system

Abstract

The system according to this embodiment aims to provide music in real time that responds to the user's emotions. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a generation unit, and a provision unit. The acquisition unit acquires voice and body information. The analysis unit analyzes the voice and body information acquired by the acquisition unit and estimates emotions. The generation unit generates music based on the emotions estimated by the analysis unit. The provision unit provides the music generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to provide music in real time according to the user's emotion.

[0005] The system according to an embodiment aims to provide music in real time according to the user's emotion.

Means for Solving the Problems

[0006] The system according to an embodiment includes an acquisition unit, an analysis unit, a generation unit, and a provision unit. The acquisition unit acquires voice / body information. The analysis unit analyzes the voice / body information acquired by the acquisition unit and estimates emotion. The generation unit generates music based on the emotion estimated by the analysis unit. The provision unit provides the music generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide music in real time that responds to the user's emotions. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The emotion analysis music provision system according to an embodiment of the present invention is a system that utilizes voice and physical information data measured by a smartwatch or monitoring camera to perform real-time emotion analysis, and a generating AI provides music that corresponds to the user's current emotions based on the analysis results. This system measures the user's voice and physical information, and the generating AI analyzes this data to analyze the user's emotions in real time. Based on the analysis results, the generating AI generates and provides music that corresponds to the user's current emotions. For example, if the user is feeling sad, the generating AI generates and provides soothing music to the user. This allows the user to relax. Also, if the user is feeling joy or excitement, the generating AI generates and provides music that further energizes the user. This allows the user to improve their motivation. Furthermore, since the generating AI generates music considering the user's preferences and culture, it can provide music that is optimal for the user. This system supports the user's psychological state and is effective in promoting relaxation, concentration, and motivation. For example, if the user wants to improve their concentration while working, the generating AI generates and provides music that enhances concentration. Also, if the user wants to relax, the generating AI generates and provides relaxing music. This allows the user to enjoy music that matches their psychological state. As a result, the emotion analysis music provision system supports the user's psychological state and is effective in promoting relaxation, concentration, and motivation.

[0029] The emotion analysis music provision system according to this embodiment comprises an acquisition unit, an analysis unit, a generation unit, and a provision unit. The acquisition unit acquires the user's voice and physical information. The user's voice and physical information includes, but is not limited to, heart rate, voice tone, and facial expressions. The acquisition unit acquires voice and physical information using, for example, a smartwatch or a monitoring camera. The smartwatch is equipped with a heart rate sensor and an accelerometer, and the monitoring camera is equipped with a face recognition function and a voice recognition function. The analysis unit analyzes the voice and physical information acquired by the acquisition unit and estimates the emotion. The analysis unit estimates the emotion using, for example, a machine learning algorithm or emotion recognition technology. The analysis unit performs data preprocessing, feature extraction, and algorithm application. The generation unit generates music based on the emotion estimated by the analysis unit. The generation unit generates music using, for example, a music generation algorithm or template-based generation. The generation unit can also generate music considering the user's preferences and culture. The provision unit provides the music generated by the generation unit to the user. The service provider delivers music through methods such as streaming, downloading, and real-time playback. The service provider can also estimate the user's emotions and adjust the delivery method based on the estimated emotions. As a result, the emotion-analyzing music delivery system according to this embodiment supports the user's psychological state and is effective in promoting relaxation, concentration, and motivation.

[0030] The acquisition unit can acquire voice and physical information from a smartwatch or a monitoring camera. For example, the acquisition unit can acquire voice and physical information using a smartwatch. Smartwatches are equipped with heart rate sensors and accelerometers, etc., and can measure the user's heart rate and body movements. The acquisition unit can also acquire voice and physical information using a monitoring camera. Monitoring cameras are equipped with facial recognition and voice recognition functions, and can measure the user's facial expressions and voice tone. By utilizing data from smartwatches and monitoring cameras, more accurate voice and physical information can be obtained. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data acquired from a smartwatch or monitoring camera into a generating AI and have the generating AI perform data analysis.

[0031] The analysis unit can analyze acquired voice and body information and estimate emotions. For example, the analysis unit uses machine learning algorithms to analyze voice and body information and estimate emotions. The analysis unit performs data preprocessing, feature extraction, and algorithm application. For example, the analysis unit performs noise reduction as preprocessing of voice data and extracts voice tone and pitch as feature extraction. Next, the analysis unit applies machine learning algorithms to estimate emotions. The analysis unit can also estimate emotions using emotion recognition technology. For example, the analysis unit uses facial recognition technology to analyze facial expressions and estimate emotions. This allows for accurate estimation of the user's emotions by analyzing voice and body information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input acquired voice and body information into a generating AI and have the generating AI perform emotion estimation.

[0032] The generation unit can generate music based on estimated emotions. The generation unit generates music using, for example, a music generation algorithm. The generation unit generates appropriate music based on estimated emotions. For example, if the user is feeling sad, the generation unit generates soothing music. If the user is feeling joy or excitement, the generation unit generates lively music. The generation unit can also generate music using template-based generation. For example, the generation unit generates music that matches the user's emotions based on pre-prepared music templates. This allows the generation unit to support the user's psychological state by generating music that matches their emotions. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input estimated emotions into a generation AI and have the generation AI perform music generation.

[0033] The service provider can provide the generated music to the user. The service provider can provide the music using, for example, streaming. The service provider can provide the generated music to the user in real time. The service provider can also provide the music using downloads. For example, the service provider can allow the user to download the generated music to their device. Furthermore, the service provider can provide the music using real-time playback. For example, the service provider can play the generated music in real time and provide it to the user. By providing the user with generated music, the service provider can support the user's psychological state. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the generated music into a generation AI and have the generation AI execute the method of providing the music.

[0034] The generation unit can generate music while considering the user's preferences or culture. For example, the generation unit can analyze the user's past music playback history to identify the user's preferences. The generation unit generates music based on the user's preferences. For example, the generation unit generates optimal music based on the music genres the user has enjoyed listening to in the past. The generation unit can also generate music while considering the user's culture. For example, the generation unit can consider the user's place of residence, language, religion, etc., and generate music that is appropriate for their culture. By providing music that matches the user's preferences and culture, a more satisfying service can be provided. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input data on the user's preferences and culture into a generation AI and have the generation AI perform music generation.

[0035] The generation unit can generate music based on user prompts. The generation unit is designed to accept prompts from users via text or voice input, for example. The generation unit generates music based on user prompts. For example, if the user prompts "I want you to generate relaxing music," the generation unit will generate relaxing music. Also, if the user prompts "I want you to generate music that helps me concentrate," the generation unit will generate music that helps me concentrate. This allows for more personalized music to be provided by generating music based on user instructions. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user prompts into a generation AI and have the generation AI perform the music generation.

[0036] The data acquisition unit can analyze the user's past voice and physical information and select the optimal data acquisition method. For example, the data acquisition unit can discover from the user's past data that there are large emotional fluctuations during specific time periods and focus on acquiring data during those times. The data acquisition unit analyzes the user's past data and selects the optimal data acquisition method. For example, from the user's past data, it can discover that there are large emotional fluctuations during specific activities and acquire data during those activities. The data acquisition unit can also discover from the user's past data that there are large emotional fluctuations under specific environmental conditions and acquire data under those environmental conditions. In this way, the optimal data acquisition method can be selected by analyzing past data. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's past data into a generating AI and have the generating AI select the optimal data acquisition method.

[0037] The acquisition unit can filter voice and physical information based on the user's current activity and environment. For example, if the user is in a quiet environment, the acquisition unit prioritizes acquiring voice data. The acquisition unit also filters voice and physical information based on the user's current activity and environment. For example, if the user is exercising, the acquisition unit prioritizes acquiring physical information. Furthermore, if the user is in a noisy environment, the acquisition unit can filter voice data using noise cancellation technology. This allows for the acquisition of more accurate information by filtering data based on activity and environment. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data about the user's activity and environment into a generating AI and have the generating AI perform the filtering.

[0038] The acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location when acquiring voice and physical information. For example, if the user is in a park, the acquisition unit will prioritize the acquisition of voice data including ambient sounds. The acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location when acquiring voice and physical information. For example, if the user is in an office, the acquisition unit will prioritize the acquisition of conversational audio. The acquisition unit can also prioritize the acquisition of relaxed ambient sounds if the user is at home. In this way, by considering geographical location information, highly relevant information can be prioritized. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's geographical location information into a generating AI and have the generating AI perform the acquisition of highly relevant information.

[0039] The acquisition unit can analyze the user's social media activity and acquire relevant information when acquiring voice and physical information. For example, if the user posts on social media indicating they are stressed, the acquisition unit will prioritize acquiring physical information such as heart rate and respiratory rate. The acquisition unit analyzes the user's social media activity and acquires relevant information when acquiring voice and physical information. For example, if the user posts on social media indicating they are relaxed, the acquisition unit will prioritize acquiring voice data. The acquisition unit can also prioritize acquiring exercise data if the user posts on social media indicating they are exercising. In this way, relevant information can be acquired by analyzing social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data about the user's social media activity into a generating AI and have the generating AI acquire the relevant information.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of voice and physical information during the analysis. For example, if the heart rate is high, the analysis unit performs a detailed analysis to evaluate the stress level. The analysis unit adjusts the level of detail of the analysis based on the importance of voice and physical information during the analysis. For example, if the respiratory rate is stable, a simple analysis is performed to evaluate the degree of relaxation. The analysis unit can also perform a detailed analysis to evaluate emotional fluctuations if emotional fluctuations are observed in the voice data. This allows for efficient analysis by adjusting the level of detail of the analysis based on importance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the importance of voice and physical information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the category of voice and physical information during analysis. For example, the analysis unit can apply a voice analysis algorithm to voice data to estimate emotions. The analysis unit can apply different analysis algorithms depending on the category of voice and physical information during analysis. For example, it can apply a physiological analysis algorithm to heart rate data to evaluate stress levels. The analysis unit can also apply a respiratory analysis algorithm to respiratory rate data to evaluate relaxation levels. This allows for more accurate analysis by applying analysis algorithms appropriate to the category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data related to the categories of voice and physical information into a generating AI and have the generating AI execute the application of analysis algorithms.

[0042] The analysis unit can determine the priority of analysis based on the timing of acquisition of voice and physical information during analysis. For example, the analysis unit may prioritize the analysis of the most recent data to estimate real-time emotions. The analysis unit can also prioritize the analysis based on the timing of acquisition of voice and physical information during analysis. For example, it may refer to past data to evaluate long-term emotional fluctuations. The analysis unit can also prioritize the analysis of data before and after a specific event to evaluate the impact of the event. This enables efficient analysis by determining priorities based on the acquisition timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data regarding the timing of voice and physical information acquisition into a generating AI and have the generating AI determine the priority of analysis.

[0043] The analysis unit can adjust the order of analysis based on the relationships between voice and physical information during analysis. For example, the analysis unit can analyze heart rate and respiratory rate data in association to evaluate stress levels. The analysis unit can adjust the order of analysis based on the relationships between voice and physical information during analysis. For example, it can analyze voice data and heart rate data in association to evaluate emotional fluctuations. The analysis unit can also analyze respiratory rate and exercise data in association to evaluate relaxation levels. This allows for efficient analysis by adjusting the order of analysis based on relationships. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the relationships between voice and physical information into a generating AI and have the generating AI adjust the order of analysis.

[0044] The generation unit can generate optimal music by analyzing the user's past musical preferences during the generation process. For example, the generation unit can generate optimal music based on the music genres the user has previously enjoyed listening to. The generation unit can generate optimal music by analyzing the user's past musical preferences during the generation process. For example, it can analyze the user's past music playback history and generate similar music. The generation unit can also generate highly-rated music by referring to the user's past music evaluation data. In this way, by analyzing past musical preferences, the generation unit can provide the user with optimal music. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data on the user's past musical preferences into a generation AI and have the generation AI perform music generation.

[0045] The generation unit can adjust the tempo and rhythm of the music based on the user's current activity and environment during generation. For example, if the user is exercising, the generation unit will generate fast-paced music. The generation unit can also adjust the tempo and rhythm of the music based on the user's current activity and environment during generation. For example, if the user is relaxed, it will generate slow-paced music. Furthermore, if the user is concentrating, the generation unit can generate music with a constant rhythm. In this way, by adjusting the tempo and rhythm of the music based on the activity and environment, the system can provide the user with the most suitable music. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input data about the user's activity and environment into the generation AI and have the generation AI perform the adjustment of the music's tempo and rhythm.

[0046] The generation unit can generate music that is appropriate to the user's cultural background, taking into account the user's geographical location information during generation. For example, if the user is in a specific country, the generation unit can generate traditional music from that country. The generation unit can generate music that is appropriate to the user's cultural background, taking into account the user's geographical location information during generation. For example, if the user is in a specific region, the generation unit can generate music that reflects the musical style of that region. Furthermore, if the user is traveling, the generation unit can generate music that is appropriate to the culture of the place they are visiting. In this way, by considering geographical location information, it is possible to provide music that is appropriate to the cultural background. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input data about the user's geographical location information into a generation AI and have the generation AI perform the generation of music that is appropriate to the cultural background.

[0047] The generation unit can analyze a user's social media activity during generation and generate relevant music. For example, the generation unit can generate similar music based on music a user has shared on social media. The generation unit can analyze a user's social media activity during generation and generate relevant music. For example, it can generate music that reflects the musical style of artists a user follows on social media. The generation unit can also generate music related to events a user is participating in on social media. In this way, relevant music can be provided by analyzing social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data about a user's social media activity into a generation AI and have the generation AI generate relevant music.

[0048] The service provider can select the optimal service method by referring to the user's past music usage history at the time of service provision. For example, the service provider may prioritize service methods that the user has previously preferred (such as streaming or downloading). The service provider selects the optimal service method by referring to the user's past music usage history at the time of service provision. For example, it may select a service method optimized for the device the user has previously used (such as a smartphone or tablet). The service provider can also select a service method suitable for a specific time period based on the user's past usage history. In this way, the service provider can select the optimal service method for the user by referring to past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's past music usage history into a generating AI and have the generating AI perform the selection of the service method.

[0049] The service provider can customize the music delivery method based on the user's current device status at the time of delivery. For example, if the user is using a smartphone, the service provider will provide music optimized for mobile data. The service provider customizes the music delivery method based on the user's current device status at the time of delivery. For example, if the user is using a tablet, the service provider will provide music optimized for a large screen. The service provider can also provide concise and highly visible music if the user is using a smartwatch. In this way, by customizing the delivery method based on the device status, the service provider can provide the user with the most suitable music. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data about the user's device status into a generating AI and have the generating AI perform the customization of the delivery method.

[0050] The service provider can select the optimal service delivery method at the time of delivery, taking into account the user's geographical location information. For example, if the user is at home, the service provider can provide high-quality music using Wi-Fi. The service provider selects the optimal service delivery method at the time of delivery, taking into account the user's geographical location information. For example, if the user is out, it can provide music optimized for mobile data. The service provider can also provide music related to a specific location if the user is in that location. In this way, by taking geographical location information into account, the service provider can select the optimal service delivery method for the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data about the user's geographical location information into a generating AI and have the generating AI select the service delivery method.

[0051] The service provider can analyze the user's social media activity and suggest ways to deliver music at the time of delivery. For example, the service provider can provide similar music based on music the user has shared on social media. The service provider can analyze the user's social media activity and suggest ways to deliver music at the time of delivery. For example, it can provide music by artists the user follows on social media. The service provider can also provide music related to events the user is participating in on social media. In this way, by analyzing social media activity, the service provider can provide the user with the most suitable music. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's social media activity into a generating AI and have the generating AI execute suggestions for delivery methods.

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

[0053] The acquisition unit can acquire not only the user's voice and physical information, but also environmental data surrounding the user. For example, the acquisition unit acquires environmental data such as temperature, humidity, and illuminance of the user's location and provides this information to the analysis unit. The analysis unit can analyze the acquired environmental data and use it to estimate the user's emotions. For example, since users may feel more stressed in a high-temperature environment, incorporating environmental data makes it possible to estimate emotions more accurately. Furthermore, the generation unit can also adjust the music generation based on the acquired environmental data. For example, it can generate relaxing music in a high-temperature environment and lively music in a low-temperature environment.

[0054] The analysis unit can refer to the user's past behavioral data in addition to their voice and physical information. For example, the analysis unit can retrieve information about the user's past actions from a database and use it to estimate their current emotions. This allows the unit to learn the user's behavioral patterns and enable more accurate emotion estimation. Furthermore, the analysis unit can use past behavioral data to predict fluctuations in the user's emotions. For example, it can prepare to provide appropriate music in advance to users whose emotions tend to fluctuate during specific times or activities.

[0055] The music generator can consider the user's current health state when generating music based on the user's emotions. For example, if the user is tired, the generator can generate relaxing music, and if the user is energetic, it can generate lively music. Furthermore, the generator can adjust the volume and rhythm of the music according to the user's health state. For example, it can optimize the user experience by lowering the volume when the user is tired and increasing it when the user is energetic. The generator can also select the genre of music based on the user's health state. For example, it can provide calming music when the user is tired and energetic music when the user is energetic.

[0056] The music delivery system can adjust how music is delivered based on the user's emotions. For example, if the user is stressed, the system can deliver music with a simple interface, while if the user is relaxed, it can deliver music with an interface that includes detailed information. The system can also adjust the timing of music delivery according to the user's emotions. For example, if the user is stressed, it can deliver music that helps them relax immediately, while if the user is relaxed, it can deliver music at an appropriate time. Furthermore, the system can customize the means of music delivery based on the user's emotions. For example, if the user is exercising, it can deliver music with voice guidance, while if the user is relaxed, it can deliver music with a visual interface.

[0057] The music delivery system can adjust the frequency of music delivery based on the user's emotions. For example, it can frequently provide relaxing music when the user is stressed and reduce the frequency when the user is relaxed. The system can also personalize the music delivery method according to the user's emotions. For example, it can provide music with a simple interface when the user is stressed and with a more detailed interface when the user is relaxed. Furthermore, the system can adjust the timing of music delivery based on the user's emotions. For example, it can immediately provide relaxing music when the user is stressed and provide music at an appropriate time when the user is relaxed.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The acquisition unit acquires the user's voice and physical information. This information includes heart rate, voice tone, and facial expressions. The acquisition unit uses a smartwatch and a monitoring camera to acquire this voice and physical information. The smartwatch is equipped with a heart rate sensor and an accelerometer, while the monitoring camera is equipped with facial recognition and voice recognition functions. Step 2: The analysis unit analyzes the voice and body information acquired by the acquisition unit and estimates emotions. The analysis unit estimates emotions using machine learning algorithms and emotion recognition technology, and performs data preprocessing, feature extraction, and algorithm application. Step 3: The generation unit generates music based on the emotions estimated by the analysis unit. The generation unit can generate music using music generation algorithms or template-based generation, and can also generate music while taking into account the user's preferences and culture. Step 4: The delivery unit provides the music generated by the generation unit to the user. The delivery unit can provide the music through methods such as streaming, downloading, and real-time playback, and can also estimate the user's emotions and adjust the delivery method based on the estimated emotions.

[0060] (Example of form 2) The emotion analysis music provision system according to an embodiment of the present invention is a system that utilizes voice and physical information data measured by a smartwatch or monitoring camera to perform real-time emotion analysis, and a generating AI provides music that corresponds to the user's current emotions based on the analysis results. This system measures the user's voice and physical information, and the generating AI analyzes this data to analyze the user's emotions in real time. Based on the analysis results, the generating AI generates and provides music that corresponds to the user's current emotions. For example, if the user is feeling sad, the generating AI generates and provides soothing music to the user. This allows the user to relax. Also, if the user is feeling joy or excitement, the generating AI generates and provides music that further energizes the user. This allows the user to improve their motivation. Furthermore, since the generating AI generates music considering the user's preferences and culture, it can provide music that is optimal for the user. This system supports the user's psychological state and is effective in promoting relaxation, concentration, and motivation. For example, if the user wants to improve their concentration while working, the generating AI generates and provides music that enhances concentration. Also, if the user wants to relax, the generating AI generates and provides relaxing music. This allows the user to enjoy music that matches their psychological state. As a result, the emotion analysis music provision system supports the user's psychological state and is effective in promoting relaxation, concentration, and motivation.

[0061] The emotion analysis music provision system according to this embodiment comprises an acquisition unit, an analysis unit, a generation unit, and a provision unit. The acquisition unit acquires the user's voice and physical information. The user's voice and physical information includes, but is not limited to, heart rate, voice tone, and facial expressions. The acquisition unit acquires voice and physical information using, for example, a smartwatch or a monitoring camera. The smartwatch is equipped with a heart rate sensor and an accelerometer, and the monitoring camera is equipped with a face recognition function and a voice recognition function. The analysis unit analyzes the voice and physical information acquired by the acquisition unit and estimates the emotion. The analysis unit estimates the emotion using, for example, a machine learning algorithm or emotion recognition technology. The analysis unit performs data preprocessing, feature extraction, and algorithm application. The generation unit generates music based on the emotion estimated by the analysis unit. The generation unit generates music using, for example, a music generation algorithm or template-based generation. The generation unit can also generate music considering the user's preferences and culture. The provision unit provides the music generated by the generation unit to the user. The service provider delivers music through methods such as streaming, downloading, and real-time playback. The service provider can also estimate the user's emotions and adjust the delivery method based on the estimated emotions. As a result, the emotion-analyzing music delivery system according to this embodiment supports the user's psychological state and is effective in promoting relaxation, concentration, and motivation.

[0062] The acquisition unit can acquire voice and physical information from a smartwatch or a monitoring camera. For example, the acquisition unit can acquire voice and physical information using a smartwatch. Smartwatches are equipped with heart rate sensors and accelerometers, etc., and can measure the user's heart rate and body movements. The acquisition unit can also acquire voice and physical information using a monitoring camera. Monitoring cameras are equipped with facial recognition and voice recognition functions, and can measure the user's facial expressions and voice tone. By utilizing data from smartwatches and monitoring cameras, more accurate voice and physical information can be obtained. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data acquired from a smartwatch or monitoring camera into a generating AI and have the generating AI perform data analysis.

[0063] The analysis unit can analyze acquired voice and body information and estimate emotions. For example, the analysis unit uses machine learning algorithms to analyze voice and body information and estimate emotions. The analysis unit performs data preprocessing, feature extraction, and algorithm application. For example, the analysis unit performs noise reduction as preprocessing of voice data and extracts voice tone and pitch as feature extraction. Next, the analysis unit applies machine learning algorithms to estimate emotions. The analysis unit can also estimate emotions using emotion recognition technology. For example, the analysis unit uses facial recognition technology to analyze facial expressions and estimate emotions. This allows for accurate estimation of the user's emotions by analyzing voice and body information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input acquired voice and body information into a generating AI and have the generating AI perform emotion estimation.

[0064] The generation unit can generate music based on estimated emotions. The generation unit generates music using, for example, a music generation algorithm. The generation unit generates appropriate music based on estimated emotions. For example, if the user is feeling sad, the generation unit generates soothing music. If the user is feeling joy or excitement, the generation unit generates lively music. The generation unit can also generate music using template-based generation. For example, the generation unit generates music that matches the user's emotions based on pre-prepared music templates. This allows the generation unit to support the user's psychological state by generating music that matches their emotions. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input estimated emotions into a generation AI and have the generation AI perform music generation.

[0065] The service provider can provide the generated music to the user. The service provider can provide the music using, for example, streaming. The service provider can provide the generated music to the user in real time. The service provider can also provide the music using downloads. For example, the service provider can allow the user to download the generated music to their device. Furthermore, the service provider can provide the music using real-time playback. For example, the service provider can play the generated music in real time and provide it to the user. By providing the user with generated music, the service provider can support the user's psychological state. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the generated music into a generation AI and have the generation AI execute the method of providing the music.

[0066] The generation unit can generate music while considering the user's preferences or culture. For example, the generation unit can analyze the user's past music playback history to identify the user's preferences. The generation unit generates music based on the user's preferences. For example, the generation unit generates optimal music based on the music genres the user has enjoyed listening to in the past. The generation unit can also generate music while considering the user's culture. For example, the generation unit can consider the user's place of residence, language, religion, etc., and generate music that is appropriate for their culture. By providing music that matches the user's preferences and culture, a more satisfying service can be provided. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input data on the user's preferences and culture into a generation AI and have the generation AI perform music generation.

[0067] The generation unit can generate music based on user prompts. The generation unit is designed to accept prompts from users via text or voice input, for example. The generation unit generates music based on user prompts. For example, if the user prompts "I want you to generate relaxing music," the generation unit will generate relaxing music. Also, if the user prompts "I want you to generate music that helps me concentrate," the generation unit will generate music that helps me concentrate. This allows for more personalized music to be provided by generating music based on user instructions. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user prompts into a generation AI and have the generation AI perform the music generation.

[0068] The data acquisition unit can estimate the user's emotions and adjust the timing of voice and physical information acquisition based on the estimated emotions. For example, if the user is stressed, the data acquisition unit increases the acquisition frequency to collect more detailed data. The data acquisition unit estimates the user's emotions and adjusts the acquisition timing based on the estimated emotions. For example, if the user is relaxed, the acquisition frequency is lowered to reduce the burden of data collection. The data acquisition unit can also collect accurate physical information by acquiring data at the peak of exercise if the user is exercising. This allows for the collection of more accurate data by adjusting the acquisition timing according to the user's emotions. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input data for estimating the user's emotions into a generating AI and have the generating AI perform emotion estimation and adjustment of acquisition timing.

[0069] The data acquisition unit can analyze the user's past voice and physical information and select the optimal data acquisition method. For example, the data acquisition unit can discover from the user's past data that there are large emotional fluctuations during specific time periods and focus on acquiring data during those times. The data acquisition unit analyzes the user's past data and selects the optimal data acquisition method. For example, from the user's past data, it can discover that there are large emotional fluctuations during specific activities and acquire data during those activities. The data acquisition unit can also discover from the user's past data that there are large emotional fluctuations under specific environmental conditions and acquire data under those environmental conditions. In this way, the optimal data acquisition method can be selected by analyzing past data. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's past data into a generating AI and have the generating AI select the optimal data acquisition method.

[0070] The acquisition unit can filter voice and physical information based on the user's current activity and environment. For example, if the user is in a quiet environment, the acquisition unit prioritizes acquiring voice data. The acquisition unit also filters voice and physical information based on the user's current activity and environment. For example, if the user is exercising, the acquisition unit prioritizes acquiring physical information. Furthermore, if the user is in a noisy environment, the acquisition unit can filter voice data using noise cancellation technology. This allows for the acquisition of more accurate information by filtering data based on activity and environment. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data about the user's activity and environment into a generating AI and have the generating AI perform the filtering.

[0071] The data acquisition unit can estimate the user's emotions and determine the priority of audio and physical information to acquire based on the estimated emotions. For example, if the user is stressed, the data acquisition unit will prioritize acquiring physical information such as heart rate and respiratory rate. The data acquisition unit estimates the user's emotions and determines the priority of audio and physical information to acquire based on the estimated emotions. For example, if the user is relaxed, it will prioritize acquiring audio data. The data acquisition unit can also prioritize acquiring exercise data if the user is exercising. In this way, important information can be acquired preferentially by determining priorities based on emotions. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input data for estimating the user's emotions into a generating AI and have the generating AI perform emotion estimation and priority determination.

[0072] The acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location when acquiring voice and physical information. For example, if the user is in a park, the acquisition unit will prioritize the acquisition of voice data including ambient sounds. The acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location when acquiring voice and physical information. For example, if the user is in an office, the acquisition unit will prioritize the acquisition of conversational audio. The acquisition unit can also prioritize the acquisition of relaxed ambient sounds if the user is at home. In this way, by considering geographical location information, highly relevant information can be prioritized. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's geographical location information into a generating AI and have the generating AI perform the acquisition of highly relevant information.

[0073] The acquisition unit can analyze the user's social media activity and acquire relevant information when acquiring voice and physical information. For example, if the user posts on social media indicating they are stressed, the acquisition unit will prioritize acquiring physical information such as heart rate and respiratory rate. The acquisition unit analyzes the user's social media activity and acquires relevant information when acquiring voice and physical information. For example, if the user posts on social media indicating they are relaxed, the acquisition unit will prioritize acquiring voice data. The acquisition unit can also prioritize acquiring exercise data if the user posts on social media indicating they are exercising. In this way, relevant information can be acquired by analyzing social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data about the user's social media activity into a generating AI and have the generating AI acquire the relevant information.

[0074] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit applies an algorithm that emphasizes stress-related data. The analysis unit estimates the user's emotions and adjusts the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, it applies an algorithm that emphasizes relaxation-related data. The analysis unit can also apply an algorithm that emphasizes exercise-related data if the user is exercising. By adjusting the analysis algorithm based on emotions, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for estimating the user's emotions into a generating AI and have the generating AI perform emotion estimation and adjustment of the analysis algorithm.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of voice and physical information during the analysis. For example, if the heart rate is high, the analysis unit performs a detailed analysis to evaluate the stress level. The analysis unit adjusts the level of detail of the analysis based on the importance of voice and physical information during the analysis. For example, if the respiratory rate is stable, a simple analysis is performed to evaluate the degree of relaxation. The analysis unit can also perform a detailed analysis to evaluate emotional fluctuations if emotional fluctuations are observed in the voice data. This allows for efficient analysis by adjusting the level of detail of the analysis based on importance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the importance of voice and physical information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0076] The analysis unit can apply different analysis algorithms depending on the category of voice and physical information during analysis. For example, the analysis unit can apply a voice analysis algorithm to voice data to estimate emotions. The analysis unit can apply different analysis algorithms depending on the category of voice and physical information during analysis. For example, it can apply a physiological analysis algorithm to heart rate data to evaluate stress levels. The analysis unit can also apply a respiratory analysis algorithm to respiratory rate data to evaluate relaxation levels. This allows for more accurate analysis by applying analysis algorithms appropriate to the category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data related to the categories of voice and physical information into a generating AI and have the generating AI execute the application of analysis algorithms.

[0077] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a simple and highly visible display method. The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, if the user is relaxed, it provides a display method that includes detailed information. The analysis unit can also provide a concise display method if the user is exercising. By adjusting the display method based on emotions, it becomes possible to provide a display that is easy for the user to understand. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's emotions into a generating AI and have the generating AI perform the adjustment of the display method.

[0078] The analysis unit can determine the priority of analysis based on the timing of acquisition of voice and physical information during analysis. For example, the analysis unit may prioritize the analysis of the most recent data to estimate real-time emotions. The analysis unit can also prioritize the analysis based on the timing of acquisition of voice and physical information during analysis. For example, it may refer to past data to evaluate long-term emotional fluctuations. The analysis unit can also prioritize the analysis of data before and after a specific event to evaluate the impact of the event. This enables efficient analysis by determining priorities based on the acquisition timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data regarding the timing of voice and physical information acquisition into a generating AI and have the generating AI determine the priority of analysis.

[0079] The analysis unit can adjust the order of analysis based on the relationships between voice and physical information during analysis. For example, the analysis unit can analyze heart rate and respiratory rate data in association to evaluate stress levels. The analysis unit can adjust the order of analysis based on the relationships between voice and physical information during analysis. For example, it can analyze voice data and heart rate data in association to evaluate emotional fluctuations. The analysis unit can also analyze respiratory rate and exercise data in association to evaluate relaxation levels. This allows for efficient analysis by adjusting the order of analysis based on relationships. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the relationships between voice and physical information into a generating AI and have the generating AI adjust the order of analysis.

[0080] The generation unit can estimate the user's emotions and adjust the genre of music it generates based on those emotions. For example, if the user is feeling sad, the generation unit will generate a calming music genre. The generation unit can estimate the user's emotions and adjust the genre of music it generates based on those emotions. For example, if the user is feeling joy or excitement, it will generate an upbeat music genre. The generation unit can also generate a relaxing music genre if the user is relaxed. In this way, by adjusting the music genre based on emotions, the system can provide the user with the most suitable music. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data about the user's emotions into a generation AI and have the generation AI perform the adjustment of the music genre.

[0081] The generation unit can generate optimal music by analyzing the user's past musical preferences during the generation process. For example, the generation unit can generate optimal music based on the music genres the user has previously enjoyed listening to. The generation unit can generate optimal music by analyzing the user's past musical preferences during the generation process. For example, it can analyze the user's past music playback history and generate similar music. The generation unit can also generate highly-rated music by referring to the user's past music evaluation data. In this way, by analyzing past musical preferences, the generation unit can provide the user with optimal music. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data on the user's past musical preferences into a generation AI and have the generation AI perform music generation.

[0082] The generation unit can adjust the tempo and rhythm of the music based on the user's current activity and environment during generation. For example, if the user is exercising, the generation unit will generate fast-paced music. The generation unit can also adjust the tempo and rhythm of the music based on the user's current activity and environment during generation. For example, if the user is relaxed, it will generate slow-paced music. Furthermore, if the user is concentrating, the generation unit can generate music with a constant rhythm. In this way, by adjusting the tempo and rhythm of the music based on the activity and environment, the system can provide the user with the most suitable music. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input data about the user's activity and environment into the generation AI and have the generation AI perform the adjustment of the music's tempo and rhythm.

[0083] The generation unit can estimate the user's emotions and adjust the length of the music it generates based on those emotions. For example, if the user wants to relax for a short time, the generation unit will generate short music. The generation unit can estimate the user's emotions and adjust the length of the music it generates based on those emotions. For example, if the user wants to concentrate for a long time, it will generate long music. The generation unit can also generate music that matches the duration of the exercise if the user is exercising. In this way, by adjusting the length of the music based on emotions, the generation unit can provide the user with the most suitable music. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data about the user's emotions into a generation AI and have the generation AI adjust the length of the music.

[0084] The generation unit can generate music that is appropriate to the user's cultural background, taking into account the user's geographical location information during generation. For example, if the user is in a specific country, the generation unit can generate traditional music from that country. The generation unit can generate music that is appropriate to the user's cultural background, taking into account the user's geographical location information during generation. For example, if the user is in a specific region, the generation unit can generate music that reflects the musical style of that region. Furthermore, if the user is traveling, the generation unit can generate music that is appropriate to the culture of the place they are visiting. In this way, by considering geographical location information, it is possible to provide music that is appropriate to the cultural background. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input data about the user's geographical location information into a generation AI and have the generation AI perform the generation of music that is appropriate to the cultural background.

[0085] The generation unit can analyze a user's social media activity during generation and generate relevant music. For example, the generation unit can generate similar music based on music a user has shared on social media. The generation unit can analyze a user's social media activity during generation and generate relevant music. For example, it can generate music that reflects the musical style of artists a user follows on social media. The generation unit can also generate music related to events a user is participating in on social media. In this way, relevant music can be provided by analyzing social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data about a user's social media activity into a generation AI and have the generation AI generate relevant music.

[0086] The service provider can estimate the user's emotions and adjust the way music is delivered based on those emotions. For example, if the user is stressed, the service provider can deliver music with a simple interface. The service provider can estimate the user's emotions and adjust the way music is delivered based on those emotions. For example, if the user is relaxed, the service provider can deliver music with an interface that includes detailed information. The service provider can also deliver music with voice guidance if the user is exercising. In this way, by adjusting the delivery method based on emotions, the service provider can deliver music that is optimal for the user. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data about the user's emotions into a generating AI and have the generating AI perform the adjustment of the delivery method.

[0087] The service provider can select the optimal service method by referring to the user's past music usage history at the time of service provision. For example, the service provider may prioritize service methods that the user has previously preferred (such as streaming or downloading). The service provider selects the optimal service method by referring to the user's past music usage history at the time of service provision. For example, it may select a service method optimized for the device the user has previously used (such as a smartphone or tablet). The service provider can also select a service method suitable for a specific time period based on the user's past usage history. In this way, the service provider can select the optimal service method for the user by referring to past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's past music usage history into a generating AI and have the generating AI perform the selection of the service method.

[0088] The service provider can customize the music delivery method based on the user's current device status at the time of delivery. For example, if the user is using a smartphone, the service provider will provide music optimized for mobile data. The service provider customizes the music delivery method based on the user's current device status at the time of delivery. For example, if the user is using a tablet, the service provider will provide music optimized for a large screen. The service provider can also provide concise and highly visible music if the user is using a smartwatch. In this way, by customizing the delivery method based on the device status, the service provider can provide the user with the most suitable music. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data about the user's device status into a generating AI and have the generating AI perform the customization of the delivery method.

[0089] The service provider can estimate the user's emotions and adjust the timing of music delivery based on the estimated emotions. For example, if the user is feeling stressed, the service provider can immediately provide relaxing music. The service provider can estimate the user's emotions and adjust the timing of music delivery based on the estimated emotions. For example, if the user is relaxed, the service provider can provide music at an appropriate time. The service provider can also provide music at the peak of the exercise if the user is exercising. In this way, by adjusting the timing of delivery based on emotions, the service provider can provide the user with the most suitable music. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the user's emotions into a generating AI and have the generating AI perform the adjustment of the delivery timing.

[0090] The service provider can select the optimal service delivery method at the time of delivery, taking into account the user's geographical location information. For example, if the user is at home, the service provider can provide high-quality music using Wi-Fi. The service provider selects the optimal service delivery method at the time of delivery, taking into account the user's geographical location information. For example, if the user is out, it can provide music optimized for mobile data. The service provider can also provide music related to a specific location if the user is in that location. In this way, by taking geographical location information into account, the service provider can select the optimal service delivery method for the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data about the user's geographical location information into a generating AI and have the generating AI select the service delivery method.

[0091] The service provider can analyze the user's social media activity and suggest ways to deliver music at the time of delivery. For example, the service provider can provide similar music based on music the user has shared on social media. The service provider can analyze the user's social media activity and suggest ways to deliver music at the time of delivery. For example, it can provide music by artists the user follows on social media. The service provider can also provide music related to events the user is participating in on social media. In this way, by analyzing social media activity, the service provider can provide the user with the most suitable music. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's social media activity into a generating AI and have the generating AI execute suggestions for delivery methods. === Hard Collateral 1-1 === Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires voice and body information using the heart rate sensor and accelerometer of the smart device 14, and the face recognition and voice recognition functions of the monitoring camera. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and estimates emotions using machine learning algorithms and emotion recognition technology. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates music using music generation algorithms and template-based generation. The provision unit is implemented by the control unit 46A of the smart device 14 and provides music by methods such as streaming and real-time playback. === Hard Collateral 1-2 === Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires voice and body information using the microphone and camera of the smart glasses 214. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and estimates emotions using machine learning algorithms and emotion recognition technology. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates music using music generation algorithms and template-based generation. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides music by methods such as streaming and real-time playback. === Hard Collateral 1-3 === Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires voice and body information using the microphone and camera of the headset terminal 314. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and estimates emotions using machine learning algorithms and emotion recognition technology. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates music using music generation algorithms and template-based generation. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides music by methods such as streaming and real-time playback. === Hard Collateral 1-4 === Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires voice and body information using the microphone and camera of the robot 414. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and estimates emotions using machine learning algorithms and emotion recognition technology. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates music using music generation algorithms and template-based generation. The provision unit is implemented by the control unit 46A of the robot 414 and provides music by methods such as streaming and real-time playback.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The acquisition unit can acquire not only the user's voice and physical information, but also ambient sounds around the user. For example, the acquisition unit acquires the noise level and background sounds of the user's location and provides this information to the analysis unit. The analysis unit can analyze the acquired ambient sounds and use this information to estimate the user's emotions. For example, a user in a quiet environment and a user in a noisy environment may exhibit different emotions even with the same heart rate, so incorporating ambient sound information allows for more accurate emotion estimation. Furthermore, the generation unit can adjust the music generation based on the acquired ambient sounds. For example, it can generate relaxing music in a quiet environment and music that enhances concentration in a noisy environment.

[0094] The analysis unit can refer to the user's past emotional data in addition to their voice and physical information. For example, the analysis unit can retrieve from a database what emotions the user experienced in the past in various situations, and use this information to estimate their current emotions. This allows the unit to learn the user's emotional patterns, enabling more accurate emotion estimation. Furthermore, the analysis unit can use past emotional data to predict fluctuations in the user's emotions. For example, it can prepare to provide appropriate music in advance to users whose emotions tend to fluctuate during specific times of day or activities.

[0095] The music generator can consider the user's current activity level when generating music based on the user's emotions. For example, if the user is exercising, the generator can generate fast-paced music, and if the user is relaxed, it can generate slow-paced music. Furthermore, the generator can adjust the volume and rhythm of the music according to the user's activity level. For example, it can optimize the user's experience by increasing the volume during exercise and decreasing it during relaxation. The generator can also select the genre of music based on the user's activity level. For example, it can provide energetic music during exercise and calming music during relaxation.

[0096] The music delivery system can adjust how music is delivered based on the user's emotions. For example, if the user is stressed, the system can deliver music with a simple interface, while if the user is relaxed, it can deliver music with an interface that includes detailed information. The system can also adjust the timing of music delivery according to the user's emotions. For example, if the user is stressed, it can deliver music that helps them relax immediately, while if the user is relaxed, it can deliver music at an appropriate time. Furthermore, the system can customize the means of music delivery based on the user's emotions. For example, if the user is exercising, it can deliver music with voice guidance, while if the user is relaxed, it can deliver music with a visual interface.

[0097] The music delivery system can adjust the frequency of music delivery based on the user's emotions. For example, it can frequently provide relaxing music when the user is stressed and reduce the frequency when the user is relaxed. The system can also personalize the music delivery method according to the user's emotions. For example, it can provide music with a simple interface when the user is stressed and with a more detailed interface when the user is relaxed. Furthermore, the system can adjust the timing of music delivery based on the user's emotions. For example, it can immediately provide relaxing music when the user is stressed and provide music at an appropriate time when the user is relaxed.

[0098] The acquisition unit can acquire not only the user's voice and physical information, but also environmental data surrounding the user. For example, the acquisition unit acquires environmental data such as temperature, humidity, and illuminance of the user's location and provides this information to the analysis unit. The analysis unit can analyze the acquired environmental data and use it to estimate the user's emotions. For example, since users may feel more stressed in a high-temperature environment, incorporating environmental data makes it possible to estimate emotions more accurately. Furthermore, the generation unit can also adjust the music generation based on the acquired environmental data. For example, it can generate relaxing music in a high-temperature environment and lively music in a low-temperature environment.

[0099] The analysis unit can refer to the user's past behavioral data in addition to their voice and physical information. For example, the analysis unit can retrieve information about the user's past actions from a database and use it to estimate their current emotions. This allows the unit to learn the user's behavioral patterns and enable more accurate emotion estimation. Furthermore, the analysis unit can use past behavioral data to predict fluctuations in the user's emotions. For example, it can prepare to provide appropriate music in advance to users whose emotions tend to fluctuate during specific times or activities.

[0100] The music generator can consider the user's current health state when generating music based on the user's emotions. For example, if the user is tired, the generator can generate relaxing music, and if the user is energetic, it can generate lively music. Furthermore, the generator can adjust the volume and rhythm of the music according to the user's health state. For example, it can optimize the user experience by lowering the volume when the user is tired and increasing it when the user is energetic. The generator can also select the genre of music based on the user's health state. For example, it can provide calming music when the user is tired and energetic music when the user is energetic.

[0101] The music delivery system can adjust how music is delivered based on the user's emotions. For example, if the user is stressed, the system can deliver music with a simple interface, while if the user is relaxed, it can deliver music with an interface that includes detailed information. The system can also adjust the timing of music delivery according to the user's emotions. For example, if the user is stressed, it can deliver music that helps them relax immediately, while if the user is relaxed, it can deliver music at an appropriate time. Furthermore, the system can customize the means of music delivery based on the user's emotions. For example, if the user is exercising, it can deliver music with voice guidance, while if the user is relaxed, it can deliver music with a visual interface.

[0102] The music delivery system can adjust the frequency of music delivery based on the user's emotions. For example, it can frequently provide relaxing music when the user is stressed and reduce the frequency when the user is relaxed. The system can also personalize the music delivery method according to the user's emotions. For example, it can provide music with a simple interface when the user is stressed and with a more detailed interface when the user is relaxed. Furthermore, the system can adjust the timing of music delivery based on the user's emotions. For example, it can immediately provide relaxing music when the user is stressed and provide music at an appropriate time when the user is relaxed.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The acquisition unit acquires the user's voice and physical information. This information includes heart rate, voice tone, and facial expressions. The acquisition unit uses a smartwatch and a monitoring camera to acquire this voice and physical information. The smartwatch is equipped with a heart rate sensor and an accelerometer, while the monitoring camera is equipped with facial recognition and voice recognition functions. Step 2: The analysis unit analyzes the voice and body information acquired by the acquisition unit and estimates emotions. The analysis unit estimates emotions using machine learning algorithms and emotion recognition technology, and performs data preprocessing, feature extraction, and algorithm application. Step 3: The generation unit generates music based on the emotions estimated by the analysis unit. The generation unit can generate music using music generation algorithms or template-based generation, and can also generate music while taking into account the user's preferences and culture. Step 4: The delivery unit provides the music generated by the generation unit to the user. The delivery unit can provide the music through methods such as streaming, downloading, and real-time playback, and can also estimate the user's emotions and adjust the delivery method based on the estimated emotions.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0107] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0115] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0116] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0117] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0118] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0119] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0149] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0158] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0160] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0161] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0162] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0166] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0167] 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.

[0168] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0169] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0170] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0171] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0173] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0174] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0175] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that acquires voice and physical information, An analysis unit analyzes the voice and physical information acquired by the acquisition unit and estimates emotions, A generation unit that generates music based on the emotions estimated by the analysis unit, The system includes a providing unit that provides music generated by the generation unit. A system characterized by the following features.

2. The acquisition unit is, Acquire voice and physical information from a smartwatch or monitoring camera. The system according to feature 1.

3. The aforementioned analysis unit, The system analyzes acquired voice and body information to estimate emotions. The system according to feature 1.

4. The generating unit is Generate music based on estimated emotions. The system according to feature 1.

5. The aforementioned supply unit is, Provide the generated music to the user. The system according to feature 1.

6. The generating unit is Music is generated considering the user's preferences or culture. The system according to feature 1.

7. The generating unit is Generate music based on user prompts. The system according to feature 1.

8. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of voice and physical information acquisition based on the estimated emotions. The system according to feature 1.

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