System

Abstract

An object of a system according to an embodiment is to provide music in accordance with a user's emotion in real time.SOLUTION: A system includes a data collection part, an emotion analysis part, and a music generation part. The data collection unit collects voice or physical information data measured by the smart watch. The emotion analysis unit performs emotion analysis based on the data collected by the data collection unit. The music generation unit generates music on the basis of the result analyzed by the emotion analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to provide music that responds to a user's emotions in real time.

[0005] The system according to the embodiment aims to provide music in real time according to the user's emotions. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an emotion analysis unit, and a music generation unit. The data collection unit collects voice and physical information data measured by the smartwatch. The emotion analysis unit performs emotion analysis based on the data collected by the data collection unit. The music generation unit generates music based on the results of the analysis by the emotion analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide music in real time according to the user's emotions. [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 generates music in real time according to a user's emotions, enriching the user's life. This music generation system collects voice and physical information data measured by a smartwatch, and a generation AI performs emotion analysis, generating music based on the analysis results. This allows the music generation system to generate music in real time according to the user's emotions, enriching the user's life.

[0029] A music generation system according to an embodiment includes a data collection unit, an emotion analysis unit, and a music generation unit. The data collection unit collects voice and physical information data measured by a smartwatch. For example, the data collection unit uses sensors installed in the smartwatch to collect data such as heart rate, body temperature, and activity level in real time. The data collection unit can also collect a user's voice and store the data as voice data. The emotion analysis unit performs emotion analysis based on the data collected by the data collection unit. For example, the emotion analysis unit analyzes voice tone and heart rate fluctuations to identify the user's current emotional state. The emotion analysis unit can also estimate emotions from the collected data using a machine learning algorithm. The music generation unit generates music based on the results of the analysis by the emotion analysis unit. For example, the music generation unit uses a generation AI to generate music that best matches the user's emotions. The music generation unit can also generate music that matches the user's preferences and culture. As a result, the music generation system according to an embodiment can generate music that matches the user's emotions in real time, enriching the user's life.

[0030] The data collection unit can increase the number of sensors installed in the smartwatch to also collect data on electrodermal activity or blood oxygen levels. For example, the data collection unit can add an electrodermal activity sensor to the smartwatch to measure the user's stress level in real time. This improves the accuracy of emotional analysis and enables the provision of more appropriate music. The data collection unit can also install a blood oxygen level sensor in the smartwatch to obtain a detailed understanding of the user's health condition. For example, if a decrease in oxygen levels is detected, relaxing music can be provided. Furthermore, the data collection unit can integrate multiple sensors into the smartwatch to simultaneously collect data such as heart rate, body temperature, electrodermal activity, and blood oxygen levels. This enables the user's emotional state to be analyzed from multiple angles. This improves the accuracy of emotional analysis and enables the provision of more appropriate music.

[0031] The data collection unit can learn the user's lifestyle patterns and collect data according to specific time periods and activities. For example, the data collection unit, such as a smartwatch, learns the user's lifestyle patterns and records emotional fluctuations during specific time periods. For example, it focuses on collecting data during the morning commute and evening relaxation time. The data collection unit also analyzes the user's activity log and collects data according to different activities, such as during exercise and rest. This allows for a detailed understanding of emotional fluctuations for each activity. Furthermore, the data collection unit allows the smartwatch to learn the user's lifestyle rhythm and collect data according to specific events (e.g., meetings and meals). This allows for an accurate capture of emotional fluctuations for each event. This allows for a more accurate understanding of emotional fluctuations.

[0032] The data collection unit can use the emotion estimation function to collect emotional data while the user is listening to specific music. For example, the smartwatch monitors the user's heart rate and electrodermal activity while listening to music to collect emotional data. This allows for a detailed analysis of the impact of music. The data collection unit also collects emotional data while the user is listening to specific music, and the generation AI uses this data to improve the music generation algorithm. For example, elements of music that have a high relaxing effect can be enhanced. Furthermore, the data collection unit records the user's emotional state in real time while the smartwatch is playing music and feeds that data back to the generation AI. This enables more accurate emotional response in the next music generation. This improves the accuracy of music generation.

[0033] The data collection unit can also collect data from wearable devices other than a smartwatch and integrate information from multiple devices. The data collection unit, for example, uses smart glasses to monitor the user's gaze and pupil movement and collect emotion data. This enables emotion analysis based on visual information. The data collection unit also uses a smart ring to measure the user's electrodermal activity and body temperature at their fingertips and collect emotion data. This enables emotion analysis based on information at hand. Furthermore, the data collection unit integrates data collected from multiple wearable devices, such as a smartwatch, smart glasses, and smart ring, to perform comprehensive emotion analysis. This enables more accurate emotion estimation.

[0034] The data collection unit can also connect with smart home devices to collect environmental data such as room temperature or lighting conditions. For example, the data collection unit connects with smart home devices to collect environmental data such as room temperature, humidity, and lighting brightness. This allows for analysis of the impact of environmental factors on a user's emotions. The data collection unit also inputs the environmental data collected from smart home devices into the generation AI and uses it for emotion analysis. For example, if the room temperature is high, relaxing music can be played. Furthermore, the data collection unit connects with smart home devices to build a system that monitors the user's entire living environment. This enables comprehensive emotion analysis that integrates environmental data and physical information data. This allows for environmental data to be used for emotion analysis.

[0035] The data collection unit may develop a smartwatch app equipped with an emotion estimation function and add a function that allows users to manually record emotions. For example, the data collection unit may equip the smartwatch app with an emotion estimation function and allow users to manually record emotions. For example, the data collection unit may provide an interface for selecting emotional states. The data collection unit may also feed back the emotion data manually recorded by the user to the generation AI to improve the accuracy of emotion analysis. For example, the emotion estimation algorithm may be improved based on the manually recorded data. Furthermore, the data collection unit may add an emotion recording function to the smartwatch app, allowing users to record daily emotional fluctuations. This enables analysis based on long-term emotion data. This may improve the accuracy of the data.

[0036] When analyzing voice data, the emotion analysis unit can analyze detailed characteristics of the voice, such as intonation, speed, and pauses. For example, when the generation AI analyzes voice data, the emotion analysis unit analyzes detailed characteristics such as the voice intonation, speed, and pauses to detect subtle changes in emotion. For example, it can identify emotional fluctuations based on changes in voice tone. In addition, when analyzing voice data, the emotion analysis unit analyzes the voice strength and rhythm in detail to detect subtle changes in emotion. For example, it can estimate stress levels based on changes in voice strength. Furthermore, when the generation AI analyzes voice data, the emotion analysis unit analyzes the voice strength and tempo in detail to detect subtle changes in emotion. For example, a fast speaking tempo can indicate an excited state. This makes it possible to detect subtle changes in emotion.

[0037] The emotion analysis unit can perform multimodal emotion analysis by combining physical information data and voice data. For example, the generation AI combines and analyzes physical information data and voice data to perform multimodal emotion analysis. For example, it can simultaneously analyze heart rate and voice tone to improve the accuracy of emotion estimation. The emotion analysis unit also integrates physical information data and voice data, allowing the generation AI to detect subtle changes in emotion. For example, it can combine and analyze fluctuations in body temperature and voice intonation. Furthermore, to perform multimodal emotion analysis, the generation AI integrates multiple data sources to improve the accuracy of emotion estimation. For example, it can simultaneously analyze electrodermal activity and voice speed. This enables more accurate emotion estimation.

[0038] The emotion analysis unit can use the emotion estimation function to learn patterns of emotions felt by a user in specific situations. For example, the generation AI can use the emotion estimation function to learn patterns of emotions felt by a user in specific situations. For example, the emotion analysis unit can analyze emotional patterns during a meeting or while exercising. The emotion analysis unit also learns the user's emotional patterns, allowing the generation AI to improve the prediction accuracy of emotion estimation. For example, it can learn emotional fluctuations during specific time periods or activities. Furthermore, the emotion analysis unit can use the emotion estimation function to learn patterns of emotions felt by a user in specific situations in real time, improving prediction accuracy. For example, it can learn stressful situations and relaxed situations. This can improve the prediction accuracy of emotion estimation.

[0039] The emotion analysis unit can feed back the results of the emotion analysis to the user's smartphone or tablet in real time. For example, the emotion analysis unit feeds back the results of the emotion analysis to the user's smartphone in real time, allowing the user to understand their own emotional state. For example, an app that displays an emotion score can be provided. The emotion analysis unit can also display the results of the emotion analysis on the user's tablet in real time, allowing the user to visually understand their emotional state. For example, it can display emotional fluctuations in a graph. Furthermore, the emotion analysis unit can notify the results of the emotion analysis to the smartphone or tablet, allowing the user to check their emotional state in real time. For example, an alert function that notifies of changes in emotions can be added. This allows the user to understand their own emotional state.

[0040] The emotion analysis unit can link the results of the emotion analysis with the user's social media account and suggest content to post based on the emotion. The emotion analysis unit, for example, links the results of the emotion analysis with the user's social media account and suggests content to post based on the emotion. For example, it can suggest encouraging messages when the emotion is positive. The emotion analysis unit also builds a system that automatically generates content to post on social media based on the user's emotional state. For example, it can provide a post template based on the emotion score. Furthermore, the emotion analysis unit reflects the results of the emotion analysis in the social media account and suggests content to post based on the emotion. For example, it can suggest relaxation methods when the emotion is negative. This makes it possible to suggest content to post based on the emotion.

[0041] The emotion analysis unit can use the emotion estimation function to track changes in a user's emotions after listening to specific music and reflect that data in the next music generation. For example, the emotion analysis unit can use the emotion estimation function to track changes in a user's emotions after listening to specific music. For example, it can compare emotion scores before and after music playback. The emotion analysis unit can also collect emotion data after a user listens to music and reflect that data in the next music generation. For example, it can enhance elements of music that have a high relaxing effect. Furthermore, the emotion analysis unit can use the emotion estimation function to monitor changes in a user's emotions in real time after listening to specific music and reflect that data in the next music generation. For example, it can enhance elements of music that induce positive emotional changes. This enables more accurate emotional response in the next music generation.

[0042] The music generation unit can refer to the user's past music history and reflect their preferences. For example, the generation AI of the music generation unit analyzes the user's past music history and generates music that reflects their preferences. For example, it can incorporate elements of genres and artists that have been played frequently in the past. The music generation unit also learns preferences based on the user's music history and reflects them in the next music generation. For example, it can enhance elements of a user who prefers a particular tempo or rhythm. Furthermore, the music generation unit automatically generates a playlist that reflects the user's preferences by referencing the user's music history. For example, it can provide new music that incorporates elements of songs that have been highly rated in the past. This makes it possible to generate music that suits the user's preferences.

[0043] The music generation unit can provide music that corresponds to the user's current activity. For example, the generation AI of the music generation unit analyzes the user's current activity and generates music that corresponds to that activity. For example, fast-tempo music can be provided during exercise. The music generation unit also generates music that is optimal for the current activity based on the user's activity log. For example, music that increases concentration can be provided while working. Furthermore, the music generation unit monitors the user's current activity in real time and provides music that corresponds to that activity. For example, calming music can be provided during relaxation time. This makes it possible to provide music that is optimal for the user's current activity.

[0044] The music generation unit can use the emotion estimation function to monitor emotional changes in real time while the user is listening to music and adjust the music as needed. For example, the music generation unit can use the emotion estimation function to monitor emotional changes in real time while the user is listening to music. For example, the music can be changed if the emotion score drops. The music generation unit also monitors the user's emotional changes in real time, and the generation AI adjusts the music as needed. For example, more calming music can be provided if the relaxing effect is low. Furthermore, the music generation unit can use the emotion estimation function to analyze emotional changes in real time while the user is listening to music, and the generation AI dynamically adjusts the music. For example, the music can be changed to enhance positive emotions. This makes it possible to dynamically adjust the music in response to the user's emotional changes.

[0045] The music generation unit can link with the user's smart home devices and play the generated music from speakers in the room. For example, the music generation unit can link with smart home devices to build a system in which music generated by the generation AI is played from speakers in the room. For example, the music generation unit can automatically send music to a smart speaker. The music generation unit can also link with the user's smart home devices to play music generated by the generation AI throughout the room. For example, it can play music by synchronizing multiple speakers. Furthermore, the music generation unit can link with smart home devices to play music generated by the generation AI from speakers in the room in response to a user's instructions. For example, music can be played by voice command. This allows the generated music to be played throughout the room.

[0046] The music generation unit can add a function that allows the user to share the generated music with their friends and family. For example, the music generation unit can add a function that allows the user to share the music generated by the generation AI with their friends and family. For example, it can generate a link for sharing music. The music generation unit also builds a system that allows the user to share the music generated by the generation AI with their friends and family in real time. For example, it can provide a function for playing music simultaneously. Furthermore, the music generation unit can share the music generated by the generation AI with the user's friends and family, allowing them to share a musical experience that is tailored to their emotions. For example, it can add a function that recommends music based on an emotional score. This allows them to share a musical experience that is tailored to their emotions.

[0047] The music generation unit can use the emotion estimation function to analyze changes in a user's emotions after listening to specific music, and improve the new music generation algorithm based on that data. For example, the music generation unit can use the emotion estimation function to analyze changes in a user's emotions after listening to specific music. For example, it can compare emotion scores before and after music playback. The music generation unit also collects emotion data after a user listens to music, and the generation AI improves the new music generation algorithm based on that data. For example, it can enhance elements of music that have a high relaxing effect. Furthermore, the music generation unit can use the emotion estimation function to monitor changes in a user's emotions in real time after listening to specific music, and the generation AI improves the new music generation algorithm based on that data. For example, it can enhance elements of music that induce positive emotional changes. This improves the new music generation algorithm.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The music generation system can further include a history learning unit that automatically learns the user's preferred music genres based on the user's past music history. For example, the history learning unit can analyze the genres and artists of music played by the user in the past to identify the user's preferences. The history learning unit can also learn the user's preference trends based on the number of times the user plays and their ratings. Furthermore, the history learning unit can reflect the user's music history in the next music generation. This makes it possible to provide music that suits the user's preferences.

[0050] The music generation system may further include an activity analysis unit that generates music based on the user's current activity. For example, the activity analysis unit may detect that the user is exercising and provide fast-paced music. Alternatively, the activity analysis unit may detect that the user is working and provide music that promotes concentration. Alternatively, the activity analysis unit may detect that the user is relaxing and provide calming music. This allows the system to provide music that is optimal for the user's current activity.

[0051] The music generation system may further include a sharing unit for sharing music with the user's friends and family. For example, the sharing unit may generate a link for sharing the generated music with friends and family. The sharing unit may also provide a function for sharing music in real time. The sharing unit may also provide a function for sharing a musical experience according to an emotion. This allows the user to share music with friends and family and enjoy a musical experience according to an emotion.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The data collection unit collects voice and physical information data measured by the smartwatch. For example, the data collection unit uses sensors installed in the smartwatch to collect data such as heart rate, body temperature, and activity level in real time. The data collection unit can also collect the user's voice and save it as voice data. Step 2: The sentiment analysis unit performs sentiment analysis based on the data collected by the data collection unit. For example, the sentiment analysis unit analyzes voice tone and heart rate fluctuations to identify the user's current emotional state. The sentiment analysis unit can also use machine learning algorithms to infer emotions from the collected data. Step 3: The music generation unit generates music based on the results of the analysis by the emotion analysis unit. For example, the music generation unit uses a generative AI to generate music that best suits the user's emotions. The music generation unit can also generate music that suits the user's preferences and culture.

[0054] (Example 2) A music generation system according to an embodiment of the present invention generates music in real time according to a user's emotions, enriching the user's life. This music generation system collects voice and physical information data measured by a smartwatch, and a generation AI performs emotion analysis, generating music based on the analysis results. This allows the music generation system to generate music in real time according to the user's emotions, enriching the user's life.

[0055] A music generation system according to an embodiment includes a data collection unit, an emotion analysis unit, and a music generation unit. The data collection unit collects voice and physical information data measured by a smartwatch. For example, the data collection unit uses sensors installed in the smartwatch to collect data such as heart rate, body temperature, and activity level in real time. The data collection unit can also collect a user's voice and store the data as voice data. The emotion analysis unit performs emotion analysis based on the data collected by the data collection unit. For example, the emotion analysis unit analyzes voice tone and heart rate fluctuations to identify the user's current emotional state. The emotion analysis unit can also estimate emotions from the collected data using a machine learning algorithm. The music generation unit generates music based on the results of the analysis by the emotion analysis unit. For example, the music generation unit uses a generation AI to generate music that best matches the user's emotions. The music generation unit can also generate music that matches the user's preferences and culture. As a result, the music generation system according to an embodiment can generate music that matches the user's emotions in real time, enriching the user's life.

[0056] The data collection unit can increase the number of sensors installed in the smartwatch to also collect data on electrodermal activity or blood oxygen levels. For example, the data collection unit can add an electrodermal activity sensor to the smartwatch to measure the user's stress level in real time. This improves the accuracy of emotional analysis and enables the provision of more appropriate music. The data collection unit can also install a blood oxygen level sensor in the smartwatch to obtain a detailed understanding of the user's health condition. For example, if a decrease in oxygen levels is detected, relaxing music can be provided. Furthermore, the data collection unit can integrate multiple sensors into the smartwatch to simultaneously collect data such as heart rate, body temperature, electrodermal activity, and blood oxygen levels. This enables the user's emotional state to be analyzed from multiple angles. This improves the accuracy of emotional analysis and enables the provision of more appropriate music.

[0057] The data collection unit can learn the user's lifestyle patterns and collect data according to specific time periods and activities. For example, the data collection unit, such as a smartwatch, learns the user's lifestyle patterns and records emotional fluctuations during specific time periods. For example, it focuses on collecting data during the morning commute and evening relaxation time. The data collection unit also analyzes the user's activity log and collects data according to different activities, such as during exercise and rest. This allows for a detailed understanding of emotional fluctuations for each activity. Furthermore, the data collection unit allows the smartwatch to learn the user's lifestyle rhythm and collect data according to specific events (e.g., meetings and meals). This allows for an accurate capture of emotional fluctuations for each event. This allows for a more accurate understanding of emotional fluctuations.

[0058] The data collection unit can use the emotion estimation function to collect emotional data while the user is listening to specific music. For example, the smartwatch monitors the user's heart rate and electrodermal activity while listening to music to collect emotional data. This allows for a detailed analysis of the impact of music. The data collection unit also collects emotional data while the user is listening to specific music, and the generation AI uses this data to improve the music generation algorithm. For example, elements of music that have a high relaxing effect can be enhanced. Furthermore, the data collection unit records the user's emotional state in real time while the smartwatch is playing music and feeds that data back to the generation AI. This enables more accurate emotional response in the next music generation. This improves the accuracy of music generation.

[0059] The data collection unit can also collect data from wearable devices other than a smartwatch and integrate information from multiple devices. The data collection unit, for example, uses smart glasses to monitor the user's gaze and pupil movement and collect emotion data. This enables emotion analysis based on visual information. The data collection unit also uses a smart ring to measure the user's electrodermal activity and body temperature at their fingertips and collect emotion data. This enables emotion analysis based on information at hand. Furthermore, the data collection unit integrates data collected from multiple wearable devices, such as a smartwatch, smart glasses, and smart ring, to perform comprehensive emotion analysis. This enables more accurate emotion estimation.

[0060] The data collection unit can also connect with smart home devices to collect environmental data such as room temperature or lighting conditions. For example, the data collection unit connects with smart home devices to collect environmental data such as room temperature, humidity, and lighting brightness. This allows for analysis of the impact of environmental factors on a user's emotions. The data collection unit also inputs the environmental data collected from smart home devices into the generation AI and uses it for emotion analysis. For example, if the room temperature is high, relaxing music can be played. Furthermore, the data collection unit connects with smart home devices to build a system that monitors the user's entire living environment. This enables comprehensive emotion analysis that integrates environmental data and physical information data. This allows for environmental data to be used for emotion analysis.

[0061] The data collection unit may develop a smartwatch app equipped with an emotion estimation function and add a function that allows users to manually record emotions. For example, the data collection unit may equip the smartwatch app with an emotion estimation function and allow users to manually record emotions. For example, the data collection unit may provide an interface for selecting emotional states. The data collection unit may also feed back the emotion data manually recorded by the user to the generation AI to improve the accuracy of emotion analysis. For example, the emotion estimation algorithm may be improved based on the manually recorded data. Furthermore, the data collection unit may add an emotion recording function to the smartwatch app, allowing users to record daily emotional fluctuations. This enables analysis based on long-term emotion data. This may improve the accuracy of the data.

[0062] When analyzing voice data, the emotion analysis unit can analyze detailed characteristics of the voice, such as intonation, speed, and pauses. For example, when the generation AI analyzes voice data, the emotion analysis unit analyzes detailed characteristics such as the voice intonation, speed, and pauses to detect subtle changes in emotion. For example, it can identify emotional fluctuations based on changes in voice tone. In addition, when analyzing voice data, the emotion analysis unit analyzes the voice strength and rhythm in detail to detect subtle changes in emotion. For example, it can estimate stress levels based on changes in voice strength. Furthermore, when the generation AI analyzes voice data, the emotion analysis unit analyzes the voice strength and tempo in detail to detect subtle changes in emotion. For example, a fast speaking tempo can indicate an excited state. This makes it possible to detect subtle changes in emotion.

[0063] The emotion analysis unit can perform multimodal emotion analysis by combining physical information data and voice data. For example, the generation AI combines and analyzes physical information data and voice data to perform multimodal emotion analysis. For example, it can simultaneously analyze heart rate and voice tone to improve the accuracy of emotion estimation. The emotion analysis unit also integrates physical information data and voice data, allowing the generation AI to detect subtle changes in emotion. For example, it can combine and analyze fluctuations in body temperature and voice intonation. Furthermore, to perform multimodal emotion analysis, the generation AI integrates multiple data sources to improve the accuracy of emotion estimation. For example, it can simultaneously analyze electrodermal activity and voice speed. This enables more accurate emotion estimation.

[0064] The emotion analysis unit can use the emotion estimation function to learn patterns of emotions felt by a user in specific situations. For example, the generation AI can use the emotion estimation function to learn patterns of emotions felt by a user in specific situations. For example, the emotion analysis unit can analyze emotional patterns during a meeting or while exercising. The emotion analysis unit also learns the user's emotional patterns, allowing the generation AI to improve the prediction accuracy of emotion estimation. For example, it can learn emotional fluctuations during specific time periods or activities. Furthermore, the emotion analysis unit can use the emotion estimation function to learn patterns of emotions felt by a user in specific situations in real time, improving prediction accuracy. For example, it can learn stressful situations and relaxed situations. This can improve the prediction accuracy of emotion estimation.

[0065] The emotion analysis unit can feed back the results of the emotion analysis to the user's smartphone or tablet in real time. For example, the emotion analysis unit feeds back the results of the emotion analysis to the user's smartphone in real time, allowing the user to understand their own emotional state. For example, an app that displays an emotion score can be provided. The emotion analysis unit can also display the results of the emotion analysis on the user's tablet in real time, allowing the user to visually understand their emotional state. For example, it can display emotional fluctuations in a graph. Furthermore, the emotion analysis unit can notify the results of the emotion analysis to the smartphone or tablet, allowing the user to check their emotional state in real time. For example, an alert function that notifies of changes in emotions can be added. This allows the user to understand their own emotional state.

[0066] The emotion analysis unit can link the results of the emotion analysis with the user's social media account and suggest content to post based on the emotion. The emotion analysis unit, for example, links the results of the emotion analysis with the user's social media account and suggests content to post based on the emotion. For example, it can suggest encouraging messages when the emotion is positive. The emotion analysis unit also builds a system that automatically generates content to post on social media based on the user's emotional state. For example, it can provide a post template based on the emotion score. Furthermore, the emotion analysis unit reflects the results of the emotion analysis in the social media account and suggests content to post based on the emotion. For example, it can suggest relaxation methods when the emotion is negative. This makes it possible to suggest content to post based on the emotion.

[0067] The emotion analysis unit can use the emotion estimation function to track changes in a user's emotions after listening to specific music and reflect that data in the next music generation. For example, the emotion analysis unit can use the emotion estimation function to track changes in a user's emotions after listening to specific music. For example, it can compare emotion scores before and after music playback. The emotion analysis unit can also collect emotion data after a user listens to music and reflect that data in the next music generation. For example, it can enhance elements of music that have a high relaxing effect. Furthermore, the emotion analysis unit can use the emotion estimation function to monitor changes in a user's emotions in real time after listening to specific music and reflect that data in the next music generation. For example, it can enhance elements of music that induce positive emotional changes. This enables more accurate emotional response in the next music generation.

[0068] The music generation unit can refer to the user's past music history and reflect their preferences. For example, the generation AI of the music generation unit analyzes the user's past music history and generates music that reflects their preferences. For example, it can incorporate elements of genres and artists that have been played frequently in the past. The music generation unit also learns preferences based on the user's music history and reflects them in the next music generation. For example, it can enhance elements of a user who prefers a particular tempo or rhythm. Furthermore, the music generation unit automatically generates a playlist that reflects the user's preferences by referencing the user's music history. For example, it can provide new music that incorporates elements of songs that have been highly rated in the past. This makes it possible to generate music that suits the user's preferences.

[0069] The music generation unit can provide music that corresponds to the user's current activity. For example, the generation AI of the music generation unit analyzes the user's current activity and generates music that corresponds to that activity. For example, fast-tempo music can be provided during exercise. The music generation unit also generates music that is optimal for the current activity based on the user's activity log. For example, music that increases concentration can be provided while working. Furthermore, the music generation unit monitors the user's current activity in real time and provides music that corresponds to that activity. For example, calming music can be provided during relaxation time. This makes it possible to provide music that is optimal for the user's current activity.

[0070] The music generation unit can use the emotion estimation function to monitor emotional changes in real time while the user is listening to music and adjust the music as needed. For example, the music generation unit can use the emotion estimation function to monitor emotional changes in real time while the user is listening to music. For example, the music can be changed if the emotion score drops. The music generation unit also monitors the user's emotional changes in real time, and the generation AI adjusts the music as needed. For example, more calming music can be provided if the relaxing effect is low. Furthermore, the music generation unit can use the emotion estimation function to analyze emotional changes in real time while the user is listening to music, and the generation AI dynamically adjusts the music. For example, the music can be changed to enhance positive emotions. This makes it possible to dynamically adjust the music in response to the user's emotional changes.

[0071] The music generation unit can link with the user's smart home devices and play the generated music from speakers in the room. For example, the music generation unit can link with smart home devices to build a system in which music generated by the generation AI is played from speakers in the room. For example, the music generation unit can automatically send music to a smart speaker. The music generation unit can also link with the user's smart home devices to play music generated by the generation AI throughout the room. For example, it can play music by synchronizing multiple speakers. Furthermore, the music generation unit can link with smart home devices to play music generated by the generation AI from speakers in the room in response to a user's instructions. For example, music can be played by voice command. This allows the generated music to be played throughout the room.

[0072] The music generation unit can add a function that allows the user to share the generated music with their friends and family. For example, the music generation unit can add a function that allows the user to share the music generated by the generation AI with their friends and family. For example, it can generate a link for sharing music. The music generation unit also builds a system that allows the user to share the music generated by the generation AI with their friends and family in real time. For example, it can provide a function for playing music simultaneously. Furthermore, the music generation unit can share the music generated by the generation AI with the user's friends and family, allowing them to share a musical experience that is tailored to their emotions. For example, it can add a function that recommends music based on an emotional score. This allows them to share a musical experience that is tailored to their emotions.

[0073] The music generation unit can use the emotion estimation function to analyze changes in a user's emotions after listening to specific music, and improve the new music generation algorithm based on that data. For example, the music generation unit can use the emotion estimation function to analyze changes in a user's emotions after listening to specific music. For example, it can compare emotion scores before and after music playback. The music generation unit also collects emotion data after a user listens to music, and the generation AI improves the new music generation algorithm based on that data. For example, it can enhance elements of music that have a high relaxing effect. Furthermore, the music generation unit can use the emotion estimation function to monitor changes in a user's emotions in real time after listening to specific music, and the generation AI improves the new music generation algorithm based on that data. For example, it can enhance elements of music that induce positive emotional changes. This improves the new music generation algorithm.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] The music generation system may further include a lighting control unit that adjusts the color and brightness of lighting based on the user's emotions. For example, if the emotion analysis unit detects that the user is relaxed, the lighting control unit may provide soft, warm-colored light. If the emotion analysis unit detects that the user is concentrating, the lighting control unit may intensify white light. Furthermore, if the emotion analysis unit detects that the user is stressed, the lighting control unit may adjust the color to have a relaxing effect. This makes it possible to provide an optimal environment according to the user's emotions.

[0076] The music generation system can further include a scent control unit that adjusts the scent based on the user's emotions. For example, if the emotion analysis unit detects that the user is relaxed, the scent control unit can provide a scent of lavender or chamomile. If the emotion analysis unit detects that the user is concentrating, the scent control unit can provide a scent of peppermint or rosemary. If the emotion analysis unit detects that the user is stressed, the scent control unit can provide a scent with a relaxing effect. This makes it possible to provide an optimal environment according to the user's emotions.

[0077] The music generation system can further include a temperature control unit that adjusts the temperature based on the user's emotions. For example, if the emotion analysis unit detects that the user is relaxed, the temperature control unit can slightly lower the room temperature. Alternatively, if the emotion analysis unit detects that the user is concentrating, the temperature control unit can maintain the room temperature at a moderate level. Furthermore, if the emotion analysis unit detects that the user is stressed, the temperature control unit can adjust the temperature to a comfortable level. This makes it possible to provide an optimal environment according to the user's emotions.

[0078] The music generation system can further include a furniture layout suggestion unit that suggests furniture layouts based on the user's emotions. For example, if the emotion analysis unit detects a user's relaxed state, the furniture layout suggestion unit can suggest a layout that facilitates relaxation. If the emotion analysis unit detects a user's concentrated state, the furniture layout suggestion unit can suggest a layout that facilitates concentration. If the emotion analysis unit detects a user's stress state, the furniture layout suggestion unit can suggest a layout that reduces stress. This makes it possible to provide an optimal environment according to the user's emotions.

[0079] The music generation system can further include an exercise suggestion unit that suggests an exercise program based on the user's emotions. For example, if the emotion analysis unit detects that the user is relaxed, the exercise suggestion unit can suggest light stretching or yoga. If the emotion analysis unit detects that the user is focused, the exercise suggestion unit can suggest short-term exercises to improve concentration. If the emotion analysis unit detects that the user is stressed, the exercise suggestion unit can suggest exercises to relieve stress. This makes it possible to provide an optimal exercise program based on the user's emotions.

[0080] The music generation system can further include a history learning unit that automatically learns the user's preferred music genres based on the user's past music history. For example, the history learning unit can analyze the genres and artists of music played by the user in the past to identify the user's preferences. The history learning unit can also learn the user's preference trends based on the number of times the user plays and their ratings. Furthermore, the history learning unit can reflect the user's music history in the next music generation. This makes it possible to provide music that suits the user's preferences.

[0081] The music generation system may further include an activity analysis unit that generates music based on the user's current activity. For example, the activity analysis unit may detect that the user is exercising and provide fast-paced music. Alternatively, the activity analysis unit may detect that the user is working and provide music that promotes concentration. Alternatively, the activity analysis unit may detect that the user is relaxing and provide calming music. This allows the system to provide music that is optimal for the user's current activity.

[0082] The music generation system may further include a sharing unit for sharing music with the user's friends and family. For example, the sharing unit may generate a link for sharing the generated music with friends and family. The sharing unit may also provide a function for sharing music in real time. The sharing unit may also provide a function for sharing a musical experience according to an emotion. This allows the user to share music with friends and family and enjoy a musical experience according to an emotion.

[0083] The music generation system can further include a tempo adjustment unit that adjusts the tempo and rhythm of the music in real time based on the user's emotions. For example, if the emotion analysis unit detects that the user is relaxed, the tempo adjustment unit can slow down the tempo of the music. Alternatively, if the emotion analysis unit detects that the user is concentrating, the tempo adjustment unit can speed up the tempo of the music. Furthermore, if the emotion analysis unit detects that the user is stressed, the tempo adjustment unit can adjust the tempo to one that has a relaxing effect. This makes it possible to provide optimal music that corresponds to the user's emotions.

[0084] The music generation system may further include a volume adjustment unit that adjusts the volume of the music in real time based on the user's emotions. For example, if the emotion analysis unit detects that the user is relaxed, the volume adjustment unit can lower the volume of the music. Alternatively, if the emotion analysis unit detects that the user is concentrating, the volume adjustment unit can maintain a moderate volume of the music. Furthermore, if the emotion analysis unit detects that the user is stressed, the volume adjustment unit can adjust the volume to a relaxing level. This makes it possible to provide optimal music according to the user's emotions.

[0085] The processing flow of the second embodiment will be briefly explained below.

[0086] Step 1: The data collection unit collects voice and physical information data measured by the smartwatch. For example, the data collection unit uses sensors installed in the smartwatch to collect data such as heart rate, body temperature, and activity level in real time. The data collection unit can also collect the user's voice and save it as voice data. Step 2: The sentiment analysis unit performs sentiment analysis based on the data collected by the data collection unit. For example, the sentiment analysis unit analyzes voice tone and heart rate fluctuations to identify the user's current emotional state. The sentiment analysis unit can also use machine learning algorithms to infer emotions from the collected data. Step 3: The music generation unit generates music based on the results of the analysis by the emotion analysis unit. For example, the music generation unit uses a generative AI to generate music that best suits the user's emotions. The music generation unit can also generate music that suits the user's preferences and culture.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0091] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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 AI 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.

[0104] 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.

[0105] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0113] 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.

[0114] 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.

[0115] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0116] 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.

[0117] 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.

[0118] 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 AI 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.

[0119] 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.

[0120] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0121] 7, a 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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 processing similar to that of the specific processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] 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 AI 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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."

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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, in order to avoid confusion and to 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.

[0153] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0154] 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. a data collection unit that collects voice and physical information data measured by the smartwatch; a sentiment analysis unit that performs sentiment analysis based on the data collected by the data collection unit; a music generation unit that generates music based on the results of the analysis by the emotion analysis unit. A system characterized by:

2. The data collection unit The smartwatch will be equipped with more sensors to collect data on electrodermal activity or blood oxygen levels.

2. The system of claim 1.

3. The data collection unit Learns the user's lifestyle patterns and collects data according to specific time periods and activities 2. The system of claim 1.

4. The data collection unit Collect emotional data when users listen to specific music 2. The system of claim 1.

5. The data collection unit Collect data from wearable devices other than smartwatches and integrate information from multiple devices 2. The system of claim 1.

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