system

Abstract

The system according to the embodiment aims to automatically generate and play back EDM based on the user's biometric information and environmental information. [Solution] A system according to an embodiment includes an acquisition unit, an analysis unit, a generation unit, and a playback unit. The acquisition unit acquires biometric information. The analysis unit analyzes the biometric information acquired by the acquisition unit. The generation unit generates an EDM based on the information analyzed by the analysis unit. The playback unit plays back the EDM generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have not adequately addressed the automatic generation of music based on a user's biometric information and environmental information, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically generate and play back EDM based on the user's biometric information and environmental information. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a generation unit, and a playback unit. The acquisition unit acquires biometric information. The analysis unit analyzes the biometric information acquired by the acquisition unit. The generation unit generates an EDM based on the information analyzed by the analysis unit. The playback unit plays back the EDM generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate and play EDM based on the user's biometric information and environmental information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A music generation system according to an embodiment of the present invention recognizes sounds generated by the user and their surroundings, weather information, and the like, and automatically generates and plays electronic dance music (EDM) with a similar tempo and atmosphere. The music generation system acquires biometric information, such as the user's heart rate and walking sounds, using sensors, and also collects ambient sounds, weather information, and location information. A generation AI then analyzes this information and automatically generates EDM with a similar tempo and rhythm. The generated EDM is then played as a song that matches the user's mental state and the ambient atmosphere. For example, the music generation system acquires biometric information, such as the user's heart rate and walking sounds, using sensors. For example, sensors on a wearable device or smart device can acquire the user's heart rate and walking sounds in real time. The music generation system then collects ambient sounds, weather information, location information, and the like. For example, ambient sounds can be collected using a device's microphone, and weather information can be obtained from the Internet. Location information can also be acquired using GPS. The generation AI then analyzes the acquired biometric information and ambient environmental information to automatically generate EDM with a similar tempo and rhythm. For example, if the user's heart rate is fast, a fast-tempo EDM can be generated, and if the heart rate is slow, a slow-tempo EDM can be generated. Furthermore, music that matches the atmosphere can be generated based on ambient sounds and weather information. For example, a calm EDM can be generated on a rainy day, and a bright EDM can be generated on a sunny day. The generated EDM is played as a song that matches the user's mental state and the surrounding atmosphere. For example, if the user is feeling stressed, a relaxing EDM can be played, and if the user is feeling energetic, an energetic EDM can be played. This allows the music generation system to provide music that matches the user's mental state and the surrounding atmosphere. For example, if the user is feeling stressed, a relaxing EDM can be played, and if the user is feeling energetic, an energetic EDM can be played. This allows the user to enjoy their daily lives without feeling stressed.The music generation system is provided as an application, and can generate revenue through in-app purchases and advertising. Users can easily use the system using their smart devices. For example, by simply downloading the application and setting up the sensors, the system can automatically generate and play EDM music tailored to the user's state of mind.

[0029] A music generation system according to an embodiment includes an acquisition unit, an analysis unit, a generation unit, and a playback unit. The acquisition unit acquires biometric information. Examples of the biometric information include, but are not limited to, heart rate, blood pressure, and body temperature. The acquisition unit acquires the user's heart rate and walking sounds in real time using, for example, a sensor in a wearable device or a smart device. The acquisition unit can also collect ambient sounds, weather information, and location information. For example, ambient sounds can be collected using a device's microphone, and weather information can be acquired from the Internet. Location information can also be acquired using a GPS. The analysis unit analyzes the biometric information acquired by the acquisition unit. The analysis can be performed, for example, using data preprocessing or an analysis algorithm, but is not limited to, examples. For example, the analysis unit can analyze heart rate fluctuations to estimate the user's mental state. The analysis unit can also analyze environmental information and grasp the atmosphere around the user. The generation unit generates EDM based on the information analyzed by the analysis unit. The generation can be performed, for example, using a music generation algorithm, but is not limited to, examples. For example, the generation unit generates an EDM with a fast tempo or an EDM with a slow tempo based on the heart rate. The generation unit can also generate an EDM that matches the atmosphere based on weather information. The playback unit plays the EDM generated by the generation unit. For example, the playback may play an EDM that matches the user's mental state, but this is not a limitation. For example, if the user is feeling stressed, the playback unit plays a relaxing EDM, and if the user is feeling energetic, the playback unit plays an energetic EDM. This allows the music generation system according to the embodiment to provide music that matches the user's mental state and the surrounding atmosphere.

[0030] The acquisition unit can collect ambient sounds, weather information, and location information. Examples of ambient sounds include, but are not limited to, environmental sounds, noises, and voices. The acquisition unit can collect ambient sounds using, for example, a microphone on the device. The acquisition unit can also collect weather information. Examples of weather information include, but are not limited to, temperature, humidity, and wind speed. The acquisition unit can acquire weather information from, for example, the Internet. The acquisition unit can also collect location information. Examples of location information include, but are not limited to, GPS data and Wi-Fi location information. The acquisition unit can acquire location information using, for example, a GPS. By collecting ambient environmental information, a more appropriate EDM can be generated.

[0031] The analysis unit can analyze the acquired biometric information or surrounding environmental information. Examples of environmental information include, but are not limited to, ambient sounds, weather information, and location information. For example, the analysis unit can analyze heart rate fluctuations to estimate the user's mental state. The analysis unit can also analyze the ambient environmental information to grasp the user's surrounding atmosphere. For example, the analysis unit can analyze the type and intensity of ambient sounds to grasp the user's surrounding environment. The analysis unit can also analyze weather information to grasp the user's surrounding weather conditions. For example, the analysis unit can analyze temperature and humidity fluctuations to grasp the user's surrounding weather conditions. By analyzing the biometric information and environmental information, a more accurate EDM can be generated.

[0032] The generator can generate an EDM based on the heart rate. Heart rate information can include, but is not limited to, a heart rate sensor, a data filtering method, and the like. For example, if the heart rate is fast, the generator can generate an EDM with a fast tempo. Also, if the heart rate is slow, the generator can generate an EDM with a slow tempo. For example, the generator can generate an EDM with a varying tempo based on fluctuations in the heart rate. This allows the user to be provided with music that matches their state by generating an EDM with a tempo that corresponds to their heart rate.

[0033] The generator can generate EDM based on weather information. Weather information includes, but is not limited to, temperature, humidity, wind speed, and the like. For example, the generator can generate EDM with a calm atmosphere on a rainy day. The generator can also generate EDM with a bright atmosphere on a sunny day. For example, the generator can generate EDM with a neutral atmosphere on a cloudy day. In this way, by generating EDM based on weather information, music that matches the surrounding atmosphere can be provided.

[0034] The playback unit can play EDM based on the user's mental state. Mental states include, but are not limited to, stress level and relaxation level. For example, if the user is feeling stressed, the playback unit can play relaxing EDM. Alternatively, if the user is feeling energetic, the playback unit can play energetic EDM. For example, the playback unit can adjust the tempo and rhythm of the EDM according to the user's mental state. This allows for stress relief and enhanced relaxation by playing EDM that matches the user's mental state.

[0035] The acquisition unit can analyze the user's past biometric information and select the optimal acquisition method. Past biometric information includes, but is not limited to, past heart rate data, past blood pressure data, and the like. For example, the acquisition unit can analyze the user's past heart rate data and acquire the biometric information at the most stable timing. The acquisition unit can also analyze the user's past walking sound data and select the optimal acquisition method according to the walking pattern. For example, the acquisition unit can select the optimal acquisition method for a specific time period based on the user's past biometric information. This makes it possible to acquire data more effectively by analyzing past biometric information.

[0036] When acquiring biometric information, the acquisition unit can perform filtering based on the user's current activity status and environment. Activity status includes, but is not limited to, for example, exercising, resting, etc. For example, when the user is exercising, the acquisition unit can acquire only biometric information related to exercise. Furthermore, when the user is resting, the acquisition unit can also acquire only biometric information related to a relaxed state. For example, when the user is working, the acquisition unit can also acquire only biometric information related to a state of concentration. This allows for more accurate data to be acquired by filtering according to the activity status and environment.

[0037] When acquiring biometric information, the acquisition unit can select the optimal acquisition means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the acquisition unit acquires the biometric information using voice recognition technology. Furthermore, when the user uses text input, the acquisition unit can also acquire the biometric information using text analysis technology. For example, when the user uses image input, the acquisition unit can also acquire the biometric information using image analysis technology. This improves the accuracy of data acquisition by selecting the optimal acquisition means depending on the input method.

[0038] When acquiring biometric information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, Wi-Fi location information, and the like. For example, when the user is in a park, the acquisition unit can prioritize acquiring biometric information related to a relaxed state. Furthermore, when the user is in an office, the acquisition unit can also prioritize acquiring biometric information related to a concentration state. For example, when the user is at home, the acquisition unit can prioritize acquiring biometric information related to a resting state. In this way, by taking into account the geographical location information, highly relevant information can be prioritized.

[0039] When acquiring biometric information, the acquisition unit can analyze the user's social media activity and acquire related information. Social media activity includes, but is not limited to, for example, the content of posts and the number of likes. For example, if the user posts on social media that they are feeling stressed, the acquisition unit can prioritize acquiring their heart rate. Furthermore, if the user posts on social media that they are feeling relaxed, the acquisition unit can prioritize acquiring their respiratory rate. For example, if the user posts on social media that they are feeling excited, the acquisition unit can prioritize acquiring their walking sound. In this way, related information can be acquired by analyzing social media activity.

[0040] When acquiring biometric information, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. Feedback includes, for example, the user's ratings, comments, etc., but is not limited to these examples. For example, if the user has requested acquisition of the heart rate in the past, the acquisition unit can prioritize acquisition of the heart rate. Furthermore, if the user has requested acquisition of the respiratory rate in the past, the acquisition unit can also prioritize acquisition of the respiratory rate. For example, if the user has requested acquisition of walking sounds in the past, the acquisition unit can also prioritize acquisition of walking sounds. In this way, the acquisition method can be customized by reflecting past feedback.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the biological information. The importance includes, but is not limited to, for example, the reliability and influence of the data. For example, the analysis unit performs a detailed analysis when the heart rate is high. Furthermore, the analysis unit can also perform a simplified analysis when the respiratory rate is stable. For example, the analysis unit can also perform a detailed analysis when the walking sound fluctuates. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the biological information.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of biological information. Categories include, but are not limited to, heart rate data, blood pressure data, and the like. For example, the analysis unit applies a heart rate variability analysis algorithm to the heart rate. The analysis unit can also apply a breathing pattern analysis algorithm to the breathing rate. For example, the analysis unit can also apply a walking pattern analysis algorithm to walking sounds. In this way, by applying an analysis algorithm depending on the category of biological information, the accuracy of the analysis is improved.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, past trends and past abnormality detection results. For example, the analysis unit can improve the accuracy of the current heart rate analysis by referring to the user's past heart rate analysis results. The analysis unit can also improve the accuracy of the current respiratory rate analysis by referring to the user's past respiratory rate analysis results. For example, the analysis unit can improve the accuracy of the current walking sound analysis by referring to the user's past walking sound analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time when the biological information was acquired. The acquisition time includes, but is not limited to, the latest data, past data, etc. For example, the analysis unit prioritizes analysis of recently acquired heart rate data. The analysis unit can also prioritize analysis of recently acquired respiratory rate data. For example, the analysis unit can also prioritize analysis of recently acquired walking sound data. In this way, by determining the priority of analysis based on the time when the biological information was acquired, the latest information can be prioritized for analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the biological information. Relevance includes, for example, correlation and causal relationship of data, but is not limited to such examples. For example, if there is a high relevance between the heart rate and the respiratory rate, the analysis unit prioritizes the analysis of the heart rate. Furthermore, if there is a high relevance between the walking sound and the heart rate, the analysis unit can also prioritize the analysis of the walking sound. For example, if there is a high relevance between the respiratory rate and the walking sound, the analysis unit can also prioritize the analysis of the respiratory rate. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the biological information.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and expert. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simple language. For example, the analysis unit can adjust the level of detail in the analysis results according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy to understand.

[0047] During generation, the generation unit can adjust the rhythm of the EDM based on heart rate fluctuations. Examples of rhythm include, but are not limited to, beats and rhythmic patterns. For example, if the heart rate is fast, the generation unit can generate an EDM with a fast rhythm. Furthermore, if the heart rate is slow, the generation unit can also generate an EDM with a slow rhythm. For example, if the heart rate fluctuates, the generation unit can generate an EDM with a fluctuating rhythm. By adjusting the rhythm of the EDM based on heart rate fluctuations, music suited to the user's state can be provided.

[0048] The generation unit can adjust the atmosphere of the EDM based on changes in weather information during generation. Examples of atmospheres include, but are not limited to, bright, dark, and relaxed. For example, the generation unit can generate an EDM with a calm atmosphere on a rainy day. The generation unit can also generate an EDM with a bright atmosphere on a sunny day. For example, the generation unit can generate an EDM with a neutral atmosphere on a cloudy day. By adjusting the atmosphere of the EDM based on changes in weather information, music that matches the surrounding atmosphere can be provided.

[0049] When generating EDM, the generation unit can select the style of EDM by referring to the user's past musical preferences. Music preferences include, but are not limited to, past playback history and user ratings. For example, the generation unit can generate EDM in a similar style based on a style of EDM that the user liked in the past. The generation unit can also generate EDM in a different style based on a style of EDM that the user avoided in the past. For example, the generation unit can analyze the user's past musical preferences and generate an EDM in an optimal style. This allows EDM to be generated that suits the user by referring to the user's past musical preferences.

[0050] The generation unit can adjust the instrumental composition of the EDM based on the type of surrounding sound during generation. The instrumental composition includes, for example, the type and number of instruments used, but is not limited to these examples. For example, if there are many natural sounds in the surroundings, the generation unit can generate an EDM that makes heavy use of acoustic instruments. Furthermore, if there are many mechanical sounds in the surroundings, the generation unit can also generate an EDM that makes heavy use of electronic sounds. For example, if there are many human voices in the surroundings, the generation unit can also generate an EDM that makes heavy use of vocals. This allows for more appropriate music to be provided by adjusting the instrumental composition based on the type of surrounding sound.

[0051] The generation unit can incorporate region-specific musical elements based on the location information during generation. Region-specific musical elements include, but are not limited to, folk music and regional traditional music. For example, if the user is in Africa, the generation unit can generate EDM incorporating traditional African rhythms. Also, if the user is in Asia, the generation unit can generate EDM incorporating traditional Asian instruments. For example, if the user is in Europe, the generation unit can generate EDM incorporating European classical music elements. In this way, by incorporating region-specific musical elements based on the location information, music tailored to the user can be provided.

[0052] The generator can adjust the energy level of the EDM based on the user's activity during generation. Energy levels include, but are not limited to, volume, beat strength, and the like. For example, the generator can generate high-energy EDM when the user is exercising. The generator can also generate low-energy EDM when the user is relaxing. For example, the generator can generate medium-energy EDM when the user is working. This allows the user to adjust the energy level of the EDM based on the user's activity, thereby providing more appropriate music.

[0053] During playback, the playback unit can optimize the playback order by referring to the user's past playback history. The playback order can be based on, but is not limited to, an algorithm, user preferences, or the like. For example, the playback unit may preferentially play back EDMs that the user has previously enjoyed playing. The playback unit can also place EDMs that the user has previously skipped later in the playback order. For example, the playback unit can analyze the user's past playback history and suggest an optimal playback order. By referring to the past playback history, the playback order can be tailored to the user.

[0054] During playback, the playback unit can select a playback mode based on the user's current activity status. Examples of playback modes include, but are not limited to, shuffle playback and continuous playback. For example, when the user is exercising, the playback unit can select an energetic playback mode. Furthermore, when the user is relaxing, the playback unit can select a relaxation mode. For example, when the user is working, the playback unit can select a concentration mode. By selecting a playback mode based on the user's current activity status, more appropriate music can be provided.

[0055] The playback unit can improve the playback method by reflecting user feedback during playback. Feedback includes, but is not limited to, user ratings and comments. For example, if the user adjusts the volume, the playback unit automatically adjusts the volume the next time playback is performed based on that feedback. Furthermore, if the user skips a specific EDM, the playback unit can also adjust the next playback order based on that feedback. For example, if the user changes the playback mode, the playback unit can automatically select the next playback mode based on that feedback. By reflecting feedback, the playback method can be improved and music tailored to the user can be provided.

[0056] The playback unit can provide optimal sound quality during playback by taking into account device information of the user. Device information includes, but is not limited to, for example, the type of speaker and the type of headphones. For example, if the user is using a smartphone, the playback unit can provide sound quality optimized for the smartphone. Also, if the user is using headphones, the playback unit can provide sound quality optimized for the headphones. For example, if the user is using speakers, the playback unit can provide sound quality optimized for the speakers. In this way, optimal sound quality can be provided by taking into account device information.

[0057] During playback, the playback unit can make EDM lyrics multilingual according to the user's language setting. Language settings include, but are not limited to, device language settings and user selection. The playback unit can automatically translate EDM lyrics based on the user's device language setting. The playback unit can also provide a language switching function when the user uses multiple languages. For example, if the user selects a specific language, the playback unit can provide EDM lyrics in that language. This allows the lyrics to be multilingual according to the language setting, thereby providing music that suits the user.

[0058] During playback, the playback unit can analyze the user's social media activity and suggest related EDMs. Social media activity includes, but is not limited to, for example, the content of posts and the number of likes. For example, the playback unit can suggest EDMs related to places where the user has checked in on social media. The playback unit can also analyze the content of the user's social media posts and suggest related EDMs. For example, the playback unit can suggest related EDMs based on the activities of the user's friends on social media. In this way, related EDMs can be suggested by analyzing social media activity.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The acquisition unit can analyze the user's past biometric information and select the optimal acquisition method. For example, it can analyze the user's past heart rate data and acquire the biometric information at the most stable timing. It can also analyze the user's past walking sound data and select the optimal acquisition method according to the walking pattern. Furthermore, it can select the optimal acquisition method for a specific time period based on the user's past biometric information. This makes it possible to acquire data more effectively by analyzing past biometric information.

[0061] The playback unit can improve the playback method by reflecting user feedback during playback. For example, if the user adjusts the volume, the volume can be automatically adjusted the next time the music is played based on that feedback. Also, if the user skips a specific EDM song, the next playback order can be adjusted based on that feedback. Furthermore, if the user changes the playback mode, the next playback mode can be automatically selected based on that feedback. In this way, by reflecting feedback, the playback method can be improved and music tailored to the user can be provided.

[0062] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the biological information. For example, if the heart rate is high, a detailed analysis can be performed. Also, if the respiratory rate is stable, a simplified analysis can be performed. Furthermore, if the walking sound fluctuates, a detailed analysis can be performed. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the biological information.

[0063] The generator can adjust the rhythm of the EDM based on heart rate fluctuations during generation. For example, if the heart rate is fast, an EDM with a fast rhythm can be generated. Conversely, if the heart rate is slow, an EDM with a slow rhythm can be generated. Furthermore, if the heart rate fluctuates, an EDM with a fluctuating rhythm can be generated. In this way, by adjusting the rhythm of the EDM based on heart rate fluctuations, music that suits the user's state can be provided.

[0064] The generator can adjust the atmosphere of the EDM based on changes in weather information during generation. For example, on a rainy day, it can generate an EDM with a calm atmosphere. On a sunny day, it can also generate an EDM with a bright atmosphere. Furthermore, on a cloudy day, it can generate an EDM with a neutral atmosphere. In this way, by adjusting the atmosphere of the EDM based on changes in weather information, it is possible to provide music that matches the surrounding atmosphere.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The acquisition unit acquires biometric information. Biometric information includes heart rate, blood pressure, body temperature, etc. The acquisition unit acquires the user's heart rate and walking sounds in real time using sensors in a wearable device or smart device. It can also collect ambient sounds, weather information, and location information. For example, ambient sounds can be collected using the device's microphone, weather information can be obtained from the Internet, and location information can be obtained using GPS. Step 2: The analysis unit analyzes the biometric information acquired by the acquisition unit. The analysis is performed using data preprocessing and analysis algorithms. For example, the analysis unit analyzes heart rate fluctuations to estimate the user's mental state. The analysis unit can also analyze surrounding environmental information to grasp the atmosphere around the user. Step 3: The generator generates EDM based on the information analyzed by the analyzer. This is done using a music generation algorithm. For example, a fast-tempo EDM or a slow-tempo EDM can be generated based on heart rate. It can also generate EDM that matches the mood based on weather information. Step 4: The playback unit plays the EDM generated by the generation unit. The playback unit plays EDM that matches the user's mental state. For example, if the user is feeling stressed, a relaxing EDM is played, and if the user is feeling energetic, an energetic EDM is played.

[0067] (Example 2) A music generation system according to an embodiment of the present invention recognizes sounds generated by the user and their surroundings, weather information, and the like, and automatically generates and plays electronic dance music (EDM) with a similar tempo and atmosphere. The music generation system acquires biometric information, such as the user's heart rate and walking sounds, using sensors, and also collects ambient sounds, weather information, and location information. A generation AI then analyzes this information and automatically generates EDM with a similar tempo and rhythm. The generated EDM is then played as a song that matches the user's mental state and the ambient atmosphere. For example, the music generation system acquires biometric information, such as the user's heart rate and walking sounds, using sensors. For example, sensors on a wearable device or smart device can acquire the user's heart rate and walking sounds in real time. The music generation system then collects ambient sounds, weather information, location information, and the like. For example, ambient sounds can be collected using a device's microphone, and weather information can be obtained from the Internet. Location information can also be acquired using GPS. The generation AI then analyzes the acquired biometric information and ambient environmental information to automatically generate EDM with a similar tempo and rhythm. For example, if the user's heart rate is fast, a fast-tempo EDM can be generated, and if the heart rate is slow, a slow-tempo EDM can be generated. Furthermore, music that matches the atmosphere can be generated based on ambient sounds and weather information. For example, a calm EDM can be generated on a rainy day, and a bright EDM can be generated on a sunny day. The generated EDM is played as a song that matches the user's mental state and the surrounding atmosphere. For example, if the user is feeling stressed, a relaxing EDM can be played, and if the user is feeling energetic, an energetic EDM can be played. This allows the music generation system to provide music that matches the user's mental state and the surrounding atmosphere. For example, if the user is feeling stressed, a relaxing EDM can be played, and if the user is feeling energetic, an energetic EDM can be played. This allows the user to enjoy their daily lives without feeling stressed.The music generation system is provided as an application, and can generate revenue through in-app purchases and advertising. Users can easily use the system using their smart devices. For example, by simply downloading the application and setting up the sensors, the system can automatically generate and play EDM music tailored to the user's state of mind.

[0068] A music generation system according to an embodiment includes an acquisition unit, an analysis unit, a generation unit, and a playback unit. The acquisition unit acquires biometric information. Examples of the biometric information include, but are not limited to, heart rate, blood pressure, and body temperature. The acquisition unit acquires the user's heart rate and walking sounds in real time using, for example, a sensor in a wearable device or a smart device. The acquisition unit can also collect ambient sounds, weather information, and location information. For example, ambient sounds can be collected using a device's microphone, and weather information can be acquired from the Internet. Location information can also be acquired using a GPS. The analysis unit analyzes the biometric information acquired by the acquisition unit. The analysis can be performed, for example, using data preprocessing or an analysis algorithm, but is not limited to, examples. For example, the analysis unit can analyze heart rate fluctuations to estimate the user's mental state. The analysis unit can also analyze environmental information and grasp the atmosphere around the user. The generation unit generates EDM based on the information analyzed by the analysis unit. The generation can be performed, for example, using a music generation algorithm, but is not limited to, examples. For example, the generation unit generates an EDM with a fast tempo or an EDM with a slow tempo based on the heart rate. The generation unit can also generate an EDM that matches the atmosphere based on weather information. The playback unit plays the EDM generated by the generation unit. For example, the playback may play an EDM that matches the user's mental state, but this is not a limitation. For example, if the user is feeling stressed, the playback unit plays a relaxing EDM, and if the user is feeling energetic, the playback unit plays an energetic EDM. This allows the music generation system according to the embodiment to provide music that matches the user's mental state and the surrounding atmosphere.

[0069] The acquisition unit can collect ambient sounds, weather information, and location information. Examples of ambient sounds include, but are not limited to, environmental sounds, noises, and voices. The acquisition unit can collect ambient sounds using, for example, a microphone on the device. The acquisition unit can also collect weather information. Examples of weather information include, but are not limited to, temperature, humidity, and wind speed. The acquisition unit can acquire weather information from, for example, the Internet. The acquisition unit can also collect location information. Examples of location information include, but are not limited to, GPS data and Wi-Fi location information. The acquisition unit can acquire location information using, for example, a GPS. By collecting ambient environmental information, a more appropriate EDM can be generated.

[0070] The analysis unit can analyze the acquired biometric information or surrounding environmental information. Examples of environmental information include, but are not limited to, ambient sounds, weather information, and location information. For example, the analysis unit can analyze heart rate fluctuations to estimate the user's mental state. The analysis unit can also analyze the ambient environmental information to grasp the user's surrounding atmosphere. For example, the analysis unit can analyze the type and intensity of ambient sounds to grasp the user's surrounding environment. The analysis unit can also analyze weather information to grasp the user's surrounding weather conditions. For example, the analysis unit can analyze temperature and humidity fluctuations to grasp the user's surrounding weather conditions. By analyzing the biometric information and environmental information, a more accurate EDM can be generated.

[0071] The generator can generate an EDM based on the heart rate. Heart rate information can include, but is not limited to, a heart rate sensor, a data filtering method, and the like. For example, if the heart rate is fast, the generator can generate an EDM with a fast tempo. Also, if the heart rate is slow, the generator can generate an EDM with a slow tempo. For example, the generator can generate an EDM with a varying tempo based on fluctuations in the heart rate. This allows the user to be provided with music that matches their state by generating an EDM with a tempo that corresponds to their heart rate.

[0072] The generator can generate EDM based on weather information. Weather information includes, but is not limited to, temperature, humidity, wind speed, and the like. For example, the generator can generate EDM with a calm atmosphere on a rainy day. The generator can also generate EDM with a bright atmosphere on a sunny day. For example, the generator can generate EDM with a neutral atmosphere on a cloudy day. In this way, by generating EDM based on weather information, music that matches the surrounding atmosphere can be provided.

[0073] The playback unit can play EDM based on the user's mental state. Mental states include, but are not limited to, stress level and relaxation level. For example, if the user is feeling stressed, the playback unit can play relaxing EDM. Alternatively, if the user is feeling energetic, the playback unit can play energetic EDM. For example, the playback unit can adjust the tempo and rhythm of the EDM according to the user's mental state. This allows for stress relief and enhanced relaxation by playing EDM that matches the user's mental state.

[0074] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring the biometric information based on the estimated user's emotion. Emotions include, but are not limited to, facial expression recognition and voice analysis. For example, if the user is feeling stressed, the acquisition unit acquires the biometric information at a time when the heart rate fluctuates greatly. Furthermore, if the user is relaxed, the acquisition unit can also acquire the biometric information at a time when the heart rate is stable. For example, if the user is excited, the acquisition unit can also acquire the biometric information at a time when the heart rate suddenly increases. This allows more appropriate data to be collected by adjusting the timing of acquiring the biometric information according to the user's emotion.

[0075] The acquisition unit can analyze the user's past biometric information and select the optimal acquisition method. Past biometric information includes, but is not limited to, past heart rate data, past blood pressure data, and the like. For example, the acquisition unit can analyze the user's past heart rate data and acquire the biometric information at the most stable timing. The acquisition unit can also analyze the user's past walking sound data and select the optimal acquisition method according to the walking pattern. For example, the acquisition unit can select the optimal acquisition method for a specific time period based on the user's past biometric information. This makes it possible to acquire data more effectively by analyzing past biometric information.

[0076] When acquiring biometric information, the acquisition unit can perform filtering based on the user's current activity status and environment. Activity status includes, but is not limited to, for example, exercising, resting, etc. For example, when the user is exercising, the acquisition unit can acquire only biometric information related to exercise. Furthermore, when the user is resting, the acquisition unit can also acquire only biometric information related to a relaxed state. For example, when the user is working, the acquisition unit can also acquire only biometric information related to a state of concentration. This allows for more accurate data to be acquired by filtering according to the activity status and environment.

[0077] When acquiring biometric information, the acquisition unit can select the optimal acquisition means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the acquisition unit acquires the biometric information using voice recognition technology. Furthermore, when the user uses text input, the acquisition unit can also acquire the biometric information using text analysis technology. For example, when the user uses image input, the acquisition unit can also acquire the biometric information using image analysis technology. This improves the accuracy of data acquisition by selecting the optimal acquisition means depending on the input method.

[0078] The acquisition unit can estimate the user's emotions and determine the priority of the biometric information to be acquired based on the estimated user's emotions. Examples of priorities include, but are not limited to, importance scores and urgency scores. For example, if the user is feeling stressed, the acquisition unit can prioritize acquiring the heart rate. Furthermore, if the user is relaxed, the acquisition unit can prioritize acquiring the respiratory rate. For example, if the user is excited, the acquisition unit can prioritize acquiring the walking sound. In this way, by determining the priority of the biometric information based on the user's emotions, important data can be acquired preferentially.

[0079] When acquiring biometric information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, Wi-Fi location information, and the like. For example, when the user is in a park, the acquisition unit can prioritize acquiring biometric information related to a relaxed state. Furthermore, when the user is in an office, the acquisition unit can also prioritize acquiring biometric information related to a concentration state. For example, when the user is at home, the acquisition unit can prioritize acquiring biometric information related to a resting state. In this way, by taking into account the geographical location information, highly relevant information can be prioritized.

[0080] When acquiring biometric information, the acquisition unit can analyze the user's social media activity and acquire related information. Social media activity includes, but is not limited to, for example, the content of posts and the number of likes. For example, if the user posts on social media that they are feeling stressed, the acquisition unit can prioritize acquiring their heart rate. Furthermore, if the user posts on social media that they are feeling relaxed, the acquisition unit can prioritize acquiring their respiratory rate. For example, if the user posts on social media that they are feeling excited, the acquisition unit can prioritize acquiring their walking sound. In this way, related information can be acquired by analyzing social media activity.

[0081] When acquiring biometric information, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. Feedback includes, for example, the user's ratings, comments, etc., but is not limited to these examples. For example, if the user has requested acquisition of the heart rate in the past, the acquisition unit can prioritize acquisition of the heart rate. Furthermore, if the user has requested acquisition of the respiratory rate in the past, the acquisition unit can also prioritize acquisition of the respiratory rate. For example, if the user has requested acquisition of walking sounds in the past, the acquisition unit can also prioritize acquisition of walking sounds. In this way, the acquisition method can be customized by reflecting past feedback.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. Examples of presentation methods include, but are not limited to, graph display, text display, and the like. For example, if the user is feeling stressed, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is excited, the analysis unit can provide visually stimulating analysis results. By adjusting the presentation method of the analysis based on the user's emotions, more appropriate analysis results can be provided.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the biological information. The importance includes, but is not limited to, for example, the reliability and influence of the data. For example, the analysis unit performs a detailed analysis when the heart rate is high. Furthermore, the analysis unit can also perform a simplified analysis when the respiratory rate is stable. For example, the analysis unit can also perform a detailed analysis when the walking sound fluctuates. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the biological information.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the category of biological information. Categories include, but are not limited to, heart rate data, blood pressure data, and the like. For example, the analysis unit applies a heart rate variability analysis algorithm to the heart rate. The analysis unit can also apply a breathing pattern analysis algorithm to the breathing rate. For example, the analysis unit can also apply a walking pattern analysis algorithm to walking sounds. In this way, by applying an analysis algorithm depending on the category of biological information, the accuracy of the analysis is improved.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, past trends and past abnormality detection results. For example, the analysis unit can improve the accuracy of the current heart rate analysis by referring to the user's past heart rate analysis results. The analysis unit can also improve the accuracy of the current respiratory rate analysis by referring to the user's past respiratory rate analysis results. For example, the analysis unit can improve the accuracy of the current walking sound analysis by referring to the user's past walking sound analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results.

[0086] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. Examples of the length include, but are not limited to, the analysis time and the length of the data. For example, if the user is feeling stressed, the analysis unit can perform a short and concise analysis. Alternatively, if the user is relaxed, the analysis unit can perform a detailed analysis. For example, if the user is excited, the analysis unit can perform a visually stimulating analysis. This allows for adjusting the length of the analysis based on the user's emotions to provide more appropriate analysis results.

[0087] During analysis, the analysis unit can determine the priority of analysis based on the time when the biological information was acquired. The acquisition time includes, but is not limited to, the latest data, past data, etc. For example, the analysis unit prioritizes analysis of recently acquired heart rate data. The analysis unit can also prioritize analysis of recently acquired respiratory rate data. For example, the analysis unit can also prioritize analysis of recently acquired walking sound data. In this way, by determining the priority of analysis based on the time when the biological information was acquired, the latest information can be prioritized for analysis.

[0088] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the biological information. Relevance includes, for example, correlation and causal relationship of data, but is not limited to such examples. For example, if there is a high relevance between the heart rate and the respiratory rate, the analysis unit prioritizes the analysis of the heart rate. Furthermore, if there is a high relevance between the walking sound and the heart rate, the analysis unit can also prioritize the analysis of the walking sound. For example, if there is a high relevance between the respiratory rate and the walking sound, the analysis unit can also prioritize the analysis of the respiratory rate. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the biological information.

[0089] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and expert. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simple language. For example, the analysis unit can adjust the level of detail in the analysis results according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy to understand.

[0090] The generation unit can estimate the user's emotions and adjust the tempo of the EDM to be generated based on the estimated user's emotions. Tempo includes, but is not limited to, beats per minute (BPM) and rhythm patterns. For example, if the user is feeling stressed, the generation unit can generate an EDM with a slow tempo. Alternatively, if the user is relaxed, the generation unit can generate an EDM with a medium tempo. For example, if the user is excited, the generation unit can generate an EDM with a fast tempo. This allows the user to adjust the tempo of the EDM based on their emotions, providing more appropriate music.

[0091] During generation, the generation unit can adjust the rhythm of the EDM based on heart rate fluctuations. Examples of rhythm include, but are not limited to, beats and rhythmic patterns. For example, if the heart rate is fast, the generation unit can generate an EDM with a fast rhythm. Furthermore, if the heart rate is slow, the generation unit can also generate an EDM with a slow rhythm. For example, if the heart rate fluctuates, the generation unit can generate an EDM with a fluctuating rhythm. By adjusting the rhythm of the EDM based on heart rate fluctuations, music suited to the user's state can be provided.

[0092] The generation unit can adjust the atmosphere of the EDM based on changes in weather information during generation. Examples of atmospheres include, but are not limited to, bright, dark, and relaxed. For example, the generation unit can generate an EDM with a calm atmosphere on a rainy day. The generation unit can also generate an EDM with a bright atmosphere on a sunny day. For example, the generation unit can generate an EDM with a neutral atmosphere on a cloudy day. By adjusting the atmosphere of the EDM based on changes in weather information, music that matches the surrounding atmosphere can be provided.

[0093] When generating EDM, the generation unit can select the style of EDM by referring to the user's past musical preferences. Music preferences include, but are not limited to, past playback history and user ratings. For example, the generation unit can generate EDM in a similar style based on a style of EDM that the user liked in the past. The generation unit can also generate EDM in a different style based on a style of EDM that the user avoided in the past. For example, the generation unit can analyze the user's past musical preferences and generate an EDM in an optimal style. This allows EDM to be generated that suits the user by referring to the user's past musical preferences.

[0094] The generation unit can estimate the user's emotions and adjust the length of the EDM to be generated based on the estimated user's emotions. Examples of the length include, but are not limited to, the length of the song or the length of the segment. For example, if the user is feeling stressed, the generation unit can generate a short EDM. Furthermore, if the user is relaxed, the generation unit can generate an EDM of a moderate length. For example, if the user is excited, the generation unit can generate a long EDM. By adjusting the length of the EDM based on the user's emotions, more appropriate music can be provided.

[0095] The generation unit can adjust the instrumental composition of the EDM based on the type of surrounding sound during generation. The instrumental composition includes, for example, the type and number of instruments used, but is not limited to these examples. For example, if there are many natural sounds in the surroundings, the generation unit can generate an EDM that makes heavy use of acoustic instruments. Furthermore, if there are many mechanical sounds in the surroundings, the generation unit can also generate an EDM that makes heavy use of electronic sounds. For example, if there are many human voices in the surroundings, the generation unit can also generate an EDM that makes heavy use of vocals. This allows for more appropriate music to be provided by adjusting the instrumental composition based on the type of surrounding sound.

[0096] The generation unit can incorporate region-specific musical elements based on the location information during generation. Region-specific musical elements include, but are not limited to, folk music and regional traditional music. For example, if the user is in Africa, the generation unit can generate EDM incorporating traditional African rhythms. Also, if the user is in Asia, the generation unit can generate EDM incorporating traditional Asian instruments. For example, if the user is in Europe, the generation unit can generate EDM incorporating European classical music elements. In this way, by incorporating region-specific musical elements based on the location information, music tailored to the user can be provided.

[0097] The generator can adjust the energy level of the EDM based on the user's activity during generation. Energy levels include, but are not limited to, volume, beat strength, and the like. For example, the generator can generate high-energy EDM when the user is exercising. The generator can also generate low-energy EDM when the user is relaxing. For example, the generator can generate medium-energy EDM when the user is working. This allows the user to adjust the energy level of the EDM based on the user's activity, thereby providing more appropriate music.

[0098] The playback unit can estimate the user's emotions and adjust the volume of the EDM to be played based on the estimated user's emotions. Volume includes, but is not limited to, decibels (dB), sound pressure levels, and the like. For example, the playback unit can set the volume low when the user is feeling stressed. The playback unit can also set the volume to a medium level when the user is relaxed. For example, the playback unit can set the volume high when the user is excited. By adjusting the volume based on the user's emotions, more appropriate music can be provided.

[0099] During playback, the playback unit can optimize the playback order by referring to the user's past playback history. The playback order can be based on, but is not limited to, an algorithm, user preferences, or the like. For example, the playback unit may preferentially play back EDMs that the user has previously enjoyed playing. The playback unit can also place EDMs that the user has previously skipped later in the playback order. For example, the playback unit can analyze the user's past playback history and suggest an optimal playback order. By referring to the past playback history, the playback order can be tailored to the user.

[0100] During playback, the playback unit can select a playback mode based on the user's current activity status. Examples of playback modes include, but are not limited to, shuffle playback and continuous playback. For example, when the user is exercising, the playback unit can select an energetic playback mode. Furthermore, when the user is relaxing, the playback unit can select a relaxation mode. For example, when the user is working, the playback unit can select a concentration mode. By selecting a playback mode based on the user's current activity status, more appropriate music can be provided.

[0101] The playback unit can improve the playback method by reflecting user feedback during playback. Feedback includes, but is not limited to, user ratings and comments. For example, if the user adjusts the volume, the playback unit automatically adjusts the volume the next time playback is performed based on that feedback. Furthermore, if the user skips a specific EDM, the playback unit can also adjust the next playback order based on that feedback. For example, if the user changes the playback mode, the playback unit can automatically select the next playback mode based on that feedback. By reflecting feedback, the playback method can be improved and music tailored to the user can be provided.

[0102] The playback unit can estimate the user's emotions and adjust the effects of the EDM to be played based on the estimated user's emotions. Effects include, but are not limited to, reverb and echo. For example, if the user is feeling stressed, the playback unit can play the music with less reverb. Alternatively, if the user is relaxed, the playback unit can play the music with more reverb. For example, if the user is excited, the playback unit can play the music with more distortion. This allows the user to adjust the effects based on their emotions, providing more appropriate music.

[0103] The playback unit can provide optimal sound quality during playback by taking into account device information of the user. Device information includes, but is not limited to, for example, the type of speaker and the type of headphones. For example, if the user is using a smartphone, the playback unit can provide sound quality optimized for the smartphone. Also, if the user is using headphones, the playback unit can provide sound quality optimized for the headphones. For example, if the user is using speakers, the playback unit can provide sound quality optimized for the speakers. In this way, optimal sound quality can be provided by taking into account device information.

[0104] During playback, the playback unit can make EDM lyrics multilingual according to the user's language setting. Language settings include, but are not limited to, device language settings and user selection. The playback unit can automatically translate EDM lyrics based on the user's device language setting. The playback unit can also provide a language switching function when the user uses multiple languages. For example, if the user selects a specific language, the playback unit can provide EDM lyrics in that language. This allows the lyrics to be multilingual according to the language setting, thereby providing music that suits the user.

[0105] During playback, the playback unit can analyze the user's social media activity and suggest related EDMs. Social media activity includes, but is not limited to, for example, the content of posts and the number of likes. For example, the playback unit can suggest EDMs related to places where the user has checked in on social media. The playback unit can also analyze the content of the user's social media posts and suggest related EDMs. For example, the playback unit can suggest related EDMs based on the activities of the user's friends on social media. In this way, related EDMs can be suggested by analyzing social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the acquisition unit, analysis unit, generation unit, and playback unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires biometric information using a sensor of the smart device 14 and collects ambient sounds using the device's microphone. The analysis unit analyzes the biometric information and environmental information acquired by the specific processing unit 290 of the data processing device 12. The generation unit generates an EDM based on the analysis results by the specific processing unit 290 of the data processing device 12. The playback unit plays the generated EDM using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, generation unit, and playback unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires biometric information using a sensor of the smart glasses 214 and collects ambient sounds using a microphone of the device. The analysis unit analyzes the biometric information and environmental information acquired by the specific processing unit 290 of the data processing device 12. The generation unit generates an EDM based on the analysis result by the specific processing unit 290 of the data processing device 12. The playback unit plays the generated EDM using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, generation unit, and playback unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit acquires biometric information using a sensor of the headset type terminal 314 and collects ambient sounds using a microphone of the device. The analysis unit analyzes the biometric information and environmental information acquired by the specific processing unit 290 of the data processing device 12. The generation unit generates an EDM based on the analysis results by the specific processing unit 290 of the data processing device 12. The playback unit plays the generated EDM using the speaker 240 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, analysis unit, generation unit, and playback unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires biometric information using a sensor of the robot 414 and collects ambient sounds using a microphone of the device. The analysis unit analyzes the biometric information and environmental information acquired by the specific processing unit 290 of the data processing device 12. The generation unit generates an EDM based on the analysis results by the specific processing unit 290 of the data processing device 12. The playback unit plays the generated EDM using the speaker 240 of the robot 414.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis of the heart rate can be prioritized. If the user is relaxed, the analysis of the breathing rate can be prioritized. Furthermore, if the user is excited, the analysis of the walking sound can be prioritized. In this way, by determining the priority of analysis based on the user's emotions, important data can be analyzed preferentially.

[0108] The acquisition unit can analyze the user's past biometric information and select the optimal acquisition method. For example, it can analyze the user's past heart rate data and acquire the biometric information at the most stable timing. It can also analyze the user's past walking sound data and select the optimal acquisition method according to the walking pattern. Furthermore, it can select the optimal acquisition method for a specific time period based on the user's past biometric information. This makes it possible to acquire data more effectively by analyzing past biometric information.

[0109] The generation unit can estimate the user's emotions and adjust the tempo of the EDM to be generated based on the estimated user emotions. For example, if the user is feeling stressed, a slow EDM can be generated. If the user is relaxed, a medium EDM can be generated. Furthermore, if the user is excited, a fast EDM can be generated. In this way, by adjusting the tempo of the EDM based on the user's emotions, more appropriate music can be provided.

[0110] The playback unit can estimate the user's emotions and adjust the volume of the EDM to be played based on the estimated user emotions. For example, if the user is feeling stressed, the volume can be set low. If the user is relaxed, the volume can be set to medium. Furthermore, if the user is excited, the volume can be set to high. This allows the system to provide more appropriate music by adjusting the volume based on the user's emotions.

[0111] The playback unit can improve the playback method by reflecting user feedback during playback. For example, if the user adjusts the volume, the volume can be automatically adjusted the next time the music is played based on that feedback. Also, if the user skips a specific EDM song, the next playback order can be adjusted based on that feedback. Furthermore, if the user changes the playback mode, the next playback mode can be automatically selected based on that feedback. In this way, by reflecting feedback, the playback method can be improved and music tailored to the user can be provided.

[0112] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring biometric information based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit can acquire biometric information at a time when the heart rate fluctuates greatly. If the user is relaxed, the acquisition unit can acquire biometric information at a time when the heart rate is stable. Furthermore, if the user is excited, the acquisition unit can acquire biometric information at a time when the heart rate is rising sharply. In this way, by adjusting the timing of acquiring biometric information according to the user's emotions, more appropriate data can be collected.

[0113] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the biological information. For example, if the heart rate is high, a detailed analysis can be performed. Also, if the respiratory rate is stable, a simplified analysis can be performed. Furthermore, if the walking sound fluctuates, a detailed analysis can be performed. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the biological information.

[0114] The generator can adjust the rhythm of the EDM based on heart rate fluctuations during generation. For example, if the heart rate is fast, an EDM with a fast rhythm can be generated. Conversely, if the heart rate is slow, an EDM with a slow rhythm can be generated. Furthermore, if the heart rate fluctuates, an EDM with a fluctuating rhythm can be generated. In this way, by adjusting the rhythm of the EDM based on heart rate fluctuations, music that suits the user's state can be provided.

[0115] The generator can adjust the atmosphere of the EDM based on changes in weather information during generation. For example, on a rainy day, it can generate an EDM with a calm atmosphere. On a sunny day, it can also generate an EDM with a bright atmosphere. Furthermore, on a cloudy day, it can generate an EDM with a neutral atmosphere. In this way, by adjusting the atmosphere of the EDM based on changes in weather information, it is possible to provide music that matches the surrounding atmosphere.

[0116] The playback unit can estimate the user's emotions and adjust the EDM effects it plays based on the estimated user emotions. For example, if the user is feeling stressed, it can play the music with less reverb. If the user is relaxed, it can play the music with more reverb. Furthermore, if the user is excited, it can play the music with more distortion. This allows it to provide more appropriate music by adjusting the effects based on the user's emotions.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The acquisition unit acquires biometric information. Biometric information includes heart rate, blood pressure, body temperature, etc. The acquisition unit acquires the user's heart rate and walking sounds in real time using sensors in a wearable device or smart device. It can also collect ambient sounds, weather information, and location information. For example, ambient sounds can be collected using the device's microphone, weather information can be obtained from the Internet, and location information can be obtained using GPS. Step 2: The analysis unit analyzes the biometric information acquired by the acquisition unit. The analysis is performed using data preprocessing and analysis algorithms. For example, the analysis unit analyzes heart rate fluctuations to estimate the user's mental state. The analysis unit can also analyze surrounding environmental information to grasp the atmosphere around the user. Step 3: The generator generates EDM based on the information analyzed by the analyzer. This is done using a music generation algorithm. For example, a fast-tempo EDM or a slow-tempo EDM can be generated based on heart rate. It can also generate EDM that matches the mood based on weather information. Step 4: The playback unit plays the EDM generated by the generation unit. The playback unit plays EDM that matches the user's mental state. For example, if the user is feeling stressed, a relaxing EDM is played, and if the user is feeling energetic, an energetic EDM is played.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] 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.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit for acquiring biometric information; an analysis unit that analyzes the biometric information acquired by the acquisition unit; a generation unit that generates an EDM based on the information analyzed by the analysis unit; a playback unit that plays back the EDM generated by the generation unit. A system characterized by:

2. The acquisition unit Collecting surrounding sound, weather information, and location information 2. The system of claim 1.

3. The analysis unit Analyze the acquired biometric information or surrounding environmental information 2. The system of claim 1.

4. The generation unit Generate EDM based on your heart rate 2. The system of claim 1.

5. The generation unit Generate EDM based on weather information 2. The system of claim 1.

6. The playback unit Playing EDM based on the user's state of mind 2. The system of claim 1.

7. The acquisition unit The system estimates the user's emotions and adjusts the timing of acquiring biometric information based on the estimated user emotions.

2. The system of claim 1.

8. The acquisition unit Analyze the user's past biometric information and select the optimal acquisition method 2. The system of claim 1.

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