system

Abstract

The system according to this embodiment aims to provide music that is suitable for each individual's physical and mental state based on heart rate data. [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 heart rate data. The analysis unit analyzes the heart rate data acquired by the acquisition unit. The generation unit generates music based on the analysis results obtained by the analysis unit. The provision unit provides the music generated by the generation unit.

Description

Technical Field

[0006] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 prior art, music suitable for individual mental and physical states based on heart rate data has not been sufficiently provided, and there is room for improvement. <s

[0005] The system according to an embodiment aims to provide music suitable for individual mental and physical states based on heart rate data.

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 heart rate data. The analysis unit analyzes the heart rate data acquired by the acquisition unit. The generation unit generates music based on the analysis result obtained 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 tailored to an individual's physical and mental state based on heart rate data. [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 system according to an embodiment of the present invention is a system that utilizes heart rate data from a smartwatch to generate music that guides the user from their current physical and mental state to an ideal state. This system uses AI to analyze heart rate data from a smartwatch and creates or selects music to improve the user's mental state based on mental care information, sports psychology information, music therapy information, and song information. It is usable with all smartphone models, regardless of the model. Specifically, the system first acquires heart rate data from the smartwatch and analyzes that data with AI. Next, based on the analysis results, it grasps the user's current physical and mental state and generates or selects music to guide them to an ideal state. For example, if the user wants to calm down when their heart rate is elevated, the AI ​​analyzes the heart rate and creates calming music. This system is intended for people who use smartwatches, especially athletes, students taking exams, and people who want to concentrate on work, as mental state is a significant factor. Because it is not dependent on the smartwatch model, users can use their existing smartwatches. As a result, the system can improve the user's mental state.

[0029] The system according to the embodiment comprises an acquisition unit, an analysis unit, a generation unit, and a provision unit. The acquisition unit acquires heart rate data. The acquisition unit can acquire heart rate data from, for example, a smartwatch. The acquisition unit can set the measurement frequency and data units of the heart rate data. The acquisition unit can acquire heart rate data in real time and store the data. The analysis unit analyzes the heart rate data acquired by the acquisition unit. The analysis unit can analyze heart rate data based on, for example, mental care information, sports psychology information, music therapy information, and music information. The analysis unit can analyze the fluctuation patterns of the heart rate data and evaluate the user's mental state. The analysis unit can analyze heart rate data using AI and estimate the user's mental state. The generation unit generates music based on the analysis results obtained by the analysis unit. The generation unit can generate music that improves the user's mental state. The generation unit can adjust the tempo and rhythm of the music to generate music suitable for the user's mental state. The generation unit can generate music using AI to improve the user's mental state. The providing unit provides music generated by the generating unit. The providing unit can, for example, provide the generated music to a smartphone. The providing unit may include a reception unit for inputting the user's desired mental state. The providing unit can use AI to provide music and improve the user's mental state. As a result, the system according to this embodiment can improve the user's mental state.

[0030] The acquisition unit acquires heart rate data. For example, the acquisition unit can acquire heart rate data from a smartwatch. Specifically, it measures the heart rate from the user's wrist using an optical heart rate sensor built into the smartwatch. This sensor measures heart rate by irradiating light onto the skin and detecting changes in blood flow. The acquisition unit can set the measurement frequency and data units for heart rate data. For example, it can be set to measure heart rate every second or to record the average heart rate every minute. The acquisition unit can acquire heart rate data in real time and save the data. The data acquired in real time is transmitted from the smartwatch to a smartphone or cloud server via Bluetooth® or Wi-Fi and stored in a central database. This allows the acquisition unit to continuously monitor the user's heart rate data and provide the data to the analysis and generation units as needed. Furthermore, the acquisition unit can be equipped with a function to issue alerts when abnormal heart rate fluctuations are detected. For example, if the heart rate suddenly increases or decreases abnormally, it can send a notification to the smartwatch or smartphone to alert the user. This allows the data acquisition unit to monitor the user's health status in real time and support a quick response.

[0031] The analysis department analyzes heart rate data acquired by the acquisition department. The analysis department can analyze heart rate data based on, for example, mental care information, sports psychology information, music therapy information, and music information. Specifically, it analyzes the fluctuation patterns of heart rate data to evaluate the user's mental state. For example, a stable heart rate can be interpreted as a relaxed state, while a rapid increase in heart rate can be interpreted as a stressed state. The analysis department can use AI to analyze heart rate data and estimate the user's mental state. Based on past data and statistical information, the AI ​​learns the relationship between heart rate fluctuation patterns and mental state, and estimates the user's mental state in real time. For example, machine learning algorithms can be used to quantify stress levels and relaxation levels from heart rate data and provide feedback to the user. Furthermore, the analysis department can perform trend analysis based on long-term data to track changes in the user's mental state. This allows the analysis department to continuously monitor the user's mental state and provide information for taking appropriate measures.

[0032] The generation unit generates music based on the analysis results obtained by the analysis unit. For example, the generation unit can generate music that improves the user's mental state. Specifically, it adjusts the tempo, rhythm, and melody of the music according to the user's mental state to generate optimal music. For example, it can generate music with a relaxed tempo for users who are feeling stressed, and music with an upbeat tempo for users who are relaxed to uplift their mood. The generation unit can use AI to generate music and improve the user's mental state. The AI ​​uses a music generation algorithm to learn the user's preferences and past reactions to generate optimal music. For example, it can use deep learning to learn the user's preferred musical style and instrument combinations and provide individually customized music. Furthermore, the generation unit can receive user feedback in real time and continuously improve the music generation process. As a result, the generation unit can provide music that is optimal for the user's mental state and achieve effective mental care.

[0033] The provider unit provides music generated by the generator unit. For example, the provider unit can provide the generated music to a smartphone. Specifically, it streams the music to the user through a smartphone application. The provider unit may have a reception unit that inputs the user's desired mental state. For example, by inputting a user's desire to relax or concentrate, the provider unit can provide music that matches that desire. The provider unit can use AI to provide music and improve the user's mental state. Based on user feedback, the AI ​​optimizes the selection and playback order of the music provided. For example, if the user shows a positive reaction to a particular piece of music, the AI ​​can adjust the playback to prioritize that music. Furthermore, the provider unit may have a function to synchronize music across multiple devices. For example, it can seamlessly play music across devices such as smartphones, tablets, and smart speakers, providing a consistent experience regardless of which device the user is using. This allows the provider unit to provide the user with an optimal music experience and support the improvement of their mental state.

[0034] The acquisition unit can acquire heart rate data from a smartwatch. The acquisition unit can, for example, acquire heart rate data from a smartwatch in real time. The acquisition unit can periodically acquire heart rate data from a smartwatch and save the data. The acquisition unit can acquire heart rate data from a smartwatch and send the data to the analysis unit. This allows real-time data to be utilized by acquiring heart rate data from a smartwatch. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the heart rate data acquired from the smartwatch into a generating AI and have the generating AI perform data analysis.

[0035] The analysis unit can analyze heart rate data based on mental care information, sports psychology information, music therapy information, and music information. For example, the analysis unit can analyze heart rate data based on mental care information. The analysis unit can analyze heart rate data based on sports psychology information. The analysis unit can analyze heart rate data based on music therapy information. The analysis unit can analyze heart rate data based on music information. This allows for more accurate analysis by analyzing heart rate data based on diverse information. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input heart rate data and mental care information into a generating AI and have the generating AI perform the data analysis.

[0036] The generation unit can generate music that improves the user's mental state based on the analysis results. For example, the generation unit can generate music that has a relaxing effect based on the analysis results. The generation unit can generate music that enhances concentration based on the analysis results. The generation unit can generate music that reduces stress based on the analysis results. In this way, the user's mental state can be effectively improved by generating music based on the analysis results. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the analysis results into a generation AI and have the generation AI perform music generation.

[0037] The distribution unit can provide the generated music to a smartphone. For example, the distribution unit can provide the generated music to a smartphone in real time. The distribution unit can provide the generated music to a smartphone in a downloadable format. The distribution unit can provide the generated music in conjunction with the smartphone's music application. This allows users to easily access music by providing the generated music to their smartphones. Some or all of the above-described processes in the distribution unit may be performed using AI or not. For example, the distribution unit can input the generated music into a generation AI and have the generation AI perform the task of providing it to the smartphone.

[0038] The service provider may include a reception unit for inputting the user's desired mental state. The service provider may, for example, provide an interface for inputting the user's desired mental state. The service provider may accept the user's desired mental state via voice input. The service provider may accept the user's desired mental state via text input. This allows for more personalized music delivery by inputting the user's desired mental state. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider may input the user's desired mental state into a generating AI and have the generating AI deliver the music.

[0039] The data acquisition unit can analyze the user's past heart rate data and select the optimal acquisition method. For example, based on the user's past heart rate data, the data acquisition unit can detect when the heart rate tends to fluctuate during specific time periods and focus on acquiring data during those times. The data acquisition unit can analyze heart rate fluctuation patterns during specific activities (e.g., exercise, sleep) from the user's past heart rate data and select an acquisition method appropriate to that activity. Based on the user's past heart rate data, the data acquisition unit can identify situations that increase stress and enhance data acquisition in those situations. This enables efficient data acquisition by selecting the optimal acquisition method based on past data. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input the user's past heart rate data into a generating AI and have the generating AI select the optimal acquisition method.

[0040] The acquisition unit can filter heart rate data based on the user's current activity level and environment. For example, if the user is exercising, the acquisition unit can filter the heart rate data according to the exercise intensity and exclude abnormal values. If the user is stationary, the acquisition unit can remove noise and acquire accurate data because the fluctuation in heart rate data is small. If the user is in a noisy environment, the acquisition unit can filter the heart rate data taking environmental noise into consideration. This allows for accurate data acquisition by filtering the data based on activity level and environment. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's activity level and environmental data into a generating AI and have the generating AI perform the data filtering.

[0041] The data acquisition unit can prioritize acquiring highly relevant data by considering the user's geographical location when acquiring heart rate data. For example, if the user is in a specific location (e.g., workplace, school), the data acquisition unit can prioritize acquiring heart rate data for that location. If the user is traveling, the data acquisition unit can acquire heart rate data according to the environment of the destination and monitor fluctuations in stress and fatigue. If the user is at home, the data acquisition unit can prioritize acquiring heart rate data to maintain a relaxed state. In this way, highly relevant data can be efficiently acquired by considering geographical location information. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data 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 data.

[0042] The data acquisition unit can analyze the user's social media activity when acquiring heart rate data and acquire relevant data. For example, if the user is feeling stressed on social media, the data acquisition unit can prioritize acquiring heart rate data at that time. If the user is relaxed on social media, the data acquisition unit can acquire heart rate data at that time and use it as data to maintain a relaxed state. If the user is excited on social media, the data acquisition unit can acquire heart rate data at that time and use it as data to manage the state of excitement. In this way, relevant data can be efficiently acquired by analyzing social media activity. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input the user's social media activity data into a generating AI and have the generating AI acquire the relevant data.

[0043] The analysis unit can improve the accuracy of its analysis by considering the fluctuation patterns of heart rate data. For example, the analysis unit can accurately analyze the user's stress and relaxation levels based on the fluctuation patterns of their heart rate data. The analysis unit can analyze heart rate fluctuations during exercise in detail and evaluate the effects of exercise based on the fluctuation patterns of the user's heart rate data. The analysis unit can analyze heart rate fluctuations during sleep and evaluate the quality of sleep based on the fluctuation patterns of the user's heart rate data. In this way, the accuracy of the analysis is improved by considering the fluctuation patterns. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the fluctuation patterns of heart rate data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0044] The analysis unit can evaluate the user's current state by comparing it with past heart rate data. For example, the analysis unit can compare the user's past heart rate data with current data to evaluate increases or decreases in stress. The analysis unit can compare the user's past heart rate data with current data to evaluate changes in relaxation state. The analysis unit can compare the user's past heart rate data with current data to evaluate changes in exercise effects. This allows for an accurate evaluation of the current state by comparing it with past data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's past heart rate data and current data into a generating AI and have the generating AI perform an evaluation of the current state.

[0045] The analysis unit can perform analysis while considering the geographical distribution of users. For example, if a user is in a specific location (e.g., workplace, school), the analysis unit can perform analysis based on heart rate data at that location. If a user is traveling, the analysis unit can analyze heart rate data according to the environment of the destination and evaluate fluctuations in stress and fatigue. If a user is at home, the analysis unit can perform analysis based on heart rate data to maintain a relaxed state. This allows for more accurate analysis by considering geographical distribution. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's geographical distribution data into a generating AI and have the generating AI perform the analysis.

[0046] The analysis unit can improve the accuracy of its analysis by referring to relevant health data. For example, the analysis unit can refer to the user's sleep data and analyze it together with heart rate data to evaluate sleep quality. The analysis unit can refer to the user's exercise data and analyze it together with heart rate data to evaluate the effects of exercise. The analysis unit can refer to the user's dietary data and analyze it together with heart rate data to evaluate the impact of diet. In this way, the accuracy of the analysis is improved by referring to relevant health data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input relevant health data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0047] The generation unit can adjust the rhythm and tempo of the music by considering the fluctuation patterns of heart rate data. For example, if the user's heart rate is high, the generation unit can generate music with a relaxed, slow rhythm. If the user's heart rate is low, the generation unit can generate music with a gentle tempo to maintain a relaxed state. If the user's heart rate is fluctuating, the generation unit can generate music with a rhythm and tempo adjusted to match the fluctuations. This allows for the generation of music with a more appropriate rhythm and tempo by considering the fluctuation patterns. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the fluctuation patterns of heart rate data into a generation AI and have the generation AI adjust the rhythm and tempo of the music.

[0048] The generation unit can generate optimal music by referring to the user's past musical preferences. For example, the generation unit can generate relaxing music based on data of music the user has enjoyed listening to in the past. The generation unit can generate music to enhance the effects of exercise based on data of music the user has listened to in the past. The generation unit can generate music to reduce stress based on data of music the user has listened to in the past. In this way, by referring to past preferences, the generation unit can generate music that is optimal for the user. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's past musical preference data into a generation AI and have the generation AI perform the generation of optimal music.

[0049] The generation unit can generate optimal music by taking into account the user's geographical location. For example, if the user is in a specific location (e.g., work, school), the generation unit can generate relaxing music suitable for that location. If the user is traveling, the generation unit can generate relaxing music suited to the environment of the travel destination. If the user is at home, the generation unit can generate music that allows for relaxation at home. In this way, more appropriate music can be generated by taking geographical location into consideration. Some or all of the above processing in the generation unit may be performed using AI, or it may be performed without AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI perform the generation of optimal music.

[0050] The generation unit can analyze a user's social media activity and generate relevant music. For example, if a user is feeling stressed on social media, the generation unit can generate relaxing music based on their heart rate data at that time. If a user is relaxed on social media, the generation unit can generate music to maintain that relaxed state based on their heart rate data at that time. If a user is excited on social media, the generation unit can generate music to manage that excited state based on their heart rate data at that time. In this way, relevant music can be generated by analyzing social media activity. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the generation of relevant music.

[0051] The service provider can select the optimal service method by referring to the user's past music listening history. For example, the service provider can provide relaxing music based on data of music the user has previously enjoyed listening to. The service provider can provide music to enhance the effects of exercise based on data of music the user has previously listened to. The service provider can provide music to reduce stress based on data of music the user has previously listened to. In this way, the service provider can select the optimal service method by referring to past listening history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past music listening history data into a generating AI and have the generating AI select the optimal service method.

[0052] The music delivery unit can adjust the timing of music delivery based on the user's current activity level. For example, if the user is exercising, the music delivery unit can adjust the timing of music delivery according to the intensity of the exercise. If the user is relaxed, the music delivery unit can adjust the timing of music delivery to maintain that relaxed state. If the user is stressed, the music delivery unit can adjust the timing of music delivery to reduce stress. By adjusting the timing of delivery based on the user's activity level, music can be delivered at a more appropriate time. Some or all of the above processing in the music delivery unit may be performed using AI or not. For example, the music delivery unit can input user activity data into a generating AI and have the generating AI perform the adjustment of the music delivery timing.

[0053] The service provider can provide optimal music by taking into account the user's geographical location. For example, if the user is in a specific location (e.g., work, school), the service provider can provide relaxing music suitable for that location. If the user is traveling, the service provider can provide relaxing music suited to the environment of the travel destination. If the user is at home, the service provider can provide music that allows for relaxation at home. In this way, more appropriate music can be provided by taking geographical location into consideration. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal music.

[0054] The service provider can analyze a user's social media activity and provide relevant music. For example, if a user is feeling stressed on social media, the service provider can provide relaxing music based on their heart rate data at that time. If a user is relaxed on social media, the service provider can provide music to maintain that relaxed state based on their heart rate data at that time. If a user is excited on social media, the service provider can provide music to manage their excited state based on their heart rate data at that time. In this way, relevant music can be provided by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant music.

[0055] The reception desk can select the optimal input method by referring to the user's past preference history. For example, the reception desk can automatically display as candidates the user has frequently entered their desired mental state in the past. The reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest the desired mental state for a specific time period based on the user's past preference history. This allows the reception desk to select the optimal input method by referring to the past preference history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past preference history data into a generating AI and have the generating AI select the optimal input method.

[0056] The reception unit can select the optimal input method by considering the user's device information. For example, if the user is using a smartphone, the reception unit can provide an input method that matches the screen size. If the user is using a tablet, the reception unit can provide an input method optimized for a larger screen. If the user is using a smartwatch, the reception unit can provide a simple and highly visible input method. In this way, the optimal input method can be provided by considering the device information. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's device information into a generating AI and have the generating AI select the optimal input method.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The acquisition unit can analyze the user's past heart rate data and select the optimal acquisition method. For example, based on the user's past heart rate data, it can detect when the heart rate tends to fluctuate, and focus on acquiring data during those times. It can also analyze heart rate fluctuation patterns during specific activities (e.g., exercise, sleep) from the user's past heart rate data and select an acquisition method appropriate for that activity. Based on the user's past heart rate data, it can identify situations that increase stress and enhance data acquisition in those situations. By selecting the optimal acquisition method based on past data, efficient data acquisition becomes possible.

[0059] The analysis unit can improve the accuracy of its analysis by referring to relevant health data. For example, it can refer to a user's sleep data and analyze it together with heart rate data to evaluate sleep quality. It can refer to a user's exercise data and analyze it together with heart rate data to evaluate the effects of exercise. It can refer to a user's dietary data and analyze it together with heart rate data to evaluate the impact of diet. In this way, the accuracy of the analysis is improved by referring to relevant health data.

[0060] The generation unit can adjust the rhythm and tempo of the music by considering the fluctuation patterns of heart rate data. For example, if the user's heart rate is high, it can generate music with a relaxed, slow rhythm. If the user's heart rate is low, it can generate music with a gentle tempo to maintain a relaxed state. If the user's heart rate is fluctuating, it can generate music with a rhythm and tempo adjusted to match the fluctuations. In this way, by considering the fluctuation patterns, it is possible to generate music with a more appropriate rhythm and tempo.

[0061] The service provider can select the optimal delivery method by referring to the user's past music listening history. For example, based on data of music the user has previously enjoyed listening to, it can provide music with a relaxing effect. Based on data of music the user has previously listened to, it can provide music to enhance the effects of exercise. Based on data of music the user has previously listened to, it can provide music to reduce stress. In this way, the optimal delivery method can be selected by referring to past usage history.

[0062] The music delivery system can adjust the timing of music delivery based on the user's current activity level. For example, if the user is exercising, the timing of music delivery can be adjusted according to the intensity of the exercise. If the user is relaxed, the timing of music delivery can be adjusted to maintain that relaxed state. If the user is stressed, the timing of music delivery can be adjusted to reduce stress. By adjusting the timing of delivery based on the user's activity level, music can be delivered at a more appropriate time.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The acquisition unit acquires heart rate data. The acquisition unit can acquire heart rate data from, for example, a smartwatch. The acquisition unit can set the measurement frequency and data units for heart rate data. The acquisition unit can acquire heart rate data in real time and save the data. Step 2: The analysis unit analyzes the heart rate data acquired by the acquisition unit. The analysis unit can analyze the heart rate data based on, for example, mental care information, sports psychology information, music therapy information, and music information. The analysis unit can analyze the fluctuation patterns of the heart rate data and evaluate the user's mental state. The analysis unit can use AI to analyze the heart rate data and estimate the user's mental state. Step 3: The generation unit generates music based on the analysis results obtained by the analysis unit. The generation unit can, for example, generate music that improves the user's mental state. The generation unit can adjust the tempo and rhythm of the music to generate music suitable for the user's mental state. The generation unit can use AI to generate music and improve the user's mental state. Step 4: The providing unit provides the music generated by the generating unit. The providing unit can, for example, provide the generated music to a smartphone. The providing unit may include a reception unit for inputting the user's desired mental state. The providing unit can use AI to provide music and improve the user's mental state.

[0065] (Example of form 2) The system according to an embodiment of the present invention is a system that utilizes heart rate data from a smartwatch to generate music that guides the user from their current physical and mental state to an ideal state. This system uses AI to analyze heart rate data from a smartwatch and creates or selects music to improve the user's mental state based on mental care information, sports psychology information, music therapy information, and song information. It is usable with all smartphone models, regardless of the model. Specifically, the system first acquires heart rate data from the smartwatch and analyzes that data with AI. Next, based on the analysis results, it grasps the user's current physical and mental state and generates or selects music to guide them to an ideal state. For example, if the user wants to calm down when their heart rate is elevated, the AI ​​analyzes the heart rate and creates calming music. This system is intended for people who use smartwatches, especially athletes, students taking exams, and people who want to concentrate on work, as mental state is a significant factor. Because it is not dependent on the smartwatch model, users can use their existing smartwatches. As a result, the system can improve the user's mental state.

[0066] The system according to the embodiment comprises an acquisition unit, an analysis unit, a generation unit, and a provision unit. The acquisition unit acquires heart rate data. The acquisition unit can acquire heart rate data from, for example, a smartwatch. The acquisition unit can set the measurement frequency and data units of the heart rate data. The acquisition unit can acquire heart rate data in real time and store the data. The analysis unit analyzes the heart rate data acquired by the acquisition unit. The analysis unit can analyze heart rate data based on, for example, mental care information, sports psychology information, music therapy information, and music information. The analysis unit can analyze the fluctuation patterns of the heart rate data and evaluate the user's mental state. The analysis unit can analyze heart rate data using AI and estimate the user's mental state. The generation unit generates music based on the analysis results obtained by the analysis unit. The generation unit can generate music that improves the user's mental state. The generation unit can adjust the tempo and rhythm of the music to generate music suitable for the user's mental state. The generation unit can generate music using AI to improve the user's mental state. The providing unit provides music generated by the generating unit. The providing unit can, for example, provide the generated music to a smartphone. The providing unit may include a reception unit for inputting the user's desired mental state. The providing unit can use AI to provide music and improve the user's mental state. As a result, the system according to this embodiment can improve the user's mental state.

[0067] The acquisition unit acquires heart rate data. For example, it can acquire heart rate data from a smartwatch. Specifically, it measures the user's heart rate from their wrist using an optical heart rate sensor built into the smartwatch. This sensor measures heart rate by irradiating light onto the skin and detecting changes in blood flow. The acquisition unit can set the measurement frequency and data units for heart rate data. For example, it can be set to measure heart rate every second or to record the average heart rate every minute. The acquisition unit can acquire heart rate data in real time and save the data. The data acquired in real time is transmitted from the smartwatch to a smartphone or cloud server via Bluetooth or Wi-Fi and stored in a central database. This allows the acquisition unit to continuously monitor the user's heart rate data and provide the data to the analysis and generation units as needed. Furthermore, the acquisition unit can be equipped with a function to issue alerts when it detects abnormal heart rate fluctuations. For example, if the heart rate suddenly increases or decreases abnormally, it can send a notification to the smartwatch or smartphone to alert the user. This allows the data acquisition unit to monitor the user's health status in real time and support a quick response.

[0068] The analysis department analyzes heart rate data acquired by the acquisition department. The analysis department can analyze heart rate data based on, for example, mental care information, sports psychology information, music therapy information, and music information. Specifically, it analyzes the fluctuation patterns of heart rate data to evaluate the user's mental state. For example, a stable heart rate can be interpreted as a relaxed state, while a rapid increase in heart rate can be interpreted as a stressed state. The analysis department can use AI to analyze heart rate data and estimate the user's mental state. Based on past data and statistical information, the AI ​​learns the relationship between heart rate fluctuation patterns and mental state, and estimates the user's mental state in real time. For example, machine learning algorithms can be used to quantify stress levels and relaxation levels from heart rate data and provide feedback to the user. Furthermore, the analysis department can perform trend analysis based on long-term data to track changes in the user's mental state. This allows the analysis department to continuously monitor the user's mental state and provide information for taking appropriate measures.

[0069] The generation unit generates music based on the analysis results obtained by the analysis unit. For example, the generation unit can generate music that improves the user's mental state. Specifically, it adjusts the tempo, rhythm, and melody of the music according to the user's mental state to generate optimal music. For example, it can generate music with a relaxed tempo for users who are feeling stressed, and music with an upbeat tempo for users who are relaxed to uplift their mood. The generation unit can use AI to generate music and improve the user's mental state. The AI ​​uses a music generation algorithm to learn the user's preferences and past reactions to generate optimal music. For example, it can use deep learning to learn the user's preferred musical style and instrument combinations and provide individually customized music. Furthermore, the generation unit can receive user feedback in real time and continuously improve the music generation process. As a result, the generation unit can provide music that is optimal for the user's mental state and achieve effective mental care.

[0070] The provider unit provides music generated by the generator unit. For example, the provider unit can provide the generated music to a smartphone. Specifically, it streams the music to the user through a smartphone application. The provider unit may have a reception unit that inputs the user's desired mental state. For example, by inputting a user's desire to relax or concentrate, the provider unit can provide music that matches that desire. The provider unit can use AI to provide music and improve the user's mental state. Based on user feedback, the AI ​​optimizes the selection and playback order of the music provided. For example, if the user shows a positive reaction to a particular piece of music, the AI ​​can adjust the playback to prioritize that music. Furthermore, the provider unit may have a function to synchronize music across multiple devices. For example, it can seamlessly play music across devices such as smartphones, tablets, and smart speakers, providing a consistent experience regardless of which device the user is using. This allows the provider unit to provide the user with an optimal music experience and support the improvement of their mental state.

[0071] The acquisition unit can acquire heart rate data from a smartwatch. The acquisition unit can, for example, acquire heart rate data from a smartwatch in real time. The acquisition unit can periodically acquire heart rate data from a smartwatch and save the data. The acquisition unit can acquire heart rate data from a smartwatch and send the data to the analysis unit. This allows real-time data to be utilized by acquiring heart rate data from a smartwatch. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the heart rate data acquired from the smartwatch into a generating AI and have the generating AI perform data analysis.

[0072] The analysis unit can analyze heart rate data based on mental care information, sports psychology information, music therapy information, and music information. For example, the analysis unit can analyze heart rate data based on mental care information. The analysis unit can analyze heart rate data based on sports psychology information. The analysis unit can analyze heart rate data based on music therapy information. The analysis unit can analyze heart rate data based on music information. This allows for more accurate analysis by analyzing heart rate data based on diverse information. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input heart rate data and mental care information into a generating AI and have the generating AI perform the data analysis.

[0073] The generation unit can generate music that improves the user's mental state based on the analysis results. For example, the generation unit can generate music that has a relaxing effect based on the analysis results. The generation unit can generate music that enhances concentration based on the analysis results. The generation unit can generate music that reduces stress based on the analysis results. In this way, the user's mental state can be effectively improved by generating music based on the analysis results. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the analysis results into a generation AI and have the generation AI perform music generation.

[0074] The distribution unit can provide the generated music to a smartphone. For example, the distribution unit can provide the generated music to a smartphone in real time. The distribution unit can provide the generated music to a smartphone in a downloadable format. The distribution unit can provide the generated music in conjunction with the smartphone's music application. This allows users to easily access music by providing the generated music to their smartphones. Some or all of the above-described processes in the distribution unit may be performed using AI or not. For example, the distribution unit can input the generated music into a generation AI and have the generation AI perform the task of providing it to the smartphone.

[0075] The service provider may include a reception unit for inputting the user's desired mental state. The service provider may, for example, provide an interface for inputting the user's desired mental state. The service provider may accept the user's desired mental state via voice input. The service provider may accept the user's desired mental state via text input. This allows for more personalized music delivery by inputting the user's desired mental state. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider may input the user's desired mental state into a generating AI and have the generating AI deliver the music.

[0076] The acquisition unit can estimate the user's emotions and adjust the timing of heart rate data acquisition based on the estimated emotions. For example, if the user is stressed, the acquisition unit can acquire heart rate data frequently and monitor fluctuations in real time. If the user is relaxed, the acquisition unit can widen the interval between heart rate data acquisitions and acquire data only when necessary. If the user is exercising, the acquisition unit can adjust the frequency of heart rate data acquisition according to the intensity of the exercise. This allows for more appropriate data acquisition by adjusting the timing of heart rate data acquisition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's emotion data into the generative AI and have the generative AI adjust the timing of heart rate data acquisition.

[0077] The data acquisition unit can analyze the user's past heart rate data and select the optimal acquisition method. For example, based on the user's past heart rate data, the data acquisition unit can detect when the heart rate tends to fluctuate during specific time periods and focus on acquiring data during those times. The data acquisition unit can analyze heart rate fluctuation patterns during specific activities (e.g., exercise, sleep) from the user's past heart rate data and select an acquisition method appropriate to that activity. Based on the user's past heart rate data, the data acquisition unit can identify situations that increase stress and enhance data acquisition in those situations. This enables efficient data acquisition by selecting the optimal acquisition method based on past data. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input the user's past heart rate data into a generating AI and have the generating AI select the optimal acquisition method.

[0078] The acquisition unit can filter heart rate data based on the user's current activity level and environment. For example, if the user is exercising, the acquisition unit can filter the heart rate data according to the exercise intensity and exclude abnormal values. If the user is stationary, the acquisition unit can remove noise and acquire accurate data because the fluctuation in heart rate data is small. If the user is in a noisy environment, the acquisition unit can filter the heart rate data taking environmental noise into consideration. This allows for accurate data acquisition by filtering the data based on activity level and environment. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's activity level and environmental data into a generating AI and have the generating AI perform the data filtering.

[0079] The acquisition unit can estimate the user's emotions and determine the priority of heart rate data to acquire based on the estimated emotions. For example, if the user is stressed, the acquisition unit can prioritize the acquisition of heart rate data and monitor fluctuations in real time. If the user is relaxed, the acquisition unit can reduce the frequency of heart rate data acquisition and acquire data only when necessary. If the user is exercising, the acquisition unit can adjust the frequency of heart rate data acquisition according to the intensity of the exercise. This allows for the priority acquisition of important data by determining data priority based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's emotion data into a generative AI and have the generative AI determine the priority of heart rate data.

[0080] The data acquisition unit can prioritize acquiring highly relevant data by considering the user's geographical location when acquiring heart rate data. For example, if the user is in a specific location (e.g., workplace, school), the data acquisition unit can prioritize acquiring heart rate data for that location. If the user is traveling, the data acquisition unit can acquire heart rate data according to the environment of the destination and monitor fluctuations in stress and fatigue. If the user is at home, the data acquisition unit can prioritize acquiring heart rate data to maintain a relaxed state. In this way, highly relevant data can be efficiently acquired by considering geographical location information. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data 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 data.

[0081] The data acquisition unit can analyze the user's social media activity when acquiring heart rate data and acquire relevant data. For example, if the user is feeling stressed on social media, the data acquisition unit can prioritize acquiring heart rate data at that time. If the user is relaxed on social media, the data acquisition unit can acquire heart rate data at that time and use it as data to maintain a relaxed state. If the user is excited on social media, the data acquisition unit can acquire heart rate data at that time and use it as data to manage the state of excitement. In this way, relevant data can be efficiently acquired by analyzing social media activity. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input the user's social media activity data into a generating AI and have the generating AI acquire the relevant data.

[0082] The analysis unit can estimate the user's emotions and adjust the analysis method of heart rate data based on the estimated user emotions. For example, if the user is stressed, the analysis unit can analyze the fluctuations in heart rate data in detail and identify the cause of the stress. If the user is relaxed, the analysis unit can analyze the stability of heart rate data and identify the factors that contribute to maintaining the relaxed state. If the user is exercising, the analysis unit can analyze the fluctuations in heart rate data according to the exercise intensity and evaluate the effect of the exercise. This allows for more appropriate analysis by adjusting the analysis method based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI adjust the analysis method of heart rate data.

[0083] The analysis unit can improve the accuracy of its analysis by considering the fluctuation patterns of heart rate data. For example, the analysis unit can accurately analyze the user's stress and relaxation levels based on the fluctuation patterns of their heart rate data. The analysis unit can analyze heart rate fluctuations during exercise in detail and evaluate the effects of exercise based on the fluctuation patterns of the user's heart rate data. The analysis unit can analyze heart rate fluctuations during sleep and evaluate the quality of sleep based on the fluctuation patterns of the user's heart rate data. In this way, the accuracy of the analysis is improved by considering the fluctuation patterns. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the fluctuation patterns of heart rate data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0084] The analysis unit can evaluate the user's current state by comparing it with past heart rate data. For example, the analysis unit can compare the user's past heart rate data with current data to evaluate increases or decreases in stress. The analysis unit can compare the user's past heart rate data with current data to evaluate changes in relaxation state. The analysis unit can compare the user's past heart rate data with current data to evaluate changes in exercise effects. This allows for an accurate evaluation of the current state by comparing it with past data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's past heart rate data and current data into a generating AI and have the generating AI perform an evaluation of the current state.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is exercising, the analysis unit can visually display the effects of the exercise in an easy-to-understand way. By adjusting the display method based on emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0086] The analysis unit can perform analysis while considering the geographical distribution of users. For example, if a user is in a specific location (e.g., workplace, school), the analysis unit can perform analysis based on heart rate data at that location. If a user is traveling, the analysis unit can analyze heart rate data according to the environment of the destination and evaluate fluctuations in stress and fatigue. If a user is at home, the analysis unit can perform analysis based on heart rate data to maintain a relaxed state. This allows for more accurate analysis by considering geographical distribution. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's geographical distribution data into a generating AI and have the generating AI perform the analysis.

[0087] The analysis unit can improve the accuracy of its analysis by referring to relevant health data. For example, the analysis unit can refer to the user's sleep data and analyze it together with heart rate data to evaluate sleep quality. The analysis unit can refer to the user's exercise data and analyze it together with heart rate data to evaluate the effects of exercise. The analysis unit can refer to the user's dietary data and analyze it together with heart rate data to evaluate the impact of diet. In this way, the accuracy of the analysis is improved by referring to relevant health data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input relevant health data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0088] The generation unit can estimate the user's emotions and adjust the characteristics of the music it generates based on the estimated emotions. For example, if the user is stressed, the generation unit can generate relaxing music. If the user is relaxed, the generation unit can generate music to maintain that relaxed state. If the user is exercising, the generation unit can generate music to enhance the effects of the exercise. In this way, more effective music can be generated by adjusting the characteristics of the music based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform the adjustment of the music characteristics.

[0089] The generation unit can adjust the rhythm and tempo of the music by considering the fluctuation patterns of heart rate data. For example, if the user's heart rate is high, the generation unit can generate music with a relaxed, slow rhythm. If the user's heart rate is low, the generation unit can generate music with a gentle tempo to maintain a relaxed state. If the user's heart rate is fluctuating, the generation unit can generate music with a rhythm and tempo adjusted to match the fluctuations. This allows for the generation of music with a more appropriate rhythm and tempo by considering the fluctuation patterns. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the fluctuation patterns of heart rate data into a generation AI and have the generation AI adjust the rhythm and tempo of the music.

[0090] The generation unit can generate optimal music by referring to the user's past musical preferences. For example, the generation unit can generate relaxing music based on data of music the user has enjoyed listening to in the past. The generation unit can generate music to enhance the effects of exercise based on data of music the user has listened to in the past. The generation unit can generate music to reduce stress based on data of music the user has listened to in the past. In this way, by referring to past preferences, the generation unit can generate music that is optimal for the user. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's past musical preference data into a generation AI and have the generation AI perform the generation of optimal music.

[0091] The generation unit can estimate the user's emotions and determine the genre of music to generate based on the estimated emotions. For example, if the user is stressed, the generation unit can generate relaxing classical music. If the user is relaxed, the generation unit can generate ambient music to maintain that relaxed state. If the user is exercising, the generation unit can generate upbeat music to enhance the exercise effect. This allows for the generation of more effective music by determining the genre of music based on emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI determine the genre of music.

[0092] The generation unit can generate optimal music by taking into account the user's geographical location. For example, if the user is in a specific location (e.g., work, school), the generation unit can generate relaxing music suitable for that location. If the user is traveling, the generation unit can generate relaxing music suited to the environment of the travel destination. If the user is at home, the generation unit can generate music that allows for relaxation at home. In this way, more appropriate music can be generated by taking geographical location into consideration. Some or all of the above processing in the generation unit may be performed using AI, or it may be performed without AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI perform the generation of optimal music.

[0093] The generation unit can analyze a user's social media activity and generate relevant music. For example, if a user is feeling stressed on social media, the generation unit can generate relaxing music based on their heart rate data at that time. If a user is relaxed on social media, the generation unit can generate music to maintain that relaxed state based on their heart rate data at that time. If a user is excited on social media, the generation unit can generate music to manage that excited state based on their heart rate data at that time. In this way, relevant music can be generated by analyzing social media activity. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the generation of relevant music.

[0094] 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 feeling stressed, the service provider can automatically play relaxing music. If the user is relaxed, the service provider can provide music to help maintain that relaxed state. If the user is exercising, the service provider can provide music to enhance the effects of the exercise. By adjusting the delivery method based on emotions, more effective music delivery becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the way music is delivered.

[0095] The service provider can select the optimal service method by referring to the user's past music listening history. For example, the service provider can provide relaxing music based on data of music the user has previously enjoyed listening to. The service provider can provide music to enhance the effects of exercise based on data of music the user has previously listened to. The service provider can provide music to reduce stress based on data of music the user has previously listened to. In this way, the service provider can select the optimal service method by referring to past listening history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past music listening history data into a generating AI and have the generating AI select the optimal service method.

[0096] The music delivery unit can adjust the timing of music delivery based on the user's current activity level. For example, if the user is exercising, the music delivery unit can adjust the timing of music delivery according to the intensity of the exercise. If the user is relaxed, the music delivery unit can adjust the timing of music delivery to maintain that relaxed state. If the user is stressed, the music delivery unit can adjust the timing of music delivery to reduce stress. By adjusting the timing of delivery based on the user's activity level, music can be delivered at a more appropriate time. Some or all of the above processing in the music delivery unit may be performed using AI or not. For example, the music delivery unit can input user activity data into a generating AI and have the generating AI perform the adjustment of the music delivery timing.

[0097] The music delivery unit can estimate the user's emotions and determine the order in which music is delivered based on the estimated emotions. For example, if the user is feeling stressed, the music delivery unit can prioritize providing relaxing music. If the user is relaxed, the music delivery unit can provide music to maintain that relaxed state. If the user is exercising, the music delivery unit can prioritize providing music to enhance the effects of the exercise. This makes it possible to provide more effective music by determining the order of delivery based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the music delivery unit may be performed using AI or not using AI. For example, the music delivery unit can input user emotion data into a generative AI and have the generative AI determine the order in which music is delivered.

[0098] The service provider can provide optimal music by taking into account the user's geographical location. For example, if the user is in a specific location (e.g., work, school), the service provider can provide relaxing music suitable for that location. If the user is traveling, the service provider can provide relaxing music suited to the environment of the travel destination. If the user is at home, the service provider can provide music that allows for relaxation at home. In this way, more appropriate music can be provided by taking geographical location into consideration. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal music.

[0099] The service provider can analyze a user's social media activity and provide relevant music. For example, if a user is feeling stressed on social media, the service provider can provide relaxing music based on their heart rate data at that time. If a user is relaxed on social media, the service provider can provide music to maintain that relaxed state based on their heart rate data at that time. If a user is excited on social media, the service provider can provide music to manage their excited state based on their heart rate data at that time. In this way, relevant music can be provided by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant music.

[0100] The reception desk can estimate the user's emotions and adjust the input method for the desired mental state based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of the desired mental state. This allows for more appropriate input by adjusting the input method based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's emotion data into a generative AI and have the generative AI adjust the input method for the desired mental state.

[0101] The reception desk can select the optimal input method by referring to the user's past preference history. For example, the reception desk can automatically display as candidates the user has frequently entered their desired mental state in the past. The reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest the desired mental state for a specific time period based on the user's past preference history. This allows the reception desk to select the optimal input method by referring to the past preference history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past preference history data into a generating AI and have the generating AI select the optimal input method.

[0102] The reception unit can estimate the user's emotions and determine the priority of desired mental states based on the estimated emotions. For example, if the user is stressed, the reception unit can prioritize providing mental states that promote relaxation. If the user is relaxed, the reception unit can provide mental states that help maintain that relaxed state. If the user is exercising, the reception unit can prioritize providing mental states that enhance the effects of the exercise. This allows for the provision of more appropriate mental states by prioritizing based on emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's emotion data into a generative AI and have the generative AI determine the priority of desired mental states.

[0103] The reception unit can select the optimal input method by considering the user's device information. For example, if the user is using a smartphone, the reception unit can provide an input method that matches the screen size. If the user is using a tablet, the reception unit can provide an input method optimized for a larger screen. If the user is using a smartwatch, the reception unit can provide a simple and highly visible input method. In this way, the optimal input method can be provided by considering the device information. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's device information into a generating AI and have the generating AI select the optimal input method.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The acquisition unit can estimate the user's emotions and adjust the frequency of heart rate data acquisition based on the estimated emotions. For example, if the user is stressed, heart rate data can be acquired frequently to monitor fluctuations in real time. If the user is relaxed, the interval between heart rate data acquisitions can be widened, and data can be acquired only when necessary. If the user is exercising, the frequency of heart rate data acquisition can be adjusted according to the intensity of the exercise. This allows for more appropriate data acquisition by adjusting the frequency of heart rate data acquisition according to the user's emotions.

[0106] The acquisition unit can analyze the user's past heart rate data and select the optimal acquisition method. For example, based on the user's past heart rate data, it can detect when the heart rate tends to fluctuate, and focus on acquiring data during those times. It can also analyze heart rate fluctuation patterns during specific activities (e.g., exercise, sleep) from the user's past heart rate data and select an acquisition method appropriate for that activity. Based on the user's past heart rate data, it can identify situations that increase stress and enhance data acquisition in those situations. By selecting the optimal acquisition method based on past data, efficient data acquisition becomes possible.

[0107] The analysis unit can estimate the user's emotions and adjust the analysis method of heart rate data based on the estimated emotions. For example, if the user is stressed, the system can analyze the fluctuations in heart rate data in detail to identify the cause of the stress. If the user is relaxed, the system can analyze the stability of the heart rate data to identify factors that contribute to maintaining the relaxed state. If the user is exercising, the system can analyze the fluctuations in heart rate data according to the exercise intensity to evaluate the effects of the exercise. By adjusting the analysis method based on emotions, a more appropriate analysis becomes possible.

[0108] The analysis unit can improve the accuracy of its analysis by referring to relevant health data. For example, it can refer to a user's sleep data and analyze it together with heart rate data to evaluate sleep quality. It can refer to a user's exercise data and analyze it together with heart rate data to evaluate the effects of exercise. It can refer to a user's dietary data and analyze it together with heart rate data to evaluate the impact of diet. In this way, the accuracy of the analysis is improved by referring to relevant health data.

[0109] The generation unit can estimate the user's emotions and adjust the characteristics of the music it generates based on those emotions. For example, if the user is stressed, it can generate music with a relaxing effect. If the user is relaxed, it can generate music to maintain that relaxed state. If the user is exercising, it can generate music to enhance the effects of the exercise. In this way, by adjusting the characteristics of the music based on emotions, more effective music can be generated.

[0110] The generation unit can adjust the rhythm and tempo of the music by considering the fluctuation patterns of heart rate data. For example, if the user's heart rate is high, it can generate music with a relaxed, slow rhythm. If the user's heart rate is low, it can generate music with a gentle tempo to maintain a relaxed state. If the user's heart rate is fluctuating, it can generate music with a rhythm and tempo adjusted to match the fluctuations. In this way, by considering the fluctuation patterns, it is possible to generate music with a more appropriate rhythm and tempo.

[0111] The music delivery system can estimate the user's emotions and adjust the music delivery method based on those emotions. For example, if the user is feeling stressed, it can automatically play relaxing music. If the user is relaxed, it can provide music to maintain that relaxed state. If the user is exercising, it can provide music to enhance the effects of the exercise. By adjusting the delivery method based on emotions, more effective music delivery becomes possible.

[0112] The service provider can select the optimal delivery method by referring to the user's past music listening history. For example, based on data of music the user has previously enjoyed listening to, it can provide music with a relaxing effect. Based on data of music the user has previously listened to, it can provide music to enhance the effects of exercise. Based on data of music the user has previously listened to, it can provide music to reduce stress. In this way, the optimal delivery method can be selected by referring to past usage history.

[0113] The music delivery system can adjust the timing of music delivery based on the user's current activity level. For example, if the user is exercising, the timing of music delivery can be adjusted according to the intensity of the exercise. If the user is relaxed, the timing of music delivery can be adjusted to maintain that relaxed state. If the user is stressed, the timing of music delivery can be adjusted to reduce stress. By adjusting the timing of delivery based on the user's activity level, music can be delivered at a more appropriate time.

[0114] The reception system can estimate the user's emotions and adjust the input method for their desired mental state based on that estimation. For example, if the user is stressed, a simple interface can be provided, minimizing the input steps. If the user is relaxed, detailed input options can be offered, and a customizable input method can be suggested. If the user is in a hurry, voice input can be prioritized, allowing them to quickly input their desired mental state. This allows for more appropriate input by adjusting the input method based on emotions.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The acquisition unit acquires heart rate data. The acquisition unit can acquire heart rate data from, for example, a smartwatch. The acquisition unit can set the measurement frequency and data units for heart rate data. The acquisition unit can acquire heart rate data in real time and save the data. Step 2: The analysis unit analyzes the heart rate data acquired by the acquisition unit. The analysis unit can analyze the heart rate data based on, for example, mental care information, sports psychology information, music therapy information, and music information. The analysis unit can analyze the fluctuation patterns of the heart rate data and evaluate the user's mental state. The analysis unit can use AI to analyze the heart rate data and estimate the user's mental state. Step 3: The generation unit generates music based on the analysis results obtained by the analysis unit. The generation unit can, for example, generate music that improves the user's mental state. The generation unit can adjust the tempo and rhythm of the music to generate music suitable for the user's mental state. The generation unit can use AI to generate music and improve the user's mental state. Step 4: The providing unit provides the music generated by the generating unit. The providing unit can, for example, provide the generated music to a smartphone. The providing unit may include a reception unit for inputting the user's desired mental state. The providing unit can use AI to provide music and improve the user's mental state.

[0117] 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.

[0118] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0119] 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.

[0120] 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 device 12. For example, the acquisition unit is implemented by the control unit 46A of the smart device 14 and acquires heart rate data from a smartwatch. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the heart rate data. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates music that improves the user's mental state. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated music to a smartphone. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] 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.

[0136] Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, and provision unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit is implemented by the control unit 46A of the smart glasses 214 and acquires heart rate data from a smartwatch. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing device 12 and analyzes the heart rate data. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing device 12 and generates music that improves the user's mental state. The provision unit is implemented by, for example, the control unit 46A of the smart glasses 214 and provides the generated music to a smartphone. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] 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.

[0152] 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 is implemented by the control unit 46A of the headset terminal 314 and acquires heart rate data from a smartwatch. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the heart rate data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates music that improves the user's mental state. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated music to a smartphone. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] 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.

[0169] Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the robot 414 and acquires heart rate data from a smartwatch. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the heart rate data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates music that improves the user's mental state. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the generated music to a smartphone. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Note 1) An acquisition unit that acquires heart rate data, An analysis unit analyzes the heart rate data acquired by the acquisition unit, A generation unit that generates music based on the analysis results obtained 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. (Note 2) The acquisition unit is, Obtain heart rate data from a smartwatch. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Analyze heart rate data based on mental health care information, sports psychology information, music therapy information, and music information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Based on the analysis results, it generates music that improves the user's mental state. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provides the generated music to your smartphone. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, It features a reception area where the user can input their desired mental state. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of heart rate data acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, Analyze the user's past heart rate data and select the optimal acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When acquiring heart rate data, filtering is performed based on the user's current activity level and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, The system estimates the user's emotions and prioritizes the acquisition of heart rate data based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring heart rate data, the system prioritizes the acquisition of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When acquiring heart rate data, the system analyzes the user's social media activity and retrieves relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is The system estimates the user's emotions and adjusts the analysis method of heart rate data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is Improve the accuracy of the analysis by considering the variability patterns of heart rate data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is The system evaluates the user's current state by comparing it to their past heart rate data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is The analysis will take into account the geographical distribution of users. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is Improve the accuracy of the analysis by referring to relevant health data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the characteristics of the music generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is The music rhythm and tempo are adjusted based on the variability patterns of heart rate data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It generates optimal music by referencing the user's past musical preferences. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and determines the genre of music to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is It generates optimal music considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is Analyzes users' social media activity to generate relevant music. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way music is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, The optimal delivery method is selected by referring to the user's past music usage history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, Adjust the timing of music delivery based on the user's current activity level. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the user's emotions and determines the order in which music is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, Providing optimal music based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, Analyze users' social media activity and provide relevant music. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for their desired mental state based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned reception unit is The system selects the optimal input method by referring to the user's past preference history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned reception unit is It estimates the user's emotions and determines the priority of desired mental states based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned reception unit is The optimal input method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0189] 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 heart rate data, An analysis unit analyzes the heart rate data acquired by the acquisition unit, A generation unit that generates music based on the analysis results obtained 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, Obtain heart rate data from a smartwatch. The system according to feature 1.

3. The aforementioned analysis unit is Analyze heart rate data based on mental health care information, sports psychology information, music therapy information, and music information. The system according to feature 1.

4. The generating unit is Based on the analysis results, it generates music that improves the user's mental state. The system according to feature 1.

5. The aforementioned supply unit is, Provides the generated music to your smartphone. The system according to feature 1.

6. The aforementioned supply unit is, It features a reception area where the user can input their desired mental state. The system according to feature 1.

7. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of heart rate data acquisition based on the estimated emotions. The system according to feature 1.

8. The acquisition unit is, Analyze the user's past heart rate data and select the optimal acquisition method. The system according to feature 1.

9. The acquisition unit is, When acquiring heart rate data, filtering is performed based on the user's current activity level and environment. The system according to feature 1.

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