and galvanic skin response to identify the intensity of their emotions. The emotion analysis unit can also analyze the user's breathing patterns to estimate their state of relaxation or tension. Furthermore, the emotion analysis unit can provide feedback on changes in emotions in real time based on the user's biometric information and provide appropriate music. This enables emotion analysis based on the user's biometric information, resulting in a more personalized music experience.

[0077] The emotion analysis unit can also analyze the user's voice data to capture changes in emotion. For example, the emotion analysis unit can analyze the tone and pitch of the user's voice to identify the type and intensity of emotion. The emotion analysis unit can also analyze the user's speaking speed and rhythm to estimate changes in emotion. Furthermore, the emotion analysis unit can provide feedback on changes in emotion in real time based on the user's voice data and provide appropriate music. This enables emotion analysis based on the user's voice data, resulting in more accurate music generation.

[0078] The emotion analysis unit can also analyze user behavioral data to capture changes in emotions. For example, the emotion analysis unit can analyze a user's movement patterns and activity levels to identify changes in emotions. The emotion analysis unit can also analyze a user's schedule and task completion status to evaluate the impact of emotions. Furthermore, the emotion analysis unit can predict changes in emotions based on the user's behavioral data and provide appropriate music. This enables emotion analysis based on user behavioral data, resulting in a more personalized music experience.

[0079] The music history generation system may further include a hobby analysis unit that generates music based on the user's hobbies and interests. For example, the hobby analysis unit may analyze information about the user's favorite movies, books, sports, etc., and generate music based on that information. The hobby analysis unit may also provide music tailored to events or activities related to the user's hobbies. Furthermore, the hobby analysis unit may create personalized playlists based on the user's hobbies and interests. This allows the user to enjoy a richer music experience by providing music tailored to their hobbies and interests.

[0080] The music history generation system may further include a location information analysis unit that acquires the user's geographical location information and generates music based on the region. For example, the location information analysis unit may analyze the culture and musical style of the user's current location and provide music based on that. The location information analysis unit may also analyze the user's movement history and generate music related to the places the user has visited. Furthermore, the location information analysis unit may provide music tailored to local events or festivals based on the user's location information. This allows the system to provide music based on the user's geographical location information, resulting in a more personalized music experience.

[0081] The music history generation system may further include a lifestyle analysis unit that acquires lifestyle data of the user and generates music based on the user's lifestyle. For example, the lifestyle analysis unit may analyze the user's eating, exercise, and sleep patterns and provide music based on the analysis. The lifestyle analysis unit may also generate music that matches the user's work or hobby time. Furthermore, the lifestyle analysis unit may provide a personalized music experience based on the user's lifestyle data. This allows the system to provide music that matches the user's lifestyle, resulting in a richer music experience.

[0082] The music history generation system may further include a learning analysis unit that acquires the user's learning data and generates music based on the user's learning status. For example, the learning analysis unit may analyze the subjects the user is studying and their progress, and provide music based on that analysis. The learning analysis unit may also generate music to enhance the user's concentration. Furthermore, the learning analysis unit may provide music with a relaxing effect based on the user's learning data. This allows the system to provide music that matches the user's learning status, creating a more effective learning environment.

[0083] The music history generation system may further include a feedback analysis unit that collects user feedback and generates music based on the feedback. For example, the feedback analysis unit may analyze ratings and comments on music provided by users and provide music based on the analysis. The feedback analysis unit may also generate music that reflects the user's preferences and tendencies. Furthermore, the feedback analysis unit may improve the music generation algorithm based on the user's feedback. This allows the system to provide music that reflects the user's feedback, resulting in a more personalized music experience.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The emotion analysis unit analyzes the emotion or photo entered by the user. For example, the emotion analysis unit analyzes the text data entered by the user using natural language processing technology to identify the type and intensity of emotion. The emotion analysis unit can also use image recognition technology to identify objects and scenes in the photo and infer emotion based on that. For example, the emotion analysis unit analyzes the text entered by the user, "I'm feeling very happy today," to identify a positive emotion. The emotion analysis unit also analyzes smiling faces and bright colors in the photo to infer a positive emotion. Step 2: The lyrics generation unit generates lyrics based on the emotions and photos analyzed by the emotion analysis unit. For example, the lyrics generation unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to generate lyrics incorporating poetic expressions and metaphors that match the emotions. The lyrics generation unit can also refer to the user's past input data to create lyrics with a consistent theme or story. For example, the lyrics generation unit generates positive lyrics based on the emotion input by the user, such as "I'm feeling very happy today." The lyrics generation unit also creates lyrics with continuity based on the user's past input data. Step 3: The music generation unit creates music that matches the lyrics generated by the lyrics generation unit. For example, the music generation unit uses the emotion estimation function to select instruments and tones that best suit the user's emotions and generate music. The music generation unit can also refer to the user's past music generation history to maintain a consistent musical style. For example, the music generation unit creates a bright melody that matches positive lyrics. The music generation unit also creates a new song based on the user's past music generation history. Step 4: The music history generation unit creates a music history that aligns with the user's life based on the multiple emotions and photos input by the user. For example, the music history generation unit uses an emotion estimation function to analyze the user's emotional transitions in detail and create a music history that aligns with the flow of emotions. The music history generation unit can also organize the user's past input data in chronological order to visualize emotional changes. For example, the music history generation unit chronologically arranges the user's past input emotions and photos and combines the corresponding songs to create an album. In this way, the music history generation system according to the embodiment records the user's emotions and memories as music, allowing the user to look back on important moments in life. For example, the user can record special moments such as weddings, graduations, and trips as music. Furthermore, by recording the small joys and sorrows of everyday life as music, a richer record of life can be created.

[0086] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0087] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0088] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0091] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0092] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0093] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0094] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0095] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0096] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0097] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0099] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0100] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 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.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0127] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0136] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0137] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0138] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0139] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0140] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0141] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0142] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0143] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0144] 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.

[0145] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0146] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0147] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0148] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0149] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0150] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0151] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0152] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0153] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an emotion analysis unit that analyzes emotions or photos input by a user; a lyrics generation unit that generates lyrics based on the emotion analyzed by the emotion analysis unit or the photo; a music generation unit that generates music that matches the lyrics generated by the lyrics generation unit; a music history generation unit that creates a music history that matches the user's life based on a plurality of emotions or photos input by the user. A system characterized by:

2. The emotion analysis unit The intensity and type of the emotion input by the user are analyzed in detail to capture subtle changes in the emotion.

2. The system of claim 1.

3. The lyrics generation unit Generate lyrics incorporating poetic expressions and metaphors based on the user's emotions 2. The system of claim 1.

4. The music generation unit Selecting an instrument and tone that best suits the emotion of the user and generating the music.

2. The system of claim 1.

5. The music history generation unit The transition of the user's emotions is analyzed in detail, and the music history is created in accordance with the flow of the emotions.

2. The system of claim 1.

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