System

Abstract

An object of the system according to the embodiment is to provide music corresponding to a user's mental state in real time and to improve the efficiency of copyright management.SOLUTION: A system according to an embodiment includes an emotion determination unit, a music generation unit, a reinforcement learning unit, and a copyright determination unit. The emotion determination unit determines a mental state of the user. The music generation part generates music on the basis of the mental state discriminated by the feeling discrimination part. The reinforcement learning unit learns based on data after the music piece generated by the music piece generation unit is provided. The copyright determination unit determines a copyright of the music generated by the music generation unit.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 technology has had the problem that it is difficult to provide music that corresponds to the user's mental state in real time, and copyright management is also complicated.

[0005] The system according to the embodiment aims to provide music that matches the mental state of the user in real time and to improve the efficiency of copyright management. [Means for solving the problem]

[0006] The system according to the embodiment includes an emotion determination unit, a music generation unit, a reinforcement learning unit, and a copyright determination unit. The emotion determination unit determines the mental state of the user. The music generation unit generates music based on the mental state determined by the emotion determination unit. The reinforcement learning unit learns based on data generated by the music generation unit after the music is provided. The copyright determination unit determines the copyright of the music generated by the music generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide music that matches the mental state of the user in real time, and can improve the efficiency of copyright management. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The music providing system according to the embodiment of the present invention is a system that composes and provides music appropriate to the user's mental state in real time, thereby supporting the user's mental health by providing music that matches the user's mental state in real time.

[0029] A music providing system according to an embodiment includes an emotion determination unit, a music generation unit, a reinforcement learning unit, and a copyright determination unit. The emotion determination unit determines a user's mental state. For example, the emotion determination unit uses a camera to capture the user's facial expression in real time and analyzes features such as smiling and wrinkles between the eyebrows. The emotion determination unit also analyzes the tone and tempo of the user's voice using a microphone and measures the heart rate using a heart rate sensor. For example, the emotion determination unit analyzes the frequency of smiling using facial expression recognition technology to determine whether the user is relaxed. The voice tone analysis analyzes the pitch and speed of the voice to determine the user's stress level. The heart rate measurement analyzes heart rate fluctuations to determine the user's mental state. The music generation unit generates music based on the mental state determined by the emotion determination unit. For example, if the user is relaxed, the music generation unit generates music with a gentle melody or a slow rhythm. If the user is feeling stressed, the music generation unit generates an up-tempo music with a refreshing effect. For example, the music generation unit uses a generation AI to generate a relaxing piano piece. For up-tempo music generation, energetic music using drums and guitar is generated. The generation AI generates music using a text generation AI (e.g., LLM) or a multimodal generation AI. The reinforcement learning unit learns based on data after the music is provided. For example, if the user's mental state improves after the music is provided, the reinforcement learning unit learns that data and reflects it in future music generation. Furthermore, if the user's mental state worsens, the reinforcement learning unit adjusts the music generation algorithm based on that data. For example, the reinforcement learning unit collects user feedback and improves the music generation AI algorithm. The feedback includes evaluations of the music's relaxing and stress-reducing effects. The copyright determination unit determines the copyright of the music generated by the music generation unit. For example, the copyright determination unit analyzes the generated music and determines its similarity to existing music. Furthermore, the copyright determination unit references a copyright database to check for copyright infringement. For example, the copyright determination unit analyzes the melody and rhythm of the generated music and calculates the degree of similarity with existing music.The copyright database contains copyright information for existing songs, and the copyright determination unit refers to this information to make determinations. This allows the music distribution system according to the embodiment to provide songs tailored to the user's mental state in real time, improve accuracy through reinforcement learning, and appropriately manage copyrights. For example, the music distribution system supports the user's mental health and provides songs that allow them to relax during stressful work breaks. It also addresses copyright issues and can pay appropriate compensation to legitimate rights holders.

[0030] The emotion discrimination unit can monitor the user's electrodermal activity or changes in body temperature in real time and provide the data to the emotion discrimination AI as additional data. For example, the emotion discrimination unit uses a wearable device to measure the user's electrodermal activity and collects data in real time. This allows for more accurate discrimination of stress or relaxation states. The emotion discrimination unit also uses a body temperature sensor to monitor changes in the user's body temperature in real time and provides the data to the emotion discrimination AI. For example, a rise in body temperature may indicate a state of stress. The emotion discrimination unit also integrates data on electrodermal activity and changes in body temperature and provides the data to the emotion discrimination AI to more accurately discriminate mental states. For example, high electrodermal activity and rising body temperature can be determined as a state of high stress. This allows for more accurate discrimination of mental states by monitoring the user's electrodermal activity and changes in body temperature.

[0031] The emotion determination unit accumulates the user's past mental state data and can make determinations based on long-term changes in the user's mental state. For example, the emotion determination unit accumulates the user's past mental state data in a database and analyzes long-term trends. For example, it determines seasonal changes in the user's mental state based on data from the past year. The emotion determination unit also uses the past mental state data to learn patterns of change in the user's mental state and more accurately determine the user's current mental state. For example, it predicts reactions to specific events or situations. The emotion determination unit also analyzes the user's mental health trends based on the long-term mental state data and suggests preventive approaches. For example, it can provide music with a relaxing effect during periods of increased stress. This makes it possible to more accurately determine the user's mental state by taking past mental state data into consideration.

[0032] When determining the user's mental state, the emotion determination unit can make a more accurate determination based on changes in ambient environmental sounds or lighting. The emotion determination unit, for example, collects ambient environmental sounds using a microphone and provides the data to the emotion determination AI. For example, a noisy environment may be determined as a stressed state. The emotion determination unit also detects changes in lighting using a sensor and provides the data to the emotion determination AI. For example, a dark environment may be determined as a relaxed state. The emotion determination unit also integrates environmental sound and lighting data and provides the data to the emotion determination AI, thereby making a more accurate determination of the user's mental state. For example, a quiet environment with bright lighting may be determined as a relaxed state. This makes it possible to take into account changes in ambient environmental sounds and lighting, enabling a more accurate determination of the user's mental state.

[0033] The emotion discrimination unit can maximize the training effect based on the discrimination of the user's mental state during sports or fitness training. For example, the emotion discrimination unit monitors the user's heart rate and breathing rate in real time during sports or fitness and provides the results to the emotion discrimination AI. For example, if the heart rate is elevated, it determines that the user is in a high-intensity training state. The emotion discrimination unit also analyzes the user's facial expressions and tone of voice during training to determine the user's mental state. For example, if the user appears tired, it suggests taking a break. The emotion discrimination unit also integrates the training data and mental state data to provide music to maximize the training effect. For example, it provides up-tempo music to increase motivation. In this way, the training effect can be maximized by discerning the user's mental state during sports or fitness.

[0034] The music generation unit can input the user's past music preference data and generate personalized music. For example, the music generation unit analyzes the user's past music playback history and identifies their favorite genres and artists. For example, it generates pop-style music for a user who listens to a lot of pop music. The music generation unit also uses the music generation AI to generate personalized music based on the user's music preference data. For example, it provides music with those characteristics to a user who likes a particular tempo or melody line. The music generation unit also accumulates past music preference data and analyzes changes in the user's preferences. For example, for a user whose preferences change with the seasons, it generates music that suits the season. In this way, personalized music can be generated based on past music preference data, allowing the user to be provided with the music that is best suited to them.

[0035] The music generation unit can combine natural or environmental sounds with the music it generates to generate music with a stronger relaxing effect. For example, the music generation unit generates music with a stronger relaxing effect by combining natural sounds (e.g., birds chirping or the sound of waves) with the music generation AI. For example, it provides music with forest sounds as a background. The music generation unit also generates music that helps the user relax by incorporating environmental sounds (e.g., the sound of rain or wind) into the music. For example, it provides a piano piece with the sound of rain as a background. The music generation unit also collects natural or environmental sounds in real time and provides them to the music generation AI to generate music with a stronger relaxing effect that matches the user's environment. For example, if the user is in nature, it provides music that incorporates those environmental sounds. In this way, by combining natural or environmental sounds, it is possible to generate music with a stronger relaxing effect.

[0036] The music generation unit can be applied to automatically generating background music that matches scenes in movies and games. For example, to automatically generate background music that matches a movie scene, the music generation unit analyzes the content and emotions of the scene and generates the optimal music. For example, orchestral music that enhances emotions is provided for moving scenes. In addition, to automatically generate background music that matches a game scene, the music generation unit analyzes the progress of the game and the player's emotions and generates the optimal music. For example, up-tempo music is provided for tense scenes. In addition, the music generation unit generates background music that matches movie or game scenes in real time and dynamically adjusts the music according to changes in the scene. For example, music is switched when transitioning from an action scene to a relaxing scene. In this way, background music that matches a movie or game scene can be automatically generated to provide the optimal music for the scene.

[0037] The music generation unit can be applied to the generation of educational music teaching materials to enhance learning effectiveness. For example, the music generation unit generates music tailored to the learning content and target age group in order to automatically generate educational music teaching materials. For example, it provides music with simple melodies for young children. The music generation unit also incorporates educational elements into the music generated by the music generation AI to enhance learning effectiveness. For example, it generates songs for memorizing the alphabet and numbers. The music generation unit also generates educational music teaching materials in real time and dynamically adjusts the music according to the learning progress. For example, it provides music with increasing difficulty according to the learning progress. In this way, the learning effectiveness can be enhanced by generating educational music teaching materials.

[0038] The reinforcement learning unit can collect user feedback in real time and perform learning based on that data. For example, the reinforcement learning unit collects feedback in real time after users listen to music and performs reinforcement learning based on that data. For example, it collects evaluations of whether a music piece has a relaxing effect. The reinforcement learning unit also adjusts the music generation AI algorithm based on user feedback and reflects this in subsequent music generation. For example, it adjusts the tempo and melody of a music piece based on the feedback. The reinforcement learning unit also analyzes the feedback data collected in real time and incorporates it into the reinforcement learning model. For example, it learns the characteristics of music that receive a lot of positive feedback and uses this in the next music generation. In this way, by collecting user feedback in real time, the accuracy of reinforcement learning can be improved.

[0039] The reinforcement learning unit can introduce a model that takes into account patterns of change in the user's mental state. The reinforcement learning unit, for example, analyzes patterns of change in the user's mental state and adjusts the reinforcement learning algorithm based on the data. For example, it learns patterns of increasing stress and provides appropriate music. The reinforcement learning unit also introduces a model that takes into account patterns of change in the mental state into reinforcement learning to improve the accuracy of music generation. For example, it predicts a change from a relaxed state to a stressed state and provides appropriate music. The reinforcement learning unit also learns long-term patterns of change in the user's mental state and optimizes the reinforcement learning model based on that data. For example, it generates music that takes into account seasonal changes in mental state. In this way, the accuracy of reinforcement learning can be improved by taking into account patterns of change in mental state.

[0040] The reinforcement learning unit can be applied to content generation in other entertainment fields. For example, the reinforcement learning unit uses reinforcement learning to develop a system that automatically generates background music that matches movie scenes. For example, it provides emotionally enhancing music for moving scenes. The reinforcement learning unit also uses reinforcement learning to generate background music that matches game scenes, dynamically adjusting the music according to the player's emotions. For example, it provides up-tempo music for tense scenes. The reinforcement learning unit also applies reinforcement learning to content generation in other entertainment fields (for example, stage performances and events) to provide music that matches the audience's emotions. For example, it generates background music that matches a stage scene in real time. This makes it possible to generate a wide range of content by applying reinforcement learning to other entertainment fields.

[0041] The reinforcement learning unit can be applied to mental health care in the medical field to provide treatment tailored to the patient's mental state. The reinforcement learning unit, for example, uses reinforcement learning to develop a system that provides music therapy tailored to the patient's mental state. For example, it can provide music with a relaxing effect to reduce stress. The reinforcement learning unit also uses reinforcement learning to suggest the optimal treatment based on data on the patient's mental state. For example, it can learn patterns of emotional changes and provide appropriate music therapy. The reinforcement learning unit also applies reinforcement learning to mental health care in the medical field to provide treatment tailored to the patient's emotions in real time. For example, it can dynamically adjust the treatment based on emotion estimation data. This makes it possible to provide the optimal treatment for the patient by applying it to mental health care in the medical field.

[0042] The copyright determination unit can be added with a function that can determine the copyright of not only music, but also video and text. For example, the copyright determination unit adds a video copyright determination function to the copyright determination AI to determine the copyright of generated video content. For example, it analyzes the similarity with existing video. The copyright determination unit can also add a text copyright determination function to determine the copyright of generated text content. For example, it analyzes the similarity with existing text. The copyright determination unit can also add a function to the copyright determination AI that comprehensively determines the copyright of music, video, and text, to determine the copyright of composite content. For example, it can analyze the copyright of content that combines music and video. This makes it possible to determine the copyright of not only music, but also video and text, enabling copyright management of a wide range of content.

[0043] The copyright determination unit can safely manage the determined copyright information using blockchain technology. For example, the copyright determination unit records copyright information determined by copyright determination AI on a blockchain and builds a system for secure management. For example, the copyright information is stored in a form that cannot be tampered with. The copyright determination unit also uses blockchain technology to ensure the traceability of copyright information. For example, it tracks the history of copyright information and identifies the legitimate rights holder. The copyright determination unit also reduces the risk of copyright infringement by recording copyright information on a blockchain. For example, it ensures transparency of copyright information and pays appropriate compensation to legitimate rights holders. In this way, copyright information can be safely managed using blockchain technology.

[0044] The copyright determination unit can be applied to copyright determination in other creative fields. For example, the copyright determination unit applies copyright determination AI to copyright determination in artworks to determine the copyright of created artworks. For example, by analyzing similarities with existing artworks. The copyright determination unit also applies copyright determination AI to copyright determination in design works to determine the copyright of created designs. For example, by analyzing similarities with existing designs. The copyright determination unit also applies copyright determination AI to copyright determination in other creative fields (e.g., photographs and videos) to determine the copyright of created content. For example, by analyzing similarities with existing photographs and videos. This makes it possible to determine the copyright of a wide range of content by applying it to other creative fields.

[0045] The copyright determination unit can be applied to the management of technology license agreements between companies, improving the transparency of the agreements. For example, the copyright determination unit uses copyright determination AI to manage technology license agreements between companies, improving the transparency of the agreements. For example, it analyzes the contents of the license agreement and provides accurate information. The copyright determination unit also manages the history of technology license agreements using copyright determination AI, ensuring the transparency of the agreements. For example, it tracks the change history of the agreement and provides accurate information. The copyright determination unit also uses copyright determination AI to automate the management of technology license agreements between companies, improving the transparency of the agreements. For example, it automatically analyzes and reports the contents of the agreement. As a result, by applying this to the management of technology license agreements between companies, the transparency of the agreements can be improved.

[0046] The reinforcement learning unit can be applied to content generation in other entertainment fields. For example, the reinforcement learning unit uses reinforcement learning to develop a system that automatically generates background music that matches movie scenes. For example, it provides emotionally enhancing music for moving scenes. The reinforcement learning unit also uses reinforcement learning to generate background music that matches game scenes, dynamically adjusting the music according to the player's emotions. For example, it provides up-tempo music for tense scenes. The reinforcement learning unit also applies reinforcement learning to content generation in other entertainment fields (for example, stage performances and events) to provide music that matches the audience's emotions. For example, it generates background music that matches a stage scene in real time. This makes it possible to generate a wide range of content by applying reinforcement learning to other entertainment fields.

[0047] The reinforcement learning unit can be applied to mental health care in the medical field to provide treatment tailored to the patient's mental state. The reinforcement learning unit, for example, uses reinforcement learning to develop a system that provides music therapy tailored to the patient's mental state. For example, it can provide music with a relaxing effect to reduce stress. The reinforcement learning unit also uses reinforcement learning to suggest the optimal treatment based on data on the patient's mental state. For example, it can learn patterns of emotional changes and provide appropriate music therapy. The reinforcement learning unit also applies reinforcement learning to mental health care in the medical field to provide treatment tailored to the patient's emotions in real time. For example, it can dynamically adjust the treatment based on emotion estimation data. This makes it possible to provide the optimal treatment for the patient by applying it to mental health care in the medical field.

[0048] The copyright determination unit can be added with a function that can determine the copyright of not only music, but also video and text. For example, the copyright determination unit adds a video copyright determination function to the copyright determination AI to determine the copyright of generated video content. For example, it analyzes the similarity with existing video. The copyright determination unit can also add a text copyright determination function to determine the copyright of generated text content. For example, it analyzes the similarity with existing text. The copyright determination unit can also add a function to the copyright determination AI that comprehensively determines the copyright of music, video, and text, to determine the copyright of composite content. For example, it can analyze the copyright of content that combines music and video. This makes it possible to determine the copyright of not only music, but also video and text, enabling copyright management of a wide range of content.

[0049] The copyright determination unit can safely manage the determined copyright information using blockchain technology. For example, the copyright determination unit records copyright information determined by copyright determination AI on a blockchain and builds a system for secure management. For example, the copyright information is stored in a form that cannot be tampered with. The copyright determination unit also uses blockchain technology to ensure the traceability of copyright information. For example, it tracks the history of copyright information and identifies the legitimate rights holder. The copyright determination unit also reduces the risk of copyright infringement by recording copyright information on a blockchain. For example, it ensures transparency of copyright information and pays appropriate compensation to legitimate rights holders. In this way, copyright information can be safely managed using blockchain technology.

[0050] The copyright determination unit can be applied to copyright determination in other creative fields. For example, the copyright determination unit applies copyright determination AI to copyright determination in artworks to determine the copyright of created artworks. For example, by analyzing similarities with existing artworks. The copyright determination unit also applies copyright determination AI to copyright determination in design works to determine the copyright of created designs. For example, by analyzing similarities with existing designs. The copyright determination unit also applies copyright determination AI to copyright determination in other creative fields (e.g., photographs and videos) to determine the copyright of created content. For example, by analyzing similarities with existing photographs and videos. This makes it possible to determine the copyright of a wide range of content by applying it to other creative fields.

[0051] The copyright determination unit can be applied to the management of technology license agreements between companies, improving the transparency of the agreements. For example, the copyright determination unit uses copyright determination AI to manage technology license agreements between companies, improving the transparency of the agreements. For example, it analyzes the contents of the license agreement and provides accurate information. The copyright determination unit also manages the history of technology license agreements using copyright determination AI, ensuring the transparency of the agreements. For example, it tracks the change history of the agreement and provides accurate information. The copyright determination unit also uses copyright determination AI to automate the management of technology license agreements between companies, improving the transparency of the agreements. For example, it automatically analyzes and reports the contents of the agreement. As a result, by applying this to the management of technology license agreements between companies, the transparency of the agreements can be improved.

[0052] The reinforcement learning unit can be applied to content generation in other entertainment fields. For example, the reinforcement learning unit uses reinforcement learning to develop a system that automatically generates background music that matches movie scenes. For example, it provides emotionally enhancing music for moving scenes. The reinforcement learning unit also uses reinforcement learning to generate background music that matches game scenes, dynamically adjusting the music according to the player's emotions. For example, it provides up-tempo music for tense scenes. The reinforcement learning unit also applies reinforcement learning to content generation in other entertainment fields (for example, stage performances and events) to provide music that matches the audience's emotions. For example, it generates background music that matches a stage scene in real time. This makes it possible to generate a wide range of content by applying reinforcement learning to other entertainment fields.

[0053] The reinforcement learning unit can be applied to mental health care in the medical field to provide treatment tailored to the patient's mental state. The reinforcement learning unit, for example, uses reinforcement learning to develop a system that provides music therapy tailored to the patient's mental state. For example, it can provide music with a relaxing effect to reduce stress. The reinforcement learning unit also uses reinforcement learning to suggest the optimal treatment based on data on the patient's mental state. For example, it can learn patterns of emotional changes and provide appropriate music therapy. The reinforcement learning unit also applies reinforcement learning to mental health care in the medical field to provide treatment tailored to the patient's emotions in real time. For example, it can dynamically adjust the treatment based on emotion estimation data. This makes it possible to provide the optimal treatment for the patient by applying it to mental health care in the medical field.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The music providing system can also collect user activity data and provide music according to the type of activity. For example, if the user is running, music with a fast tempo can be provided, allowing the user to enjoy exercise in rhythm. If the user is reading, quiet music can be provided to improve concentration. Furthermore, if the user is cooking, music that allows the user to work while relaxing can be provided. This can improve the quality of daily life by providing the most suitable music according to the user's activity.

[0056] The music providing system can also acquire the user's location information and provide music appropriate to the location. For example, if the user is in a park, relaxing music incorporating natural sounds can be provided. If the user is in a cafe, jazz or acoustic music that matches the cafe's atmosphere can be provided. Furthermore, if the user is at home, relaxing music or music that improves concentration can be provided. This allows for a music experience that suits the environment by providing optimal music based on the user's location information.

[0057] The music providing system can also obtain the user's schedule information and provide music that matches the schedule. For example, it can provide music that helps the user relax before a work meeting, thereby relieving tension. It can also provide upbeat music that motivates the user when they exercise. It can also provide calm music with a relaxing effect before the user goes to bed, helping them sleep comfortably. This can improve the quality of daily life by providing the optimal music that matches the user's schedule.

[0058] The music providing system can also acquire the user's health data and provide music according to the user's health condition. For example, if the user is feeling stressed, music with a relaxing effect can be provided to help reduce stress. If the user is feeling tired, music with a refreshing effect can be provided. Furthermore, after the user exercises, calm music with a recovery effect can be provided to help the body recover. In this way, health management can be supported by providing the most suitable music according to the user's health condition.

[0059] The music provision system can also obtain a user's social media data and provide music tailored to the user's interests. For example, if a user is interested in a particular artist or genre, music from that artist or genre can be provided. Also, if a user is interested in a particular event or topic, music related to that event or topic can be provided. Furthermore, based on songs and playlists shared with friends, music based on shared interests can be provided. This allows for a more personalized music experience by providing the most appropriate music tailored to the user's interests.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The emotion determination unit determines the user's mental state. For example, a camera is used to capture the user's facial expressions in real time and analyze features such as smiles and wrinkles between the eyebrows. A microphone is also used to analyze the tone and tempo of the user's voice, and a heart rate sensor is used to measure the heart rate. This allows facial expression recognition technology to analyze the frequency of smiles and determine whether the user is relaxed. Voice tone analysis analyzes the pitch and speed of the voice to determine the user's stress level. Heart rate measurement analyzes heart rate fluctuations to determine the user's mental state. Step 2: The music generation unit generates music based on the mental state determined by the emotion determination unit. For example, if the user is relaxed, it generates music with a gentle melody and a slow rhythm. If the user is feeling stressed, it generates up-tempo music with a refreshing effect. Using the generation AI, it generates relaxing piano music or energetic music using drums and guitar. Step 3: The reinforcement learning unit learns based on data after the song is provided. For example, if the user's mental state improves after the song is provided, that data is learned and reflected in future song generation. If the user's mental state worsens, the music generation algorithm is adjusted based on that data. User feedback is collected and the music generation AI algorithm is improved. The feedback includes evaluations of the song's relaxing and stress-reducing effects. Step 4: The copyright determination unit determines the copyright of the music generated by the music generation unit. For example, it analyzes the generated music and determines its similarity to existing music. It references a copyright database to check for copyright infringement. It analyzes the melody and rhythm of the generated music and calculates the degree of similarity with existing music. The copyright database contains copyright information for existing music, and this is used to make the determination.

[0062] (Example 2) The music providing system according to the embodiment of the present invention is a system that composes and provides music appropriate to the user's mental state in real time, thereby supporting the user's mental health by providing music that matches the user's mental state in real time.

[0063] A music providing system according to an embodiment includes an emotion determination unit, a music generation unit, a reinforcement learning unit, and a copyright determination unit. The emotion determination unit determines a user's mental state. For example, the emotion determination unit uses a camera to capture the user's facial expression in real time and analyzes features such as smiling and wrinkles between the eyebrows. The emotion determination unit also analyzes the tone and tempo of the user's voice using a microphone and measures the heart rate using a heart rate sensor. For example, the emotion determination unit analyzes the frequency of smiling using facial expression recognition technology to determine whether the user is relaxed. The voice tone analysis analyzes the pitch and speed of the voice to determine the user's stress level. The heart rate measurement analyzes heart rate fluctuations to determine the user's mental state. The music generation unit generates music based on the mental state determined by the emotion determination unit. For example, if the user is relaxed, the music generation unit generates music with a gentle melody or a slow rhythm. If the user is feeling stressed, the music generation unit generates an up-tempo music with a refreshing effect. For example, the music generation unit uses a generation AI to generate a relaxing piano piece. For up-tempo music generation, energetic music using drums and guitar is generated. The generation AI generates music using a text generation AI (e.g., LLM) or a multimodal generation AI. The reinforcement learning unit learns based on data after the music is provided. For example, if the user's mental state improves after the music is provided, the reinforcement learning unit learns that data and reflects it in future music generation. Furthermore, if the user's mental state worsens, the reinforcement learning unit adjusts the music generation algorithm based on that data. For example, the reinforcement learning unit collects user feedback and improves the music generation AI algorithm. The feedback includes evaluations of the music's relaxing and stress-reducing effects. The copyright determination unit determines the copyright of the music generated by the music generation unit. For example, the copyright determination unit analyzes the generated music and determines its similarity to existing music. Furthermore, the copyright determination unit references a copyright database to check for copyright infringement. For example, the copyright determination unit analyzes the melody and rhythm of the generated music and calculates the degree of similarity with existing music.The copyright database contains copyright information for existing songs, and the copyright determination unit refers to this information to make determinations. This allows the music distribution system according to the embodiment to provide songs tailored to the user's mental state in real time, improve accuracy through reinforcement learning, and appropriately manage copyrights. For example, the music distribution system supports the user's mental health and provides songs that allow them to relax during stressful work breaks. It also addresses copyright issues and can pay appropriate compensation to legitimate rights holders.

[0064] The emotion discrimination unit can monitor the user's electrodermal activity or changes in body temperature in real time and provide the data to the emotion discrimination AI as additional data. For example, the emotion discrimination unit uses a wearable device to measure the user's electrodermal activity and collects data in real time. This allows for more accurate discrimination of stress or relaxation states. The emotion discrimination unit also uses a body temperature sensor to monitor changes in the user's body temperature in real time and provides the data to the emotion discrimination AI. For example, a rise in body temperature may indicate a state of stress. The emotion discrimination unit also integrates data on electrodermal activity and changes in body temperature and provides the data to the emotion discrimination AI to more accurately discriminate mental states. For example, high electrodermal activity and rising body temperature can be determined as a state of high stress. This allows for more accurate discrimination of mental states by monitoring the user's electrodermal activity and changes in body temperature.

[0065] The emotion determination unit accumulates the user's past mental state data and can make determinations based on long-term changes in the user's mental state. For example, the emotion determination unit accumulates the user's past mental state data in a database and analyzes long-term trends. For example, it determines seasonal changes in the user's mental state based on data from the past year. The emotion determination unit also uses the past mental state data to learn patterns of change in the user's mental state and more accurately determine the user's current mental state. For example, it predicts reactions to specific events or situations. The emotion determination unit also analyzes the user's mental health trends based on the long-term mental state data and suggests preventive approaches. For example, it can provide music with a relaxing effect during periods of increased stress. This makes it possible to more accurately determine the user's mental state by taking past mental state data into consideration.

[0066] The emotion determination unit uses the emotion estimation function to estimate the user's emotion in real time and can determine the mental state based on the estimation result. For example, the emotion determination unit uses the emotion estimation function to analyze the user's facial expression and tone of voice in real time to estimate the emotion. For example, it detects smiling or angry facial expressions and determines the mental state. The emotion determination unit also updates the mental state in real time based on the user's emotion estimation result and provides appropriate music. For example, it provides calm music when the user is relaxed. The emotion determination unit also uses the emotion estimation function to monitor the user's emotional changes in real time and determine the mental state based on the data. For example, it determines that a sudden change in emotion is a stress state. As a result, the emotion estimation function makes it possible to determine the mental state in real time.

[0067] When determining the user's mental state, the emotion determination unit can make a more accurate determination based on changes in ambient environmental sounds or lighting. The emotion determination unit, for example, collects ambient environmental sounds using a microphone and provides the data to the emotion determination AI. For example, a noisy environment may be determined as a stressed state. The emotion determination unit also detects changes in lighting using a sensor and provides the data to the emotion determination AI. For example, a dark environment may be determined as a relaxed state. The emotion determination unit also integrates environmental sound and lighting data and provides the data to the emotion determination AI, thereby making a more accurate determination of the user's mental state. For example, a quiet environment with bright lighting may be determined as a relaxed state. This makes it possible to take into account changes in ambient environmental sounds and lighting, enabling a more accurate determination of the user's mental state.

[0068] The emotion discrimination unit can maximize the training effect based on the discrimination of the user's mental state during sports or fitness training. For example, the emotion discrimination unit monitors the user's heart rate and breathing rate in real time during sports or fitness and provides the results to the emotion discrimination AI. For example, if the heart rate is elevated, it determines that the user is in a high-intensity training state. The emotion discrimination unit also analyzes the user's facial expressions and tone of voice during training to determine the user's mental state. For example, if the user appears tired, it suggests taking a break. The emotion discrimination unit also integrates the training data and mental state data to provide music to maximize the training effect. For example, it provides up-tempo music to increase motivation. In this way, the training effect can be maximized by discerning the user's mental state during sports or fitness.

[0069] The emotion determination unit uses the emotion estimation function to determine the user's emotions toward a specific music genre and can determine the user's mental state based on the estimation results. For example, the emotion determination unit uses the emotion estimation function to analyze the user's emotions when listening to a specific music genre in real time. For example, it determines whether the user feels relaxed when listening to classical music. The emotion determination unit also estimates the user's emotions toward a specific music genre based on the user's music preference data and generates music specialized for that genre. For example, it provides energetic music to a user who likes rock music. The emotion determination unit also accumulates emotional data when the user listens to a specific music genre, and the music generation AI generates optimal music based on that data. For example, it provides jazz-style music to a user who feels relaxed when listening to jazz music. In this way, the emotion determination unit can determine the user's emotions toward a specific music genre and provide the user with optimal music.

[0070] The music generation unit can input the user's past music preference data and generate personalized music. For example, the music generation unit analyzes the user's past music playback history and identifies their favorite genres and artists. For example, it generates pop-style music for a user who listens to a lot of pop music. The music generation unit also uses the music generation AI to generate personalized music based on the user's music preference data. For example, it provides music with those characteristics to a user who likes a particular tempo or melody line. The music generation unit also accumulates past music preference data and analyzes changes in the user's preferences. For example, for a user whose preferences change with the seasons, it generates music that suits the season. In this way, personalized music can be generated based on past music preference data, allowing the user to be provided with the music that is best suited to them.

[0071] The music generation unit can combine natural or environmental sounds with the music it generates to generate music with a stronger relaxing effect. For example, the music generation unit generates music with a stronger relaxing effect by combining natural sounds (e.g., birds chirping or the sound of waves) with the music generation AI. For example, it provides music with forest sounds as a background. The music generation unit also generates music that helps the user relax by incorporating environmental sounds (e.g., the sound of rain or wind) into the music. For example, it provides a piano piece with the sound of rain as a background. The music generation unit also collects natural or environmental sounds in real time and provides them to the music generation AI to generate music with a stronger relaxing effect that matches the user's environment. For example, if the user is in nature, it provides music that incorporates those environmental sounds. In this way, by combining natural or environmental sounds, it is possible to generate music with a stronger relaxing effect.

[0072] The music generation unit can use the emotion estimation function to select an instrument or tone that best suits the user's emotion and generate music based on that. The music generation unit, for example, uses the emotion estimation function to select an instrument that best suits the user's emotion. For example, an acoustic guitar is used to enhance a sense of relaxation. The music generation unit also selects an optimal tone based on the user's emotion and provides it to the music generation AI. For example, a piano with a soft tone is used to reduce stress. The music generation unit also generates music that combines instruments and tones that best suit the user's emotion based on the emotion estimation data. For example, a music that combines a flute and a harp is provided to enhance a sense of relaxation. In this way, optimal music can be generated by selecting an instrument and tone that best suits the user's emotion.

[0073] The music generation unit can be applied to automatically generating background music that matches scenes in movies and games. For example, to automatically generate background music that matches a movie scene, the music generation unit analyzes the content and emotions of the scene and generates the optimal music. For example, orchestral music that enhances emotions is provided for moving scenes. In addition, to automatically generate background music that matches a game scene, the music generation unit analyzes the progress of the game and the player's emotions and generates the optimal music. For example, up-tempo music is provided for tense scenes. In addition, the music generation unit generates background music that matches movie or game scenes in real time and dynamically adjusts the music according to changes in the scene. For example, music is switched when transitioning from an action scene to a relaxing scene. In this way, background music that matches a movie or game scene can be automatically generated to provide the optimal music for the scene.

[0074] The music generation unit can be applied to the generation of educational music teaching materials to enhance learning effectiveness. For example, the music generation unit generates music tailored to the learning content and target age group in order to automatically generate educational music teaching materials. For example, it provides music with simple melodies for young children. The music generation unit also incorporates educational elements into the music generated by the music generation AI to enhance learning effectiveness. For example, it generates songs for memorizing the alphabet and numbers. The music generation unit also generates educational music teaching materials in real time and dynamically adjusts the music according to the learning progress. For example, it provides music with increasing difficulty according to the learning progress. In this way, the learning effectiveness can be enhanced by generating educational music teaching materials.

[0075] The music generation unit can use the emotion estimation function to generate music that is most effective for the user during a specific activity. For example, the music generation unit uses the emotion estimation function to generate music that allows the user to concentrate best while studying. For example, classical music is provided to improve concentration. The music generation unit also generates music that is most effective for exercise based on the user's emotion data. For example, up-tempo music is provided to increase motivation. The music generation unit also generates music that is most effective for the user during a specific activity in real time based on the emotion estimation data. For example, calm music is provided when the user wants to relax. In this way, by generating music that is most effective during a specific activity, the user's performance can be improved.

[0076] The reinforcement learning unit can collect user feedback in real time and perform learning based on that data. For example, the reinforcement learning unit collects feedback in real time after users listen to music and performs reinforcement learning based on that data. For example, it collects evaluations of whether a music piece has a relaxing effect. The reinforcement learning unit also adjusts the music generation AI algorithm based on user feedback and reflects this in subsequent music generation. For example, it adjusts the tempo and melody of a music piece based on the feedback. The reinforcement learning unit also analyzes the feedback data collected in real time and incorporates it into the reinforcement learning model. For example, it learns the characteristics of music that receive a lot of positive feedback and uses this in the next music generation. In this way, by collecting user feedback in real time, the accuracy of reinforcement learning can be improved.

[0077] The reinforcement learning unit can introduce a model that takes into account patterns of change in the user's mental state. The reinforcement learning unit, for example, analyzes patterns of change in the user's mental state and adjusts the reinforcement learning algorithm based on the data. For example, it learns patterns of increasing stress and provides appropriate music. The reinforcement learning unit also introduces a model that takes into account patterns of change in the mental state into reinforcement learning to improve the accuracy of music generation. For example, it predicts a change from a relaxed state to a stressed state and provides appropriate music. The reinforcement learning unit also learns long-term patterns of change in the user's mental state and optimizes the reinforcement learning model based on that data. For example, it generates music that takes into account seasonal changes in mental state. In this way, the accuracy of reinforcement learning can be improved by taking into account patterns of change in mental state.

[0078] The reinforcement learning unit can use the emotion estimation function to monitor changes in the user's emotions after a song is provided, and perform reinforcement learning based on that data. The reinforcement learning unit, for example, uses the emotion estimation function to monitor changes in the user's emotions after a song is provided in real time. For example, it analyzes changes in the sense of relaxation and stress after listening to a song. The reinforcement learning unit also adjusts the reinforcement learning model based on the data on the user's emotions and reflects this in subsequent song generation. For example, it learns the characteristics of songs that frequently cause positive emotional changes. The reinforcement learning unit also incorporates the emotion estimation data into the reinforcement learning algorithm to improve the accuracy of song generation. For example, it learns patterns of emotional changes and provides optimal songs. In this way, the accuracy of reinforcement learning can be improved by monitoring changes in emotions after a song is provided.

[0079] The reinforcement learning unit can be applied to content generation in other entertainment fields. For example, the reinforcement learning unit uses reinforcement learning to develop a system that automatically generates background music that matches movie scenes. For example, it provides emotionally enhancing music for moving scenes. The reinforcement learning unit also uses reinforcement learning to generate background music that matches game scenes, dynamically adjusting the music according to the player's emotions. For example, it provides up-tempo music for tense scenes. The reinforcement learning unit also applies reinforcement learning to content generation in other entertainment fields (for example, stage performances and events) to provide music that matches the audience's emotions. For example, it generates background music that matches a stage scene in real time. This makes it possible to generate a wide range of content by applying reinforcement learning to other entertainment fields.

[0080] The reinforcement learning unit can be applied to mental health care in the medical field to provide treatment tailored to the patient's mental state. The reinforcement learning unit, for example, uses reinforcement learning to develop a system that provides music therapy tailored to the patient's mental state. For example, it can provide music with a relaxing effect to reduce stress. The reinforcement learning unit also uses reinforcement learning to suggest the optimal treatment based on data on the patient's mental state. For example, it can learn patterns of emotional changes and provide appropriate music therapy. The reinforcement learning unit also applies reinforcement learning to mental health care in the medical field to provide treatment tailored to the patient's emotions in real time. For example, it can dynamically adjust the treatment based on emotion estimation data. This makes it possible to provide the optimal treatment for the patient by applying it to mental health care in the medical field.

[0081] The reinforcement learning unit can use the emotion estimation function to generate personalized advertisements based on user emotions. The reinforcement learning unit, for example, uses the emotion estimation function to develop a system that generates personalized advertisements based on user emotions. For example, advertising products with a relaxing effect to a relaxed user. The reinforcement learning unit also uses reinforcement learning to suggest optimal advertisements based on user emotion data. For example, providing advertisements containing positive messages to users with positive emotions. The reinforcement learning unit also incorporates the emotion estimation data into a reinforcement learning algorithm to generate advertisements in real time based on the user's emotions. For example, dynamically adjusting advertisement content in response to changes in emotion. This maximizes the effectiveness of advertisements by generating personalized advertisements based on the user's emotions.

[0082] The copyright determination unit can be added with a function that can determine the copyright of not only music, but also video and text. For example, the copyright determination unit adds a video copyright determination function to the copyright determination AI to determine the copyright of generated video content. For example, it analyzes the similarity with existing video. The copyright determination unit can also add a text copyright determination function to determine the copyright of generated text content. For example, it analyzes the similarity with existing text. The copyright determination unit can also add a function to the copyright determination AI that comprehensively determines the copyright of music, video, and text, to determine the copyright of composite content. For example, it can analyze the copyright of content that combines music and video. This makes it possible to determine the copyright of not only music, but also video and text, enabling copyright management of a wide range of content.

[0083] The copyright determination unit can safely manage the determined copyright information using blockchain technology. For example, the copyright determination unit records copyright information determined by copyright determination AI on a blockchain and builds a system for secure management. For example, the copyright information is stored in a form that cannot be tampered with. The copyright determination unit also uses blockchain technology to ensure the traceability of copyright information. For example, it tracks the history of copyright information and identifies the legitimate rights holder. The copyright determination unit also reduces the risk of copyright infringement by recording copyright information on a blockchain. For example, it ensures transparency of copyright information and pays appropriate compensation to legitimate rights holders. In this way, copyright information can be safely managed using blockchain technology.

[0084] The copyright determination unit uses the emotion estimation function to prioritize the copyright of songs that the user most emotionally empathizes with and can share revenue. The copyright determination unit, for example, uses the emotion estimation function to identify songs that the user most emotionally empathizes with and prioritizes determining the copyright of those songs. For example, it analyzes the copyright of songs with high emotion scores. The copyright determination unit also determines the copyright of songs that are likely to evoke emotional empathy based on the user's emotion data and shares revenue. For example, it prioritizes determining the copyright of songs that evoke positive emotions. The copyright determination unit also uses the emotion estimation data to determine the copyright of songs that the user most emotionally empathizes with in real time and shares revenue. For example, it instantly analyzes copyright information for songs with high emotion scores. As a result, by using the emotion estimation function, it is possible to prioritize the copyright of songs that the user most emotionally empathizes with and share revenue appropriately.

[0085] The copyright determination unit can be applied to copyright determination in other creative fields. For example, the copyright determination unit applies copyright determination AI to copyright determination in artworks to determine the copyright of created artworks. For example, by analyzing similarities with existing artworks. The copyright determination unit also applies copyright determination AI to copyright determination in design works to determine the copyright of created designs. For example, by analyzing similarities with existing designs. The copyright determination unit also applies copyright determination AI to copyright determination in other creative fields (e.g., photographs and videos) to determine the copyright of created content. For example, by analyzing similarities with existing photographs and videos. This makes it possible to determine the copyright of a wide range of content by applying it to other creative fields.

[0086] The copyright determination unit can be applied to the management of technology license agreements between companies, improving the transparency of the agreements. For example, the copyright determination unit uses copyright determination AI to manage technology license agreements between companies, improving the transparency of the agreements. For example, it analyzes the contents of the license agreement and provides accurate information. The copyright determination unit also manages the history of technology license agreements using copyright determination AI, ensuring the transparency of the agreements. For example, it tracks the change history of the agreement and provides accurate information. The copyright determination unit also uses copyright determination AI to automate the management of technology license agreements between companies, improving the transparency of the agreements. For example, it automatically analyzes and reports the contents of the agreement. As a result, by applying this to the management of technology license agreements between companies, the transparency of the agreements can be improved.

[0087] The copyright determination unit uses the emotion estimation function to determine the copyright of content that the user most emotionally empathizes with and can share revenue for that content. The copyright determination unit, for example, uses the emotion estimation function to identify content that the user most emotionally empathizes with and determine the copyright of that content. For example, it analyzes the copyright of content with a high emotion score. The copyright determination unit also determines the copyright of content that is likely to be emotionally empathized with based on the user's emotion data and performs revenue sharing. For example, it prioritizes determining the copyright of content that evokes positive emotions. The copyright determination unit also uses the emotion estimation data to determine the copyright of content that the user most emotionally empathizes with in real time and performs revenue sharing. For example, it instantly analyzes the copyright information of content with a high emotion score. As a result, by using the emotion estimation function, it is possible to determine the copyright of content that the user most emotionally empathizes with and perform appropriate revenue sharing.

[0088] The reinforcement learning unit can be applied to content generation in other entertainment fields. For example, the reinforcement learning unit uses reinforcement learning to develop a system that automatically generates background music that matches movie scenes. For example, it provides emotionally enhancing music for moving scenes. The reinforcement learning unit also uses reinforcement learning to generate background music that matches game scenes, dynamically adjusting the music according to the player's emotions. For example, it provides up-tempo music for tense scenes. The reinforcement learning unit also applies reinforcement learning to content generation in other entertainment fields (for example, stage performances and events) to provide music that matches the audience's emotions. For example, it generates background music that matches a stage scene in real time. This makes it possible to generate a wide range of content by applying reinforcement learning to other entertainment fields.

[0089] The reinforcement learning unit can be applied to mental health care in the medical field to provide treatment tailored to the patient's mental state. The reinforcement learning unit, for example, uses reinforcement learning to develop a system that provides music therapy tailored to the patient's mental state. For example, it can provide music with a relaxing effect to reduce stress. The reinforcement learning unit also uses reinforcement learning to suggest the optimal treatment based on data on the patient's mental state. For example, it can learn patterns of emotional changes and provide appropriate music therapy. The reinforcement learning unit also applies reinforcement learning to mental health care in the medical field to provide treatment tailored to the patient's emotions in real time. For example, it can dynamically adjust the treatment based on emotion estimation data. This makes it possible to provide the optimal treatment for the patient by applying it to mental health care in the medical field.

[0090] The reinforcement learning unit can use the emotion estimation function to apply it to generating personalized advertisements based on user emotions. For example, the reinforcement learning unit uses the emotion estimation function to develop a system that generates personalized advertisements based on user emotions. For example, advertising products with a relaxing effect to a relaxed user. The reinforcement learning unit also uses reinforcement learning to suggest optimal advertisements based on user emotion data. For example, providing advertisements containing positive messages to users with positive emotions. The reinforcement learning unit also incorporates the emotion estimation data into a reinforcement learning algorithm to generate advertisements in real time based on the user's emotions. For example, dynamically adjusting advertisement content in response to changes in emotion. This maximizes the effectiveness of advertisements by generating personalized advertisements based on the user's emotions.

[0091] The copyright determination unit can be added with a function that can determine the copyright of not only music, but also video and text. For example, the copyright determination unit adds a video copyright determination function to the copyright determination AI to determine the copyright of generated video content. For example, it analyzes the similarity with existing video. The copyright determination unit can also add a text copyright determination function to determine the copyright of generated text content. For example, it analyzes the similarity with existing text. The copyright determination unit can also add a function to the copyright determination AI that comprehensively determines the copyright of music, video, and text, to determine the copyright of composite content. For example, it can analyze the copyright of content that combines music and video. This makes it possible to determine the copyright of not only music, but also video and text, enabling copyright management of a wide range of content.

[0092] The copyright determination unit can safely manage the determined copyright information using blockchain technology. For example, the copyright determination unit records copyright information determined by copyright determination AI on a blockchain and builds a system for secure management. For example, the copyright information is stored in a form that cannot be tampered with. The copyright determination unit also uses blockchain technology to ensure the traceability of copyright information. For example, it tracks the history of copyright information and identifies the legitimate rights holder. The copyright determination unit also reduces the risk of copyright infringement by recording copyright information on a blockchain. For example, it ensures transparency of copyright information and pays appropriate compensation to legitimate rights holders. In this way, copyright information can be safely managed using blockchain technology.

[0093] The copyright determination unit uses the emotion estimation function to prioritize the copyright of songs that the user most emotionally empathizes with and can share revenue. The copyright determination unit, for example, uses the emotion estimation function to identify songs that the user most emotionally empathizes with and prioritizes determining the copyright of those songs. For example, it analyzes the copyright of songs with high emotion scores. The copyright determination unit also determines the copyright of songs that are likely to evoke emotional empathy based on the user's emotion data and shares revenue. For example, it prioritizes determining the copyright of songs that evoke positive emotions. The copyright determination unit also uses the emotion estimation data to determine the copyright of songs that the user most emotionally empathizes with in real time and shares revenue. For example, it instantly analyzes copyright information for songs with high emotion scores. As a result, by using the emotion estimation function, it is possible to prioritize the copyright of songs that the user most emotionally empathizes with and share revenue appropriately.

[0094] The copyright determination unit can be applied to copyright determination in other creative fields. For example, the copyright determination unit applies copyright determination AI to copyright determination in artworks to determine the copyright of created artworks. For example, by analyzing similarities with existing artworks. The copyright determination unit also applies copyright determination AI to copyright determination in design works to determine the copyright of created designs. For example, by analyzing similarities with existing designs. The copyright determination unit also applies copyright determination AI to copyright determination in other creative fields (e.g., photographs and videos) to determine the copyright of created content. For example, by analyzing similarities with existing photographs and videos. This makes it possible to determine the copyright of a wide range of content by applying it to other creative fields.

[0095] The copyright determination unit can be applied to the management of technology license agreements between companies, improving the transparency of the agreements. For example, the copyright determination unit uses copyright determination AI to manage technology license agreements between companies, improving the transparency of the agreements. For example, it analyzes the contents of the license agreement and provides accurate information. The copyright determination unit also manages the history of technology license agreements using copyright determination AI, ensuring the transparency of the agreements. For example, it tracks the change history of the agreement and provides accurate information. The copyright determination unit also uses copyright determination AI to automate the management of technology license agreements between companies, improving the transparency of the agreements. For example, it automatically analyzes and reports the contents of the agreement. As a result, by applying this to the management of technology license agreements between companies, the transparency of the agreements can be improved.

[0096] The copyright determination unit uses the emotion estimation function to determine the copyright of content that the user most emotionally empathizes with and can share revenue for that content. The copyright determination unit, for example, uses the emotion estimation function to identify content that the user most emotionally empathizes with and determine the copyright of that content. For example, it analyzes the copyright of content with a high emotion score. The copyright determination unit also determines the copyright of content that is likely to be emotionally empathized with based on the user's emotion data and performs revenue sharing. For example, it prioritizes determining the copyright of content that evokes positive emotions. The copyright determination unit also uses the emotion estimation data to determine the copyright of content that the user most emotionally empathizes with in real time and performs revenue sharing. For example, it instantly analyzes the copyright information of content with a high emotion score. As a result, by using the emotion estimation function, it is possible to determine the copyright of content that the user most emotionally empathizes with and perform appropriate revenue sharing.

[0097] The reinforcement learning unit can be applied to content generation in other entertainment fields. For example, the reinforcement learning unit uses reinforcement learning to develop a system that automatically generates background music that matches movie scenes. For example, it provides emotionally enhancing music for moving scenes. The reinforcement learning unit also uses reinforcement learning to generate background music that matches game scenes, dynamically adjusting the music according to the player's emotions. For example, it provides up-tempo music for tense scenes. The reinforcement learning unit also applies reinforcement learning to content generation in other entertainment fields (for example, stage performances and events) to provide music that matches the audience's emotions. For example, it generates background music that matches a stage scene in real time. This makes it possible to generate a wide range of content by applying reinforcement learning to other entertainment fields.

[0098] The reinforcement learning unit can be applied to mental health care in the medical field to provide treatment tailored to the patient's mental state. The reinforcement learning unit, for example, uses reinforcement learning to develop a system that provides music therapy tailored to the patient's mental state. For example, it can provide music with a relaxing effect to reduce stress. The reinforcement learning unit also uses reinforcement learning to suggest the optimal treatment based on data on the patient's mental state. For example, it can learn patterns of emotional changes and provide appropriate music therapy. The reinforcement learning unit also applies reinforcement learning to mental health care in the medical field to provide treatment tailored to the patient's emotions in real time. For example, it can dynamically adjust the treatment based on emotion estimation data. This makes it possible to provide the optimal treatment for the patient by applying it to mental health care in the medical field.

[0099] The reinforcement learning unit can use the emotion estimation function to apply it to generating personalized advertisements based on user emotions. For example, the reinforcement learning unit uses the emotion estimation function to develop a system that generates personalized advertisements based on user emotions. For example, advertising products with a relaxing effect to a relaxed user. The reinforcement learning unit also uses reinforcement learning to suggest optimal advertisements based on user emotion data. For example, providing advertisements containing positive messages to users with positive emotions. The reinforcement learning unit also incorporates the emotion estimation data into a reinforcement learning algorithm to generate advertisements in real time based on the user's emotions. For example, dynamically adjusting advertisement content in response to changes in emotion. This maximizes the effectiveness of advertisements by generating personalized advertisements based on the user's emotions.

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

[0101] The music providing system can also collect user activity data and provide music according to the type of activity. For example, if the user is running, music with a fast tempo can be provided, allowing the user to enjoy exercise in rhythm. If the user is reading, quiet music can be provided to improve concentration. Furthermore, if the user is cooking, music that allows the user to work while relaxing can be provided. This can improve the quality of daily life by providing the most suitable music according to the user's activity.

[0102] The music providing system can also acquire the user's location information and provide music appropriate to the location. For example, if the user is in a park, relaxing music incorporating natural sounds can be provided. If the user is in a cafe, jazz or acoustic music that matches the cafe's atmosphere can be provided. Furthermore, if the user is at home, relaxing music or music that improves concentration can be provided. This allows for a music experience that suits the environment by providing optimal music based on the user's location information.

[0103] The music providing system can also obtain the user's schedule information and provide music that matches the schedule. For example, it can provide music that helps the user relax before a work meeting, thereby relieving tension. It can also provide upbeat music that motivates the user when they exercise. It can also provide calm music with a relaxing effect before the user goes to bed, helping them sleep comfortably. This can improve the quality of daily life by providing the optimal music that matches the user's schedule.

[0104] The music providing system can also acquire the user's health data and provide music according to the user's health condition. For example, if the user is feeling stressed, music with a relaxing effect can be provided to help reduce stress. If the user is feeling tired, music with a refreshing effect can be provided. Furthermore, after the user exercises, calm music with a recovery effect can be provided to help the body recover. In this way, health management can be supported by providing the most suitable music according to the user's health condition.

[0105] The music provision system can also obtain a user's social media data and provide music tailored to the user's interests. For example, if a user is interested in a particular artist or genre, music from that artist or genre can be provided. Also, if a user is interested in a particular event or topic, music related to that event or topic can be provided. Furthermore, based on songs and playlists shared with friends, music based on shared interests can be provided. This allows for a more personalized music experience by providing the most appropriate music tailored to the user's interests.

[0106] The music providing system can also estimate the user's emotions and provide music that the user has liked to listen to in the past based on the estimated emotions. For example, if the user is relaxed, music that has had a relaxing effect in the past can be provided. If the user is stressed, music that has had a stress-reducing effect in the past can be provided. Furthermore, if the user is happy, music that has brought joy to the user in the past can be provided. This makes it possible to provide a more personalized music experience by providing music that reflects the user's past preferences based on their emotions.

[0107] The music providing system can also estimate the user's emotions and recommend new music that the user may be interested in based on the estimated emotions. For example, if the user is relaxed, music from new artists or genres that have a relaxing effect can be recommended. If the user is feeling stressed, new music that has a stress-reducing effect can be recommended. Furthermore, if the user is happy, new music that brings joy can be recommended. This can enrich the music experience by supporting the discovery of new music based on the user's emotions.

[0108] The music providing system can also provide a function to estimate a user's emotions and share the user's emotions based on the estimated emotions. For example, if a user is relaxed, they can share their emotions with friends and family and listen to the same music. If a user is feeling stressed, they can share their emotions and receive encouraging messages or music. Furthermore, if a user is happy, they can share their emotions and share their joy together. This allows users to share their emotions and promote communication through music experiences.

[0109] The music providing system can also provide a function to estimate the user's emotions, record the user's emotions based on the estimated emotions, and review them later. For example, if the user is relaxed, the music they listened to along with that emotion can be recorded and played back later when they want to relax. If the user is feeling stressed, the music they listened to along with that emotion can be recorded and played back to reduce stress. Furthermore, if the user is happy, the music they listened to along with that emotion can be recorded and the joy can be re-experienced. This allows the user to look back on past music experiences through the recording of the user's emotions and music.

[0110] The music providing system can also estimate the user's emotions and, based on the estimated emotions, suggest actions to improve the user's emotions. For example, if the user is feeling stressed, the system can not only provide relaxing music but also suggest relaxation methods such as deep breathing or meditation. If the user is feeling tired, the system can provide refreshing music and suggest light stretching or a break. Furthermore, if the user is happy, the system can suggest music and activities to maintain that emotion. This makes it possible to provide comprehensive support to improve the user's emotions.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The emotion determination unit determines the user's mental state. For example, a camera is used to capture the user's facial expressions in real time and analyze features such as smiles and wrinkles between the eyebrows. A microphone is also used to analyze the tone and tempo of the user's voice, and a heart rate sensor is used to measure the heart rate. This allows facial expression recognition technology to analyze the frequency of smiles and determine whether the user is relaxed. Voice tone analysis analyzes the pitch and speed of the voice to determine the user's stress level. Heart rate measurement analyzes heart rate fluctuations to determine the user's mental state. Step 2: The music generation unit generates music based on the mental state determined by the emotion determination unit. For example, if the user is relaxed, it generates music with a gentle melody and a slow rhythm. If the user is feeling stressed, it generates up-tempo music with a refreshing effect. Using the generation AI, it generates relaxing piano music or energetic music using drums and guitar. Step 3: The reinforcement learning unit learns based on data after the song is provided. For example, if the user's mental state improves after the song is provided, that data is learned and reflected in future song generation. If the user's mental state worsens, the music generation algorithm is adjusted based on that data. User feedback is collected and the music generation AI algorithm is improved. The feedback includes evaluations of the song's relaxing and stress-reducing effects. Step 4: The copyright determination unit determines the copyright of the music generated by the music generation unit. For example, it analyzes the generated music and determines its similarity to existing music. It references a copyright database to check for copyright infringement. It analyzes the melody and rhythm of the generated music and calculates the degree of similarity with existing music. The copyright database contains copyright information for existing music, and this is used to make the determination.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0115] 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.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

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

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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]

[0180] 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 determination unit that determines the mental state of the user; a music generating unit that generates music based on the mental state determined by the emotion determining unit; a reinforcement learning unit that learns based on data generated by the music generation unit after the music is provided; a copyright determination unit that determines the copyright of the music piece generated by the music piece generation unit. A system characterized by:

2. The emotion determination unit Monitors the user's electrodermal activity or body temperature changes in real time and provides additional data to emotion recognition AI 2. The system of claim 1.

3. The emotion determination unit Accumulates data on the user's past mental state and makes judgments based on long-term changes in mental state 2. The system of claim 1.

4. The emotion determination unit Estimate the user's emotions in real time and determine their mental state based on the estimation results 2. The system of claim 1.

5. The emotion determination unit When determining the user's mental state, the system can make more accurate determinations based on changes in ambient sound or lighting.

2. The system of claim 1.

6. The emotion determination unit Maximize your training effectiveness by identifying your mental state during sports or fitness training 2. The system of claim 1.

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