system

Abstract

The system according to this embodiment aims to generate and play optimal background music according to the user's emotions and fatigue level. [Solution] The system according to the embodiment comprises an acquisition unit, an estimation unit, a generation unit, and a playback unit. The acquisition unit acquires information on the user's facial expressions and posture. The estimation unit estimates emotions and fatigue levels based on the information acquired by the acquisition unit. The generation unit generates optimal background music based on the emotions and fatigue levels estimated by the estimation unit and the BGM playback history. The playback unit plays the BGM generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it has not been fully achieved to automatically generate and play an optimal BGM according to the user's emotion and fatigue level, and there is room for improvement.

[0005] The system according to the embodiment aims to generate and play an optimal BGM according to the user's emotion and fatigue level.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an estimation unit, a generation unit, and a playback unit. The acquisition unit acquires information on the user's facial expressions and posture. The estimation unit estimates emotions and fatigue levels based on the information acquired by the acquisition unit. The generation unit generates optimal background music (BGM) based on the emotions and fatigue levels estimated by the estimation unit and the BGM playback history. The playback unit plays the BGM generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can generate and play optimal background music according to the user's emotions and fatigue level. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system in which an agent acquires human facial expressions and posture information from the camera of a PC or smartphone and estimates the user's emotions and fatigue level at that time. Based on the estimated emotions and fatigue level, and the BGM playback history, this system generates the most suitable background music for that time. Furthermore, if the user hums, the system can automatically generate music to lift their spirits. If the user is wearing a smartwatch, the system can also suggest BGM that is even more appropriate to their current state based on blood pressure and heart rate information. The system can also generate and play music in accordance with the user's instructions given by calling out to the agent. For example, the agent acquires human facial expressions and posture information from the camera of a PC or smartphone. For example, when a user is working in front of a PC, the camera captures the user's facial expressions and posture in real time. This information is transmitted to the agent and used to estimate emotions and fatigue level. Next, the agent generates the most suitable background music for that time based on the estimated emotions and fatigue level, and the BGM playback history. For example, if the user is tired, the system generates and plays relaxing background music. Furthermore, if a user hums, the system automatically converts the humming into music and plays it as background music to uplift the user's mood. Additionally, if the user is wearing a smartwatch, the agent acquires information on blood pressure and heart rate and suggests background music more appropriate to their current state. For example, if the user's heart rate is high, the system suggests relaxing background music. The system can also generate and play music based on general instructions given by the user. For instance, if the user says, "Play some relaxing music," the agent generates and plays relaxing background music based on that instruction. In this way, the present invention can improve the user's mood by generating and playing optimal background music based on the user's emotions, fatigue level, and physical information. It can also provide a musical experience tailored to the user's needs by generating and playing music based on user instructions. Thus, the system can generate and play optimal background music based on the user's emotions, fatigue level, and physical information.

[0029] The system according to the embodiment comprises an acquisition unit, an estimation unit, a generation unit, and a playback unit. The acquisition unit acquires the user's facial expressions and posture information. The acquisition unit captures the user's facial expressions and posture information in real time, for example, using a camera on a PC or smartphone. For example, the acquisition unit can acquire information when the user is working in front of a PC, by having the camera capture the user's facial expressions and posture. The estimation unit estimates emotions and fatigue levels based on the information acquired by the acquisition unit. For example, the estimation unit can estimate the user's emotions using facial recognition technology. For example, the estimation unit can estimate the user's fatigue level using posture analysis technology. For example, the estimation unit can estimate the user's emotions and fatigue levels using collaborative filtering technology. The generation unit generates optimal background music (BGM) based on the emotions, fatigue levels, and BGM playback history estimated by the estimation unit. For example, the generation unit can generate BGM that corresponds to the user's emotions and fatigue level using generation AI. For example, if the user is tired, the generation unit can generate relaxing BGM. The generation unit can, for example, generate background music (BGM) that enhances concentration when the user is concentrating. The playback unit plays the BGM generated by the generation unit. The playback unit can play the BGM using, for example, speakers or headphones. The playback unit can play the BGM based on user instructions. For example, if the user instructs the playback unit to "play relaxing music," the playback unit can play relaxing BGM based on that instruction. In this way, the system can improve the user's mood by estimating their emotions and fatigue level based on the user's facial expressions and posture information, and generating and playing the optimal BGM.

[0030] The data acquisition unit captures the user's facial expressions and posture information. Specifically, it uses the camera on a PC or smartphone to capture the user's facial expressions and posture information in real time. For example, when a user is working in front of a PC, the camera captures the user's face and detects changes in facial expressions and posture. This allows the data acquisition unit to capture even subtle changes in the user's facial expressions and posture with high accuracy. Furthermore, by using multiple cameras, the data acquisition unit can acquire information from different angles and collect more accurate data. For example, by using both the front and side cameras of a PC, it is possible to capture not only the user's facial expressions but also their entire body posture in detail. In addition to cameras, the data acquisition unit can also use a microphone to acquire changes in the user's voice tone and speaking style. This allows for the collection of data to evaluate the user's emotions and fatigue level from a more multifaceted perspective. Furthermore, with the user's permission, the data acquisition unit can send this data to the cloud and share it with other devices. This enables consistent data collection even when the user is using different devices.

[0031] The estimation unit estimates emotions and fatigue levels based on information acquired by the acquisition unit. Specifically, it estimates the user's emotions using facial recognition technology. For example, it analyzes the feature points of the user's face to identify emotions such as smiles, anger, and sadness. It also estimates the user's fatigue level using posture analysis technology. For example, if the user's posture is poor or they maintain the same posture for a long time, it can be estimated that they are fatigued. Furthermore, collaborative filtering technology can be used to compare past data and data from other users to estimate emotions and fatigue levels more accurately. For example, by referring to data from other users who exhibit similar facial expressions and postures, the accuracy of the estimation can be improved. By combining these technologies, the estimation unit can estimate the user's emotions and fatigue levels with high accuracy and improve the overall system performance.

[0032] The generation unit generates optimal background music (BGM) based on the emotions, fatigue levels, and BGM playback history estimated by the estimation unit. Specifically, it uses a generation AI to generate BGM that matches the user's emotions and fatigue level. For example, if the user is tired, it generates music with a relaxed tempo and gentle melody to create relaxing BGM. Conversely, if the user is concentrating, it can generate fast-paced, rhythmic music to enhance concentration. The generation AI can generate more personalized BGM by analyzing past BGM playback history and learning the user's musical preferences. For example, it can learn the characteristics of music the user has listened to to relax in the past and generate new BGM based on that. The generation unit can also monitor the user's state in real time and change the BGM as needed. For example, if the user's emotions or fatigue level change, it can automatically switch the BGM accordingly. This allows the generation unit to provide BGM that is optimal for the user's state, improving the user's mood and performance.

[0033] The playback unit plays the background music (BGM) generated by the generation unit. Specifically, it plays the BGM using speakers or headphones. For example, if the user is working on a PC, the BGM can be played through the PC's speakers. If the user is using headphones, the BGM can be played through the headphones. The playback unit can also play BGM based on user instructions. For example, if the user instructs, "Play some relaxing music," the playback unit can play relaxing BGM based on that instruction. The playback unit can also adjust the volume and timing of the BGM. For example, if the user is concentrating, the BGM volume can be set low, and if they are relaxing, the volume can be set high. Furthermore, the playback unit can collect user feedback and reflect it in the selection of BGM to play and the adjustment of the volume. This allows the playback unit to provide optimal BGM according to the user's preferences and state, improving the user's mood and performance. In addition, the playback unit can synchronize BGM playback across multiple devices. For example, if the user is using a smartphone and a PC simultaneously, the same BGM can be played on both devices. This allows the user to enjoy a consistent music experience regardless of which device they are using.

[0034] The conversion unit converts humming into music. For example, if a user hums, the conversion unit can automatically convert that humming into music. For example, the conversion unit can use speech recognition technology to convert humming into music. For example, the conversion unit can use speech synthesis technology to convert humming into music. For example, the conversion unit can use music generation technology to convert humming into music. This allows users to automatically convert their humming into music, thereby lifting their spirits.

[0035] The information acquisition unit acquires blood pressure and heart rate information from the smartwatch. For example, the information acquisition unit can acquire the user's blood pressure and heart rate information in real time using the smartwatch. For example, if the user is wearing a smartwatch, the information acquisition unit can acquire blood pressure and heart rate information from that smartwatch. For example, the information acquisition unit can acquire blood pressure and heart rate information using the smartwatch's sensors. This allows the system to acquire blood pressure and heart rate information from the smartwatch and suggest background music appropriate to the user's current condition.

[0036] The instruction unit generates music based on user instructions. For example, the instruction unit can generate and play music in accordance with a user's general instructions. For example, if the user instructs the instruction unit to "play relaxing music," the instruction unit can generate and play relaxing background music based on that instruction. For example, if the user instructs the instruction unit to "play music that helps me concentrate," the instruction unit can generate and play background music that enhances concentration based on that instruction. In this way, by generating and playing music based on user instructions, it is possible to provide a music experience that meets the user's needs.

[0037] The estimation unit can estimate emotions and fatigue levels using collaborative filtering, emotion estimation, and posture estimation techniques. For example, the estimation unit can estimate a user's emotions and fatigue levels using collaborative filtering techniques. For example, the estimation unit can estimate a user's emotions using facial recognition techniques. For example, the estimation unit can estimate a user's fatigue levels using posture analysis techniques. As a result, the accuracy of emotion and fatigue level estimation is improved by using collaborative filtering, emotion estimation, and posture estimation techniques.

[0038] The conversion unit can convert humming into music using speech recognition, speech synthesis, and music generation technologies. For example, the conversion unit can convert humming into music using speech recognition technology. For example, the conversion unit can convert humming into music using speech synthesis technology. For example, the conversion unit can convert humming into music using music generation technology. This allows for high-precision conversion of humming into music using speech recognition, speech synthesis, and music generation technologies.

[0039] The data acquisition unit can analyze the user's past history of facial expressions and postures and select the optimal acquisition method. For example, the data acquisition unit can detect specific patterns based on facial expressions and postures that the user has frequently shown in the past and acquire information based on those patterns. For example, the data acquisition unit can analyze changes in facial expressions and postures during specific time periods from the user's past history and acquire information with a focus on those time periods. For example, the data acquisition unit can predict facial expressions and postures that the user will show during specific activities based on the user's past history and acquire information during those activities. In this way, by analyzing past history, the optimal acquisition method can be selected and information can be acquired efficiently.

[0040] The data acquisition unit can filter facial expressions and posture information based on the user's current activity status. For example, if the user is exercising, the unit can prioritize acquiring facial expressions and posture information related to exercise and filter out other information. For example, if the user is relaxed, the unit can prioritize acquiring facial expressions and posture information related to relaxation and filter out other information. For example, if the user is working, the unit can prioritize acquiring facial expressions and posture information related to work and filter out other information. By filtering information based on the current activity status, it is possible to acquire highly relevant information.

[0041] The data acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location when acquiring facial expressions and posture information. For example, if the user is at home, the data acquisition unit can prioritize the acquisition of facial expressions and posture information related to activities at home. For example, if the user is at work, the data acquisition unit can prioritize the acquisition of facial expressions and posture information related to activities at work. For example, if the user is out, the data acquisition unit can prioritize the acquisition of facial expressions and posture information related to activities at the location where the user is out. In this way, by considering geographical location information, highly relevant information can be prioritized.

[0042] The data acquisition unit can analyze the user's social media activity when acquiring facial expression and posture information, and acquire relevant information. For example, if the user posts on social media indicating stress, the data acquisition unit can prioritize acquiring facial expression and posture information related to that stress. For example, if the user posts on social media indicating relaxation, the data acquisition unit can prioritize acquiring facial expression and posture information related to that relaxation. For example, if the user posts on social media indicating concentration, the data acquisition unit can prioritize acquiring facial expression and posture information related to that concentration. This makes it easier to acquire relevant information by analyzing social media activity.

[0043] The estimation unit can improve the accuracy of its estimations of emotions and fatigue levels by referring to the user's past emotional history. For example, the estimation unit can detect specific patterns based on the user's past emotional history and estimate emotions and fatigue levels based on those patterns. For example, the estimation unit can analyze changes in emotions during specific time periods from the user's past emotional history and estimate emotions and fatigue levels with emphasis on those time periods. For example, the estimation unit can predict the emotions a user will exhibit during a specific activity based on their past emotional history and estimate emotions and fatigue levels during that activity. In this way, the accuracy of the estimation is improved by referring to past emotional history.

[0044] The estimation unit can optimize its estimation algorithm based on the user's current activity level when estimating emotions and fatigue levels. For example, if the user is exercising, the estimation unit can estimate emotions and fatigue levels by emphasizing facial expressions and posture information related to exercise. For example, if the user is relaxed, the estimation unit can estimate emotions and fatigue levels by emphasizing facial expressions and posture information related to relaxation. For example, if the user is working, the estimation unit can estimate emotions and fatigue levels by emphasizing facial expressions and posture information related to work. By optimizing the estimation algorithm based on the current activity level, the accuracy of the estimation is improved.

[0045] The estimation unit can improve the accuracy of its estimations of emotions and fatigue levels by considering the user's geographical location information. For example, if the user is at home, the estimation unit can estimate emotions and fatigue levels by emphasizing facial expressions and posture information related to activities at home. For example, if the user is at work, the estimation unit can estimate emotions and fatigue levels by emphasizing facial expressions and posture information related to activities at work. For example, if the user is out, the estimation unit can estimate emotions and fatigue levels by emphasizing facial expressions and posture information related to activities at the location where the user is out. This improves the accuracy of the estimation by considering geographical location information.

[0046] The estimation unit can improve the accuracy of its estimations of emotions and fatigue levels by analyzing the user's social media activity. For example, if a user posts on social media indicating they are stressed, the estimation unit can estimate their emotions and fatigue levels by emphasizing facial expressions and posture information related to that stress. For example, if a user posts on social media indicating they are relaxed, the estimation unit can estimate their emotions and fatigue levels by emphasizing facial expressions and posture information related to that relaxation. For example, if a user posts on social media indicating they are concentrating, the estimation unit can estimate their emotions and fatigue levels by emphasizing facial expressions and posture information related to that concentration. In this way, analyzing social media activity improves the accuracy of the estimations.

[0047] The generation unit can improve the accuracy of BGM generation by referring to the user's past BGM playback history. For example, the generation unit can analyze patterns of BGM previously played by the user and generate BGM based on those patterns. For example, the generation unit can analyze BGM played during a specific time period from the user's past BGM playback history and generate BGM suitable for that time period. For example, the generation unit can analyze BGM played during a specific activity based on the user's past BGM playback history and generate BGM suitable for that activity. In this way, the accuracy of generation is improved by referring to past BGM playback history.

[0048] The generation unit can optimize its generation algorithm based on the user's current activity level when generating background music (BGM). For example, if the user is exercising, the generation unit can generate BGM suitable for exercise. For example, if the user is relaxing, the generation unit can generate BGM suitable for relaxation. For example, if the user is working, the generation unit can generate BGM suitable for work. By optimizing the generation algorithm based on the user's current activity level, the accuracy of the generation is improved.

[0049] The generation unit can generate optimal background music (BGM) by considering the user's geographical location information. For example, if the user is at home, the generation unit can generate BGM suitable for activities at home. For example, if the user is at work, the generation unit can generate BGM suitable for activities at work. For example, if the user is out, the generation unit can generate BGM suitable for activities while out. In this way, the optimal BGM can be generated by considering geographical location information.

[0050] The generation unit can improve the accuracy of background music (BGM) generation by analyzing the user's social media activity. For example, if a user posts about feeling stressed on social media, the generation unit can generate BGM that reduces that stress. For example, if a user posts about relaxing on social media, the generation unit can generate BGM that maintains that relaxation. For example, if a user posts about concentrating on social media, the generation unit can generate BGM that maintains that concentration. In this way, the accuracy of generation is improved by analyzing social media activity.

[0051] The playback unit can improve playback accuracy by referring to the user's past playback history when playing background music (BGM). For example, the playback unit can analyze patterns of BGM previously played by the user and play BGM based on those patterns. For example, the playback unit can analyze BGM played during a specific time period from the user's past playback history and play BGM appropriate for that time period. For example, the playback unit can analyze BGM played during a specific activity based on the user's past playback history and play BGM appropriate for that activity. In this way, playback accuracy is improved by referring to past playback history.

[0052] The playback unit can optimize the playback algorithm based on the user's current activity level when playing background music (BGM). For example, if the user is exercising, the playback unit can play BGM suitable for exercise. For example, if the user is relaxing, the playback unit can play BGM suitable for relaxation. For example, if the user is working, the playback unit can play BGM suitable for work. By optimizing the playback algorithm based on the user's current activity level, the accuracy of playback is improved.

[0053] The playback unit can play the most suitable background music (BGM) by considering the user's geographical location. For example, if the user is at home, the playback unit can play BGM suitable for activities at home. For example, if the user is at work, the playback unit can play BGM suitable for activities at work. For example, if the user is out, the playback unit can play BGM suitable for activities while out. In this way, the optimal BGM can be played by considering geographical location.

[0054] The playback unit can improve the accuracy of background music playback by analyzing the user's social media activity. For example, if the user is posting stressful content on social media, the playback unit can play background music that reduces that stress. For example, if the user is posting relaxing content on social media, the playback unit can play background music that helps maintain that relaxation. For example, if the user is posting focused content on social media, the playback unit can play background music that helps maintain that focus. In this way, the accuracy of playback is improved by analyzing social media activity.

[0055] The conversion unit can improve the accuracy of humming by referring to the user's past humming history. For example, the conversion unit can analyze patterns of humming the user has sung in the past and convert them into music based on those patterns. For example, the conversion unit can analyze humming sung during a specific time period from the user's past humming history and convert it into music suitable for that time period. For example, the conversion unit can analyze humming sung during a specific activity based on the user's past humming history and convert it into music suitable for that activity. In this way, the accuracy of the conversion is improved by referring to the past humming history.

[0056] The conversion unit can optimize its conversion algorithm based on the user's current activity level when converting humming. For example, if the user is exercising, the unit can convert the humming to music suitable for exercise. For example, if the user is relaxing, the unit can convert the humming to music suitable for relaxation. For example, if the user is working, the unit can convert the humming to music suitable for work. By optimizing the conversion algorithm based on the user's current activity level, the accuracy of the conversion is improved.

[0057] The conversion unit can perform optimal conversions by considering the user's geographical location when converting humming. For example, if the user is at home, the unit can convert the humming to music suitable for activities at home. For example, if the user is at work, the unit can convert the humming to music suitable for activities at work. For example, if the user is out, the unit can convert the humming to music suitable for activities while out. In this way, the unit can convert the humming to music that is optimal by considering the user's geographical location.

[0058] The conversion unit can improve the accuracy of the conversion by analyzing the user's social media activity when converting humming. For example, if the user posts about feeling stressed on social media, the conversion unit can convert it into music that alleviates that stress. For example, if the user posts about feeling relaxed on social media, the conversion unit can convert it into music that maintains that relaxation. For example, if the user posts about feeling focused on social media, the conversion unit can convert it into music that maintains that focus. In this way, the accuracy of the conversion is improved by analyzing social media activity.

[0059] The information acquisition unit can improve the accuracy of data acquisition by referring to the user's past health data when acquiring blood pressure and heart rate information. For example, the information acquisition unit can detect specific patterns based on the user's past health data and acquire information based on those patterns. For example, the information acquisition unit can analyze changes in the user's past health data during specific time periods and acquire information with a focus on those time periods. For example, the information acquisition unit can predict changes that will occur during specific activities based on the user's past health data and acquire information during those activities. As a result, the accuracy of data acquisition is improved by referring to past health data.

[0060] The information acquisition unit can optimize its acquisition algorithm based on the user's current activity level when acquiring blood pressure and heart rate information. For example, if the user is exercising, the information acquisition unit can prioritize acquiring blood pressure and heart rate information related to exercise. For example, if the user is relaxed, the information acquisition unit can prioritize acquiring blood pressure and heart rate information related to relaxation. For example, if the user is working, the information acquisition unit can prioritize acquiring blood pressure and heart rate information related to work. By optimizing the acquisition algorithm based on the current activity level, the accuracy of the acquisition is improved.

[0061] The information acquisition unit can acquire optimal information by considering the user's geographical location when acquiring blood pressure and heart rate data. For example, if the user is at home, the information acquisition unit can prioritize acquiring blood pressure and heart rate information related to activities at home. For example, if the user is at work, the information acquisition unit can prioritize acquiring blood pressure and heart rate information related to activities at work. For example, if the user is out, the information acquisition unit can prioritize acquiring blood pressure and heart rate information related to activities at the location where they are out. In this way, optimal health information can be acquired by considering geographical location information.

[0062] The information acquisition unit can improve the accuracy of data acquisition by analyzing the user's social media activity when acquiring blood pressure and heart rate information. For example, if a user posts on social media indicating they are feeling stressed, the information acquisition unit can prioritize acquiring blood pressure and heart rate information related to that stress. For example, if a user posts on social media indicating they are relaxed, the information acquisition unit can prioritize acquiring blood pressure and heart rate information related to that relaxation. For example, if a user posts on social media indicating they are concentrating, the information acquisition unit can prioritize acquiring blood pressure and heart rate information related to that concentration. In this way, the accuracy of data acquisition is improved by analyzing social media activity.

[0063] The instruction unit can improve the accuracy of instructions when issuing music generation instructions by referring to the user's past instruction history. For example, the instruction unit can detect a specific pattern based on the user's past instruction history and issue music generation instructions based on that pattern. For example, the instruction unit can analyze instructions made during a specific time period from the user's past instruction history and issue music generation instructions appropriate for that time period. For example, the instruction unit can analyze instructions made during a specific activity based on the user's past instruction history and issue music generation instructions appropriate for that activity. In this way, the accuracy of instructions is improved by referring to past instruction history.

[0064] The instruction unit can optimize its instruction algorithm based on the user's current activity when issuing a music generation instruction. For example, if the user is exercising, the instruction unit can instruct the system to generate music suitable for exercise. For example, if the user is relaxing, the instruction unit can instruct the system to generate music suitable for relaxation. For example, if the user is working, the instruction unit can instruct the system to generate music suitable for work. By optimizing the instruction algorithm based on the user's current activity, the accuracy of the instructions is improved.

[0065] The instruction unit can provide optimal instructions for music generation by considering the user's geographical location. For example, if the user is at home, the instruction unit can provide instructions to generate music suitable for activities at home. For example, if the user is at work, the instruction unit can provide instructions to generate music suitable for activities at work. For example, if the user is out, the instruction unit can provide instructions to generate music suitable for activities at their destination. This makes it possible to provide optimal music generation instructions by considering geographical location information.

[0066] The instruction unit can improve the accuracy of its instructions by analyzing the user's social media activity when issuing music generation instructions. For example, if the instruction unit is posting stressful content on social media, it can issue instructions to generate music that alleviates that stress. For example, if the user is posting relaxing content on social media, it can issue instructions to generate music that maintains that relaxation. For example, if the user is posting focused content on social media, it can issue instructions to generate music that maintains that focus. In this way, analyzing social media activity improves the accuracy of the instructions.

[0067] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0068] The conversion unit can not only convert humming into music, but also convert whistling sounds into music. For example, if a user whistles, it can automatically convert that whistle into music. The conversion unit can convert whistling into music using, for example, speech recognition technology. The conversion unit can convert whistling into music using, for example, speech synthesis technology. The conversion unit can convert whistling into music using, for example, music generation technology. As a result, when a user whistles, their whistle is automatically converted into music, which can lift their spirits.

[0069] The information acquisition unit can acquire not only blood pressure and heart rate information from the smartwatch, but also information on the user's sleep patterns and activity levels. For example, it can acquire information on the user's sleep patterns and activity levels in real time using the smartwatch. For example, if the user is wearing a smartwatch, the information acquisition unit can acquire information on sleep patterns and activity levels from that smartwatch. For example, the information acquisition unit can acquire information on sleep patterns and activity levels using the smartwatch's sensors. This allows the system to acquire information on sleep patterns and activity levels from the smartwatch and suggest background music appropriate to the user's current state.

[0070] The control unit can generate music not only based on user instructions, but also based on user gestures and actions. For example, if a user waves their hand or makes a specific gesture, the control unit can generate music based on that gesture. Instead of the user saying, "Play some relaxing music," the control unit can generate and play relaxing background music based on a user's gesture indicating relaxation. This allows the system to provide a music experience tailored to the user's needs by generating and playing music based on the user's gestures and actions.

[0071] The conversion unit can convert humming into music using speech recognition, speech synthesis, and music generation technologies, as well as convert whistling into music when a user whistles. For example, if a user whistles, the unit can automatically convert the whistle into music. The conversion unit can convert whistling into music using, for example, speech recognition technology. The conversion unit can convert whistling into music using, for example, speech synthesis technology. The conversion unit can convert whistling into music using, for example, music generation technology. As a result, by using speech recognition, speech synthesis, and music generation technologies, humming and whistling can be converted into music with high accuracy.

[0072] The data acquisition unit can analyze not only the user's past facial expressions and posture history, but also the user's past voice tone and speaking patterns. For example, it can detect specific patterns based on the voice tone and speaking patterns that the user has frequently displayed in the past, and acquire information based on those patterns. The data acquisition unit can also analyze changes in voice tone and speaking patterns during specific time periods from the user's past history, and acquire information with a focus on those time periods. This allows for the selection of the optimal acquisition method by analyzing past history, enabling efficient information acquisition.

[0073] The data acquisition unit can filter not only facial expression and posture information based on the user's current activity level, but also based on the user's voice tone and speaking patterns. For example, if the user is exercising, the unit can prioritize acquiring voice tone and speaking patterns related to exercise and filter out other information. Similarly, if the user is relaxed, the unit can prioritize acquiring voice tone and speaking patterns related to relaxation and filter out other information. This allows for the acquisition of highly relevant information by filtering it based on the user's current activity level.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The acquisition unit acquires the user's facial expressions and posture information. The acquisition unit captures the user's facial expressions and posture information in real time, for example, using the camera of a PC or smartphone. For example, when a user is working in front of a PC, the acquisition unit can capture the user's facial expressions and posture with the camera and acquire that information. Step 2: The estimation unit estimates emotions and fatigue levels based on the information acquired by the acquisition unit. The estimation unit can estimate the user's emotions using, for example, facial expression recognition technology. The estimation unit can estimate the user's fatigue level using, for example, posture analysis technology. The estimation unit can estimate the user's emotions and fatigue level using, for example, collaborative filtering technology. Step 3: The generation unit generates optimal background music (BGM) based on the emotions, fatigue levels, and BGM playback history estimated by the estimation unit. For example, the generation unit can use a generation AI to generate BGM that corresponds to the user's emotions and fatigue levels. For example, if the user is tired, the generation unit can generate relaxing BGM. For example, if the user is concentrating, the generation unit can generate BGM that enhances concentration. Step 4: The playback unit plays the background music (BGM) generated by the generation unit. The playback unit can play the BGM using, for example, speakers or headphones. The playback unit can play the BGM based on user instructions. For example, if the user instructs the playback unit to "play relaxing music," the playback unit can play relaxing BGM based on that instruction.

[0076] (Example of form 2) The system according to an embodiment of the present invention is a system in which an agent acquires human facial expressions and posture information from the camera of a PC or smartphone and estimates the user's emotions and fatigue level at that time. Based on the estimated emotions and fatigue level, and the BGM playback history, this system generates the most suitable background music for that time. Furthermore, if the user hums, the system can automatically generate music to lift their spirits. If the user is wearing a smartwatch, the system can also suggest BGM that is even more appropriate to their current state based on blood pressure and heart rate information. The system can also generate and play music in accordance with the user's instructions given by calling out to the agent. For example, the agent acquires human facial expressions and posture information from the camera of a PC or smartphone. For example, when a user is working in front of a PC, the camera captures the user's facial expressions and posture in real time. This information is transmitted to the agent and used to estimate emotions and fatigue level. Next, the agent generates the most suitable background music for that time based on the estimated emotions and fatigue level, and the BGM playback history. For example, if the user is tired, the system generates and plays relaxing background music. Furthermore, if a user hums, the system automatically converts the humming into music and plays it as background music to uplift the user's mood. Additionally, if the user is wearing a smartwatch, the agent acquires information on blood pressure and heart rate and suggests background music more appropriate to their current state. For example, if the user's heart rate is high, the system suggests relaxing background music. The system can also generate and play music based on general instructions given by the user. For instance, if the user says, "Play some relaxing music," the agent generates and plays relaxing background music based on that instruction. In this way, the present invention can improve the user's mood by generating and playing optimal background music based on the user's emotions, fatigue level, and physical information. It can also provide a musical experience tailored to the user's needs by generating and playing music based on user instructions. Thus, the system can generate and play optimal background music based on the user's emotions, fatigue level, and physical information.

[0077] The system according to the embodiment comprises an acquisition unit, an estimation unit, a generation unit, and a playback unit. The acquisition unit acquires the user's facial expressions and posture information. The acquisition unit captures the user's facial expressions and posture information in real time, for example, using a camera on a PC or smartphone. For example, the acquisition unit can acquire information when the user is working in front of a PC, by having the camera capture the user's facial expressions and posture. The estimation unit estimates emotions and fatigue levels based on the information acquired by the acquisition unit. For example, the estimation unit can estimate the user's emotions using facial recognition technology. For example, the estimation unit can estimate the user's fatigue level using posture analysis technology. For example, the estimation unit can estimate the user's emotions and fatigue levels using collaborative filtering technology. The generation unit generates optimal background music (BGM) based on the emotions, fatigue levels, and BGM playback history estimated by the estimation unit. For example, the generation unit can generate BGM that corresponds to the user's emotions and fatigue level using generation AI. For example, if the user is tired, the generation unit can generate relaxing BGM. The generation unit can, for example, generate background music (BGM) that enhances concentration when the user is concentrating. The playback unit plays the BGM generated by the generation unit. The playback unit can play the BGM using, for example, speakers or headphones. The playback unit can play the BGM based on user instructions. For example, if the user instructs the playback unit to "play relaxing music," the playback unit can play relaxing BGM based on that instruction. In this way, the system can improve the user's mood by estimating their emotions and fatigue level based on the user's facial expressions and posture information, and generating and playing the optimal BGM.

[0078] The data acquisition unit captures the user's facial expressions and posture information. Specifically, it uses the camera on a PC or smartphone to capture the user's facial expressions and posture information in real time. For example, when a user is working in front of a PC, the camera captures the user's face and detects changes in facial expressions and posture. This allows the data acquisition unit to capture even subtle changes in the user's facial expressions and posture with high accuracy. Furthermore, by using multiple cameras, the data acquisition unit can acquire information from different angles and collect more accurate data. For example, by using both the front and side cameras of a PC, it is possible to capture not only the user's facial expressions but also their entire body posture in detail. In addition to cameras, the data acquisition unit can also use a microphone to acquire changes in the user's voice tone and speaking style. This allows for the collection of data to evaluate the user's emotions and fatigue level from a more multifaceted perspective. Furthermore, with the user's permission, the data acquisition unit can send this data to the cloud and share it with other devices. This enables consistent data collection even when the user is using different devices.

[0079] The estimation unit estimates emotions and fatigue levels based on information acquired by the acquisition unit. Specifically, it estimates the user's emotions using facial recognition technology. For example, it analyzes the feature points of the user's face to identify emotions such as smiles, anger, and sadness. It also estimates the user's fatigue level using posture analysis technology. For example, if the user's posture is poor or they maintain the same posture for a long time, it can be estimated that they are fatigued. Furthermore, collaborative filtering technology can be used to compare past data and data from other users to estimate emotions and fatigue levels more accurately. For example, by referring to data from other users who exhibit similar facial expressions and postures, the accuracy of the estimation can be improved. By combining these technologies, the estimation unit can estimate the user's emotions and fatigue levels with high accuracy and improve the overall system performance.

[0080] The generation unit generates optimal background music (BGM) based on the emotions, fatigue levels, and BGM playback history estimated by the estimation unit. Specifically, it uses a generation AI to generate BGM that matches the user's emotions and fatigue level. For example, if the user is tired, it generates music with a relaxed tempo and gentle melody to create relaxing BGM. Conversely, if the user is concentrating, it can generate fast-paced, rhythmic music to enhance concentration. The generation AI can generate more personalized BGM by analyzing past BGM playback history and learning the user's musical preferences. For example, it can learn the characteristics of music the user has listened to to relax in the past and generate new BGM based on that. The generation unit can also monitor the user's state in real time and change the BGM as needed. For example, if the user's emotions or fatigue level change, it can automatically switch the BGM accordingly. This allows the generation unit to provide BGM that is optimal for the user's state, improving the user's mood and performance.

[0081] The playback unit plays the background music (BGM) generated by the generation unit. Specifically, it plays the BGM using speakers or headphones. For example, if the user is working on a PC, the BGM can be played through the PC's speakers. If the user is using headphones, the BGM can be played through the headphones. The playback unit can also play BGM based on user instructions. For example, if the user instructs, "Play some relaxing music," the playback unit can play relaxing BGM based on that instruction. The playback unit can also adjust the volume and timing of the BGM. For example, if the user is concentrating, the BGM volume can be set low, and if they are relaxing, the volume can be set high. Furthermore, the playback unit can collect user feedback and reflect it in the selection of BGM to play and the adjustment of the volume. This allows the playback unit to provide optimal BGM according to the user's preferences and state, improving the user's mood and performance. In addition, the playback unit can synchronize BGM playback across multiple devices. For example, if the user is using a smartphone and a PC simultaneously, the same BGM can be played on both devices. This allows the user to enjoy a consistent music experience regardless of which device they are using.

[0082] The conversion unit converts humming into music. For example, if a user hums, the conversion unit can automatically convert that humming into music. For example, the conversion unit can use speech recognition technology to convert humming into music. For example, the conversion unit can use speech synthesis technology to convert humming into music. For example, the conversion unit can use music generation technology to convert humming into music. This allows users to automatically convert their humming into music, thereby lifting their spirits.

[0083] The information acquisition unit acquires blood pressure and heart rate information from the smartwatch. For example, the information acquisition unit can acquire the user's blood pressure and heart rate information in real time using the smartwatch. For example, if the user is wearing a smartwatch, the information acquisition unit can acquire blood pressure and heart rate information from that smartwatch. For example, the information acquisition unit can acquire blood pressure and heart rate information using the smartwatch's sensors. This allows the system to acquire blood pressure and heart rate information from the smartwatch and suggest background music appropriate to the user's current condition.

[0084] The instruction unit generates music based on user instructions. For example, the instruction unit can generate and play music in accordance with a user's general instructions. For example, if the user instructs the instruction unit to "play relaxing music," the instruction unit can generate and play relaxing background music based on that instruction. For example, if the user instructs the instruction unit to "play music that helps me concentrate," the instruction unit can generate and play background music that enhances concentration based on that instruction. In this way, by generating and playing music based on user instructions, it is possible to provide a music experience that meets the user's needs.

[0085] The estimation unit can estimate emotions and fatigue levels using collaborative filtering, emotion estimation, and posture estimation techniques. For example, the estimation unit can estimate a user's emotions and fatigue levels using collaborative filtering techniques. For example, the estimation unit can estimate a user's emotions using facial recognition techniques. For example, the estimation unit can estimate a user's fatigue levels using posture analysis techniques. As a result, the accuracy of emotion and fatigue level estimation is improved by using collaborative filtering, emotion estimation, and posture estimation techniques.

[0086] The conversion unit can convert humming into music using speech recognition, speech synthesis, and music generation technologies. For example, the conversion unit can convert humming into music using speech recognition technology. For example, the conversion unit can convert humming into music using speech synthesis technology. For example, the conversion unit can convert humming into music using music generation technology. This allows for high-precision conversion of humming into music using speech recognition, speech synthesis, and music generation technologies.

[0087] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring facial and posture information based on the estimated emotions. For example, if the user is stressed, the acquisition unit can acquire facial and posture information frequently and track changes in emotions in real time. For example, if the user is relaxed, the acquisition unit can acquire facial and posture information at regular intervals to avoid excessive data collection. For example, if the user is concentrating, the acquisition unit can adjust the acquisition timing to avoid interrupting their work and acquire information at the appropriate time. In this way, by adjusting the timing of acquiring facial and posture information according to the user's emotions, more appropriate information can be acquired. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The data acquisition unit can analyze the user's past history of facial expressions and postures and select the optimal acquisition method. For example, the data acquisition unit can detect specific patterns based on facial expressions and postures that the user has frequently shown in the past and acquire information based on those patterns. For example, the data acquisition unit can analyze changes in facial expressions and postures during specific time periods from the user's past history and acquire information with a focus on those time periods. For example, the data acquisition unit can predict facial expressions and postures that the user will show during specific activities based on the user's past history and acquire information during those activities. In this way, by analyzing past history, the optimal acquisition method can be selected and information can be acquired efficiently.

[0089] The data acquisition unit can filter facial expressions and posture information based on the user's current activity status. For example, if the user is exercising, the unit can prioritize acquiring facial expressions and posture information related to exercise and filter out other information. For example, if the user is relaxed, the unit can prioritize acquiring facial expressions and posture information related to relaxation and filter out other information. For example, if the user is working, the unit can prioritize acquiring facial expressions and posture information related to work and filter out other information. By filtering information based on the current activity status, it is possible to acquire highly relevant information.

[0090] The data acquisition unit can estimate the user's emotions and determine the priority of information to acquire based on the estimated emotions. For example, if the user is stressed, the data acquisition unit can prioritize acquiring facial expressions and posture information related to stress. For example, if the user is relaxed, the data acquisition unit can prioritize acquiring facial expressions and posture information related to relaxation. For example, if the user is concentrating, the data acquisition unit can prioritize acquiring facial expressions and posture information related to concentration. In this way, by determining the priority of information according to the user's emotions, important information can be acquired preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The data acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location when acquiring facial expressions and posture information. For example, if the user is at home, the data acquisition unit can prioritize the acquisition of facial expressions and posture information related to activities at home. For example, if the user is at work, the data acquisition unit can prioritize the acquisition of facial expressions and posture information related to activities at work. For example, if the user is out, the data acquisition unit can prioritize the acquisition of facial expressions and posture information related to activities at the location where the user is out. In this way, by considering geographical location information, highly relevant information can be prioritized.

[0092] The data acquisition unit can analyze the user's social media activity when acquiring facial expression and posture information, and acquire relevant information. For example, if the user posts on social media indicating stress, the data acquisition unit can prioritize acquiring facial expression and posture information related to that stress. For example, if the user posts on social media indicating relaxation, the data acquisition unit can prioritize acquiring facial expression and posture information related to that relaxation. For example, if the user posts on social media indicating concentration, the data acquisition unit can prioritize acquiring facial expression and posture information related to that concentration. This makes it easier to acquire relevant information by analyzing social media activity.

[0093] The estimation unit can estimate the user's emotions and adjust the method for estimating emotions and fatigue levels based on the estimated user emotions. For example, if the user is stressed, the estimation unit can estimate emotions and fatigue levels by emphasizing facial expressions and posture information related to stress. For example, if the user is relaxed, the estimation unit can estimate emotions and fatigue levels by emphasizing facial expressions and posture information related to relaxation. For example, if the user is concentrating, the estimation unit can estimate emotions and fatigue levels by emphasizing facial expressions and posture information related to concentration. By adjusting the estimation method according to the user's emotions, the accuracy of estimating emotions and fatigue levels is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0094] The estimation unit can improve the accuracy of its estimations of emotions and fatigue levels by referring to the user's past emotional history. For example, the estimation unit can detect specific patterns based on the user's past emotional history and estimate emotions and fatigue levels based on those patterns. For example, the estimation unit can analyze changes in emotions during specific time periods from the user's past emotional history and estimate emotions and fatigue levels with emphasis on those time periods. For example, the estimation unit can predict the emotions a user will exhibit during a specific activity based on their past emotional history and estimate emotions and fatigue levels during that activity. In this way, the accuracy of the estimation is improved by referring to past emotional history.

[0095] The estimation unit can optimize its estimation algorithm based on the user's current activity level when estimating emotions and fatigue levels. For example, if the user is exercising, the estimation unit can estimate emotions and fatigue levels by emphasizing facial expressions and posture information related to exercise. For example, if the user is relaxed, the estimation unit can estimate emotions and fatigue levels by emphasizing facial expressions and posture information related to relaxation. For example, if the user is working, the estimation unit can estimate emotions and fatigue levels by emphasizing facial expressions and posture information related to work. By optimizing the estimation algorithm based on the current activity level, the accuracy of the estimation is improved.

[0096] The estimation unit can estimate the user's emotions and adjust the display method of the estimation results based on the estimated user's emotions. For example, if the user is stressed, the estimation unit can provide a simple and highly visible display method. For example, if the user is relaxed, the estimation unit can provide a display method that includes detailed information. For example, if the user is focused, the estimation unit can provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, highly visible displays become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The estimation unit can improve the accuracy of its estimations of emotions and fatigue levels by considering the user's geographical location information. For example, if the user is at home, the estimation unit can estimate emotions and fatigue levels by emphasizing facial expressions and posture information related to activities at home. For example, if the user is at work, the estimation unit can estimate emotions and fatigue levels by emphasizing facial expressions and posture information related to activities at work. For example, if the user is out, the estimation unit can estimate emotions and fatigue levels by emphasizing facial expressions and posture information related to activities at the location where the user is out. This improves the accuracy of the estimation by considering geographical location information.

[0098] The estimation unit can improve the accuracy of its estimations of emotions and fatigue levels by analyzing the user's social media activity. For example, if a user posts on social media indicating they are stressed, the estimation unit can estimate their emotions and fatigue levels by emphasizing facial expressions and posture information related to that stress. For example, if a user posts on social media indicating they are relaxed, the estimation unit can estimate their emotions and fatigue levels by emphasizing facial expressions and posture information related to that relaxation. For example, if a user posts on social media indicating they are concentrating, the estimation unit can estimate their emotions and fatigue levels by emphasizing facial expressions and posture information related to that concentration. In this way, analyzing social media activity improves the accuracy of the estimations.

[0099] The generation unit can estimate the user's emotions and adjust the BGM generation method based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can generate relaxing BGM. For example, if the user is relaxed, the generation unit can generate BGM that helps maintain that relaxation. For example, if the user is concentrating, the generation unit can generate BGM that enhances concentration. In this way, by adjusting the BGM generation method according to the user's emotions, more appropriate BGM can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0100] The generation unit can improve the accuracy of BGM generation by referring to the user's past BGM playback history. For example, the generation unit can analyze patterns of BGM previously played by the user and generate BGM based on those patterns. For example, the generation unit can analyze BGM played during a specific time period from the user's past BGM playback history and generate BGM suitable for that time period. For example, the generation unit can analyze BGM played during a specific activity based on the user's past BGM playback history and generate BGM suitable for that activity. In this way, the accuracy of generation is improved by referring to past BGM playback history.

[0101] The generation unit can optimize its generation algorithm based on the user's current activity level when generating background music (BGM). For example, if the user is exercising, the generation unit can generate BGM suitable for exercise. For example, if the user is relaxing, the generation unit can generate BGM suitable for relaxation. For example, if the user is working, the generation unit can generate BGM suitable for work. By optimizing the generation algorithm based on the user's current activity level, the accuracy of the generation is improved.

[0102] The generation unit can estimate the user's emotions and determine the priority of background music (BGM) to generate based on the estimated emotions. For example, if the user is stressed, the generation unit can prioritize generating relaxing BGM. For example, if the user is relaxed, the generation unit can prioritize generating BGM that helps maintain that relaxation. For example, if the user is concentrating, the generation unit can prioritize generating BGM that enhances concentration. In this way, by determining the priority of BGM according to the user's emotions, more appropriate BGM can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0103] The generation unit can generate optimal background music (BGM) by considering the user's geographical location information. For example, if the user is at home, the generation unit can generate BGM suitable for activities at home. For example, if the user is at work, the generation unit can generate BGM suitable for activities at work. For example, if the user is out, the generation unit can generate BGM suitable for activities while out. In this way, the optimal BGM can be generated by considering geographical location information.

[0104] The generation unit can improve the accuracy of background music (BGM) generation by analyzing the user's social media activity. For example, if a user posts about feeling stressed on social media, the generation unit can generate BGM that reduces that stress. For example, if a user posts about relaxing on social media, the generation unit can generate BGM that maintains that relaxation. For example, if a user posts about concentrating on social media, the generation unit can generate BGM that maintains that concentration. In this way, the accuracy of generation is improved by analyzing social media activity.

[0105] The playback unit can estimate the user's emotions and adjust the BGM playback method based on the estimated emotions. For example, if the user is feeling stressed, the playback unit can play relaxing BGM. For example, if the user is relaxed, the playback unit can play BGM that helps maintain that relaxation. For example, if the user is concentrating, the playback unit can play BGM that enhances concentration. In this way, by adjusting the BGM playback method according to the user's emotions, more appropriate BGM can be played. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The playback unit can improve playback accuracy by referring to the user's past playback history when playing background music (BGM). For example, the playback unit can analyze patterns of BGM previously played by the user and play BGM based on those patterns. For example, the playback unit can analyze BGM played during a specific time period from the user's past playback history and play BGM appropriate for that time period. For example, the playback unit can analyze BGM played during a specific activity based on the user's past playback history and play BGM appropriate for that activity. In this way, playback accuracy is improved by referring to past playback history.

[0107] The playback unit can optimize the playback algorithm based on the user's current activity level when playing background music (BGM). For example, if the user is exercising, the playback unit can play BGM suitable for exercise. For example, if the user is relaxing, the playback unit can play BGM suitable for relaxation. For example, if the user is working, the playback unit can play BGM suitable for work. By optimizing the playback algorithm based on the user's current activity level, the accuracy of playback is improved.

[0108] The playback unit can estimate the user's emotions and determine the priority of background music (BGM) to play based on the estimated emotions. For example, if the user is stressed, the playback unit can prioritize playing relaxing BGM. For example, if the user is relaxed, the playback unit can prioritize playing BGM that helps maintain that relaxation. For example, if the user is concentrating, the playback unit can prioritize playing BGM that enhances concentration. In this way, by determining the priority of BGM according to the user's emotions, more appropriate BGM can be played. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The playback unit can play the most suitable background music (BGM) by considering the user's geographical location. For example, if the user is at home, the playback unit can play BGM suitable for activities at home. For example, if the user is at work, the playback unit can play BGM suitable for activities at work. For example, if the user is out, the playback unit can play BGM suitable for activities while out. In this way, the optimal BGM can be played by considering geographical location.

[0110] The playback unit can improve the accuracy of background music playback by analyzing the user's social media activity. For example, if the user is posting stressful content on social media, the playback unit can play background music that reduces that stress. For example, if the user is posting relaxing content on social media, the playback unit can play background music that helps maintain that relaxation. For example, if the user is posting focused content on social media, the playback unit can play background music that helps maintain that focus. In this way, the accuracy of playback is improved by analyzing social media activity.

[0111] The conversion unit can estimate the user's emotions and adjust the humming conversion method based on the estimated emotions. For example, if the user is feeling stressed, the conversion unit can convert the humming to relaxing music. For example, if the user is relaxed, the conversion unit can convert the humming to music that helps maintain that relaxation. For example, if the user is concentrating, the conversion unit can convert the humming to music that enhances concentration. In this way, by adjusting the humming conversion method according to the user's emotions, more appropriate music can be converted. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The conversion unit can improve the accuracy of humming by referring to the user's past humming history. For example, the conversion unit can analyze patterns of humming the user has sung in the past and convert them into music based on those patterns. For example, the conversion unit can analyze humming sung during a specific time period from the user's past humming history and convert it into music suitable for that time period. For example, the conversion unit can analyze humming sung during a specific activity based on the user's past humming history and convert it into music suitable for that activity. In this way, the accuracy of the conversion is improved by referring to the past humming history.

[0113] The conversion unit can optimize its conversion algorithm based on the user's current activity level when converting humming. For example, if the user is exercising, the unit can convert the humming to music suitable for exercise. For example, if the user is relaxing, the unit can convert the humming to music suitable for relaxation. For example, if the user is working, the unit can convert the humming to music suitable for work. By optimizing the conversion algorithm based on the user's current activity level, the accuracy of the conversion is improved.

[0114] The conversion unit can estimate the user's emotions and determine the priority of humming to convert based on the estimated emotions. For example, if the user is stressed, the conversion unit can prioritize humming to music that promotes relaxation. For example, if the user is relaxed, the conversion unit can prioritize humming to music that helps maintain relaxation. For example, if the user is concentrating, the conversion unit can prioritize humming to music that enhances concentration. In this way, by determining the priority of humming according to the user's emotions, more appropriate music can be converted. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The conversion unit can perform optimal conversions by considering the user's geographical location when converting humming. For example, if the user is at home, the unit can convert the humming to music suitable for activities at home. For example, if the user is at work, the unit can convert the humming to music suitable for activities at work. For example, if the user is out, the unit can convert the humming to music suitable for activities while out. In this way, the unit can convert the humming to music that is optimal by considering the user's geographical location.

[0116] The conversion unit can improve the accuracy of the conversion by analyzing the user's social media activity when converting humming. For example, if the user posts about feeling stressed on social media, the conversion unit can convert it into music that alleviates that stress. For example, if the user posts about feeling relaxed on social media, the conversion unit can convert it into music that maintains that relaxation. For example, if the user posts about feeling focused on social media, the conversion unit can convert it into music that maintains that focus. In this way, the accuracy of the conversion is improved by analyzing social media activity.

[0117] The information acquisition unit can estimate the user's emotions and adjust the timing of blood pressure and heart rate data acquisition based on the estimated emotions. For example, if the user is stressed, the information acquisition unit can acquire blood pressure and heart rate data frequently and track changes in real time. For example, if the user is relaxed, the information acquisition unit can acquire blood pressure and heart rate data at regular intervals to avoid excessive data collection. For example, if the user is concentrating, the information acquisition unit can adjust the acquisition timing to avoid interrupting their work and acquire information at the appropriate time. In this way, by adjusting the information acquisition timing according to the user's emotions, more appropriate information can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0118] The information acquisition unit can improve the accuracy of data acquisition by referring to the user's past health data when acquiring blood pressure and heart rate information. For example, the information acquisition unit can detect specific patterns based on the user's past health data and acquire information based on those patterns. For example, the information acquisition unit can analyze changes in the user's past health data during specific time periods and acquire information with a focus on those time periods. For example, the information acquisition unit can predict changes that will occur during specific activities based on the user's past health data and acquire information during those activities. As a result, the accuracy of data acquisition is improved by referring to past health data.

[0119] The information acquisition unit can optimize its acquisition algorithm based on the user's current activity level when acquiring blood pressure and heart rate information. For example, if the user is exercising, the information acquisition unit can prioritize acquiring blood pressure and heart rate information related to exercise. For example, if the user is relaxed, the information acquisition unit can prioritize acquiring blood pressure and heart rate information related to relaxation. For example, if the user is working, the information acquisition unit can prioritize acquiring blood pressure and heart rate information related to work. By optimizing the acquisition algorithm based on the current activity level, the accuracy of the acquisition is improved.

[0120] The information acquisition unit can estimate the user's emotions and determine the priority of health information to acquire based on the estimated user emotions. For example, if the user is feeling stressed, the information acquisition unit can prioritize acquiring health information related to stress. For example, if the user is relaxed, the information acquisition unit can prioritize acquiring health information related to relaxation. For example, if the user is concentrating, the information acquisition unit can prioritize acquiring health information related to concentration. In this way, by prioritizing health information according to the user's emotions, important information can be acquired preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0121] The information acquisition unit can acquire optimal information by considering the user's geographical location when acquiring blood pressure and heart rate data. For example, if the user is at home, the information acquisition unit can prioritize acquiring blood pressure and heart rate information related to activities at home. For example, if the user is at work, the information acquisition unit can prioritize acquiring blood pressure and heart rate information related to activities at work. For example, if the user is out, the information acquisition unit can prioritize acquiring blood pressure and heart rate information related to activities at the location where they are out. In this way, optimal health information can be acquired by considering geographical location information.

[0122] The information acquisition unit can improve the accuracy of data acquisition by analyzing the user's social media activity when acquiring blood pressure and heart rate information. For example, if a user posts on social media indicating they are feeling stressed, the information acquisition unit can prioritize acquiring blood pressure and heart rate information related to that stress. For example, if a user posts on social media indicating they are relaxed, the information acquisition unit can prioritize acquiring blood pressure and heart rate information related to that relaxation. For example, if a user posts on social media indicating they are concentrating, the information acquisition unit can prioritize acquiring blood pressure and heart rate information related to that concentration. In this way, the accuracy of data acquisition is improved by analyzing social media activity.

[0123] The instruction unit can estimate the user's emotions and adjust the music generation instructions based on the estimated emotions. For example, if the user is stressed, the instruction unit can prioritize generating relaxing music. For example, if the user is relaxed, the instruction unit can prioritize generating music that helps maintain that relaxation. For example, if the user is concentrating, the instruction unit can prioritize generating music that enhances concentration. By adjusting the instruction method according to the user's emotions, more appropriate music generation instructions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0124] The instruction unit can improve the accuracy of instructions when issuing music generation instructions by referring to the user's past instruction history. For example, the instruction unit can detect a specific pattern based on the user's past instruction history and issue music generation instructions based on that pattern. For example, the instruction unit can analyze instructions made during a specific time period from the user's past instruction history and issue music generation instructions appropriate for that time period. For example, the instruction unit can analyze instructions made during a specific activity based on the user's past instruction history and issue music generation instructions appropriate for that activity. In this way, the accuracy of instructions is improved by referring to past instruction history.

[0125] The instruction unit can optimize its instruction algorithm based on the user's current activity when issuing a music generation instruction. For example, if the user is exercising, the instruction unit can instruct the system to generate music suitable for exercise. For example, if the user is relaxing, the instruction unit can instruct the system to generate music suitable for relaxation. For example, if the user is working, the instruction unit can instruct the system to generate music suitable for work. By optimizing the instruction algorithm based on the user's current activity, the accuracy of the instructions is improved.

[0126] The instruction unit can estimate the user's emotions and determine the priority of the music to recommend based on the estimated emotions. For example, if the user is feeling stressed, the instruction unit can prioritize relaxing music. For example, if the user is relaxed, the instruction unit can prioritize music that helps maintain that relaxation. For example, if the user is concentrating, the instruction unit can prioritize music that enhances concentration. This allows for more appropriate music generation recommendations by prioritizing music according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0127] The instruction unit can provide optimal instructions for music generation by considering the user's geographical location. For example, if the user is at home, the instruction unit can provide instructions to generate music suitable for activities at home. For example, if the user is at work, the instruction unit can provide instructions to generate music suitable for activities at work. For example, if the user is out, the instruction unit can provide instructions to generate music suitable for activities at their destination. This makes it possible to provide optimal music generation instructions by considering geographical location information.

[0128] The instruction unit can improve the accuracy of its instructions by analyzing the user's social media activity when issuing music generation instructions. For example, if the instruction unit is posting stressful content on social media, it can issue instructions to generate music that alleviates that stress. For example, if the user is posting relaxing content on social media, it can issue instructions to generate music that maintains that relaxation. For example, if the user is posting focused content on social media, it can issue instructions to generate music that maintains that focus. In this way, analyzing social media activity improves the accuracy of the instructions.

[0129] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0130] The acquisition unit can acquire not only the user's facial expressions and posture information, but also the user's voice tone and speaking patterns. For example, while the user is speaking, the acquisition unit can capture the voice tone and speaking patterns in real time and acquire that information. The estimation unit can estimate the user's emotions and fatigue level based on the voice tone and speaking patterns acquired by the acquisition unit. For example, if the user's voice becomes higher pitched or they speak quickly, the estimation unit can estimate that the user is feeling stressed. The generation unit can generate optimal background music (BGM) based on the emotions and fatigue level estimated by the estimation unit and the BGM playback history. For example, if the user is feeling stressed, it can generate relaxing BGM. The playback unit can play the BGM generated by the generation unit. In this way, the system can improve the user's mood by estimating emotions and fatigue levels based on the user's voice tone and speaking patterns, and by generating and playing optimal BGM.

[0131] The conversion unit can not only convert humming into music, but also convert whistling sounds into music. For example, if a user whistles, it can automatically convert that whistle into music. The conversion unit can convert whistling into music using, for example, speech recognition technology. The conversion unit can convert whistling into music using, for example, speech synthesis technology. The conversion unit can convert whistling into music using, for example, music generation technology. As a result, when a user whistles, their whistle is automatically converted into music, which can lift their spirits.

[0132] The information acquisition unit can acquire not only blood pressure and heart rate information from the smartwatch, but also information on the user's sleep patterns and activity levels. For example, it can acquire information on the user's sleep patterns and activity levels in real time using the smartwatch. For example, if the user is wearing a smartwatch, the information acquisition unit can acquire information on sleep patterns and activity levels from that smartwatch. For example, the information acquisition unit can acquire information on sleep patterns and activity levels using the smartwatch's sensors. This allows the system to acquire information on sleep patterns and activity levels from the smartwatch and suggest background music appropriate to the user's current state.

[0133] The control unit can generate music not only based on user instructions, but also based on user gestures and actions. For example, if a user waves their hand or makes a specific gesture, the control unit can generate music based on that gesture. Instead of the user saying, "Play some relaxing music," the control unit can generate and play relaxing background music based on a user's gesture indicating relaxation. This allows the system to provide a music experience tailored to the user's needs by generating and playing music based on the user's gestures and actions.

[0134] The estimation unit not only estimates emotions and fatigue levels using collaborative filtering, emotion estimation, and posture estimation techniques, but can also estimate emotions and fatigue levels by analyzing the user's voice tone and speaking patterns. For example, if the user's voice becomes higher pitched or they speak quickly, the estimation unit can estimate that the user is feeling stressed. The estimation unit can more accurately estimate the user's emotions and fatigue levels by analyzing, for example, the voice tone and speaking patterns. Thus, by analyzing voice tone and speaking patterns in addition to collaborative filtering, emotion estimation, and posture estimation techniques, the accuracy of emotion and fatigue level estimation is improved.

[0135] The conversion unit can convert humming into music using speech recognition, speech synthesis, and music generation technologies, as well as convert whistling into music when a user whistles. For example, if a user whistles, the unit can automatically convert the whistle into music. The conversion unit can convert whistling into music using, for example, speech recognition technology. The conversion unit can convert whistling into music using, for example, speech synthesis technology. The conversion unit can convert whistling into music using, for example, music generation technology. As a result, by using speech recognition, speech synthesis, and music generation technologies, humming and whistling can be converted into music with high accuracy.

[0136] The data acquisition unit can estimate the user's emotions and adjust the timing of acquiring facial and posture information based on the estimated emotions. It can also adjust the timing based on the user's voice tone and speaking patterns. For example, if the user is stressed, the unit can frequently acquire voice tone and speaking patterns to track emotional changes in real time. Conversely, if the user is relaxed, the unit can acquire voice tone and speaking patterns at regular intervals, avoiding excessive data collection. This allows for the acquisition of more appropriate information by adjusting the timing of voice tone and speaking pattern acquisition according to the user's emotions.

[0137] The data acquisition unit can analyze not only the user's past facial expressions and posture history, but also the user's past voice tone and speaking patterns. For example, it can detect specific patterns based on the voice tone and speaking patterns that the user has frequently displayed in the past, and acquire information based on those patterns. The data acquisition unit can also analyze changes in voice tone and speaking patterns during specific time periods from the user's past history, and acquire information with a focus on those time periods. This allows for the selection of the optimal acquisition method by analyzing past history, enabling efficient information acquisition.

[0138] The data acquisition unit can filter not only facial expression and posture information based on the user's current activity level, but also based on the user's voice tone and speaking patterns. For example, if the user is exercising, the unit can prioritize acquiring voice tone and speaking patterns related to exercise and filter out other information. Similarly, if the user is relaxed, the unit can prioritize acquiring voice tone and speaking patterns related to relaxation and filter out other information. This allows for the acquisition of highly relevant information by filtering it based on the user's current activity level.

[0139] The data acquisition unit can estimate the user's emotions and determine the priority of information to acquire based on those emotions. It can also prioritize information based on the user's tone of voice and speaking patterns. For example, if the user is stressed, the unit can prioritize acquiring tone of voice and speaking patterns associated with stress. Similarly, if the user is relaxed, the unit can prioritize acquiring tone of voice and speaking patterns associated with relaxation. This allows for the acquisition of important information by prioritizing it according to the user's emotions.

[0140] The following briefly describes the processing flow for example form 2.

[0141] Step 1: The acquisition unit acquires the user's facial expressions and posture information. The acquisition unit captures the user's facial expressions and posture information in real time, for example, using the camera of a PC or smartphone. For example, when a user is working in front of a PC, the acquisition unit can capture the user's facial expressions and posture with the camera and acquire that information. Step 2: The estimation unit estimates emotions and fatigue levels based on the information acquired by the acquisition unit. The estimation unit can estimate the user's emotions using, for example, facial expression recognition technology. The estimation unit can estimate the user's fatigue level using, for example, posture analysis technology. The estimation unit can estimate the user's emotions and fatigue level using, for example, collaborative filtering technology. Step 3: The generation unit generates optimal background music (BGM) based on the emotions, fatigue levels, and BGM playback history estimated by the estimation unit. For example, the generation unit can use a generation AI to generate BGM that corresponds to the user's emotions and fatigue levels. For example, if the user is tired, the generation unit can generate relaxing BGM. For example, if the user is concentrating, the generation unit can generate BGM that enhances concentration. Step 4: The playback unit plays the background music (BGM) generated by the generation unit. The playback unit can play the BGM using, for example, speakers or headphones. The playback unit can play the BGM based on user instructions. For example, if the user instructs the playback unit to "play relaxing music," the playback unit can play relaxing BGM based on that instruction.

[0142] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0143] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0144] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the acquisition unit, estimation unit, generation unit, playback unit, conversion unit, information acquisition unit, and instruction unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires the user's facial expressions and posture information using the camera 42 of the smart device 14. The estimation unit is implemented by the specific processing unit 290 of the data processing unit 12 and estimates emotions and fatigue levels based on the acquired information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates optimal background music based on the estimated emotions and fatigue levels. The playback unit plays the generated background music using the speaker 40B of the smart device 14. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts humming into music. The information acquisition unit acquires blood pressure and heart rate information from a smartwatch via the communication I / F 44 of the smart device 14. The instruction unit is implemented by the control unit 46A of the smart device 14 and generates and plays music based on user instructions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0146] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0147] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the acquisition unit, estimation unit, generation unit, playback unit, conversion unit, information acquisition unit, and instruction unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires the user's facial expressions and posture information using the camera 42 of the smart glasses 214. The estimation unit is implemented by the specific processing unit 290 of the data processing unit 12 and estimates emotions and fatigue levels based on the acquired information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates optimal background music based on the estimated emotions and fatigue levels. The playback unit plays the generated background music using the speaker 240 of the smart glasses 214. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts humming into music. The information acquisition unit acquires blood pressure and heart rate information from a smartwatch via the communication I / F 44 of the smart glasses 214. The instruction unit is implemented by the control unit 46A of the smart glasses 214 and generates and plays music based on user instructions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0162] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0163] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0164] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0165] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0166] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0167] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0168] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0169] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the acquisition unit, estimation unit, generation unit, playback unit, conversion unit, information acquisition unit, and instruction unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires the user's facial expressions and posture information using the camera 42 of the headset terminal 314. The estimation unit is implemented by the specific processing unit 290 of the data processing unit 12 and estimates emotions and fatigue levels based on the acquired information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates optimal background music based on the estimated emotions and fatigue levels. The playback unit plays the generated background music using the speaker 240 of the headset terminal 314. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts humming into music. The information acquisition unit acquires blood pressure and heart rate information from the smartwatch via the communication I / F 44 of the headset terminal 314. The instruction unit is implemented by the control unit 46A of the headset terminal 314, which generates and plays music based on user instructions. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0178] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0179] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0180] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0181] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0182] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0183] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0184] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0185] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0186] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0187] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0188] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0189] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0190] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0191] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0192] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0193] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0194] Each of the multiple elements described above, including the acquisition unit, estimation unit, generation unit, playback unit, conversion unit, information acquisition unit, and instruction unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires the user's facial expressions and posture information using the camera 42 of the robot 414. The estimation unit is implemented by the specific processing unit 290 of the data processing unit 12 and estimates emotions and fatigue levels based on the acquired information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates optimal background music based on the estimated emotions and fatigue levels. The playback unit plays the generated background music using the speaker 240 of the robot 414. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts humming into music. The information acquisition unit acquires blood pressure and heart rate information from a smartwatch via the communication I / F 44 of the robot 414. The instruction unit is implemented by the control unit 46A of the robot 414 and generates and plays music based on user instructions. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0195] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0196] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0197] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0198] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0199] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0200] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0201] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0202] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0203] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0204] 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.

[0205] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0206] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0207] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0208] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0209] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0210] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0211] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0212] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0213] (Note 1) An acquisition unit that acquires information on the user's facial expressions and posture, An estimation unit that estimates emotions and fatigue levels based on the information acquired by the acquisition unit, A generation unit that generates optimal background music based on the emotions, fatigue level, and BGM playback history estimated by the estimation unit, The system includes a playback unit that plays the background music generated by the generation unit. A system characterized by the following features. (Note 2) It features a conversion unit that converts humming into music. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with an information acquisition unit that obtains blood pressure and heart rate information from a smartwatch. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a control unit that generates music based on user instructions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The estimation unit, Emotions and fatigue levels are estimated using collaborative filtering, emotion estimation, and posture estimation techniques. The system described in Appendix 1, characterized by the features described herein. (Note 6) The conversion unit is Using speech recognition, speech synthesis, and music generation technologies, we convert humming into music. The system described in Appendix 2, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of acquiring facial and posture information based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, The system analyzes the user's past facial expressions and posture history to select the optimal acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When acquiring facial expression and posture information, filtering is performed based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, It estimates the user's emotions and determines the priority of information to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring facial expression and posture information, the system prioritizes acquiring highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When acquiring facial expression and posture information, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The estimation unit, The system estimates the user's emotions and adjusts the methods for estimating emotions and fatigue levels based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The estimation unit, When estimating emotions and fatigue levels, the system improves accuracy by referencing the user's past emotional history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The estimation unit, When estimating emotions and fatigue levels, the estimation algorithm is optimized based on the user's current activity level. The system described in Appendix 1, characterized by the features described herein. (Note 16) The estimation unit, It estimates the user's emotions and adjusts how the estimation results are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The estimation unit, When estimating emotions and fatigue levels, the system takes the user's geographical location into account to improve the accuracy of the estimation. The system described in Appendix 1, characterized by the features described herein. (Note 18) The estimation unit, Analyzing users' social media activity improves the accuracy of estimations of emotions and fatigue levels. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is The system estimates the user's emotions and adjusts the BGM generation method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating background music (BGM), the system improves the accuracy of the generation by referencing the user's past BGM playback history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating background music, the generation algorithm is optimized based on the user's current activity. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and determines the priority of background music to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating background music (BGM), the system takes the user's geographical location into consideration to generate the most suitable BGM. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating background music, we analyze users' social media activity to improve the accuracy of the generation process. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned regeneration unit is It estimates the user's emotions and adjusts the BGM playback method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned regeneration unit is When playing background music, the system improves playback accuracy by referencing the user's past playback history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned regeneration unit is When playing background music, the playback algorithm is optimized based on the user's current activity. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned regeneration unit is It estimates the user's emotions and determines the priority of background music to play based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned regeneration unit is When playing background music, the system will consider the user's geographical location to play the most suitable music. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned regeneration unit is When playing background music, the system analyzes the user's social media activity to improve playback accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 31) The conversion unit is It estimates the user's emotions and adjusts the humming conversion method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The conversion unit is When converting hummed tunes, the system improves conversion accuracy by referencing the user's past humming history. The system described in Appendix 2, characterized by the features described herein. (Note 33) The conversion unit is When converting humming, the conversion algorithm is optimized based on the user's current activity. The system described in Appendix 2, characterized by the features described herein. (Note 34) The conversion unit is It estimates the user's emotions and determines the priority of humming based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The conversion unit is When converting hummed tunes, the system takes the user's geographical location into consideration to perform the optimal conversion. The system described in Appendix 2, characterized by the features described herein. (Note 36) The conversion unit is When converting hummed tunes, the system analyzes the user's social media activity to improve the accuracy of the conversion. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned information acquisition unit, The system estimates the user's emotions and adjusts the timing of blood pressure and heart rate data acquisition based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned information acquisition unit, When acquiring blood pressure and heart rate information, the system improves accuracy by referencing the user's past health data. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned information acquisition unit, When acquiring blood pressure and heart rate information, the acquisition algorithm is optimized based on the user's current activity level. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned information acquisition unit, It estimates the user's emotions and determines the priority of health information to acquire based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned information acquisition unit, When acquiring blood pressure and heart rate information, the system takes the user's geographical location into consideration to acquire the most optimal information. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned information acquisition unit, When acquiring blood pressure and heart rate information, the system analyzes the user's social media activity to improve the accuracy of the acquisition. The system described in Appendix 3, characterized by the features described herein. (Note 43) The indicator unit is, It estimates the user's emotions and adjusts the music generation instructions based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 44) The indicator unit is, When issuing music generation commands, the system references the user's past command history to improve the accuracy of the commands. The system described in Appendix 4, characterized by the features described herein. (Note 45) The indicator unit is, When issuing music generation commands, the command algorithm is optimized based on the user's current activity status. The system described in Appendix 4, characterized by the features described herein. (Note 46) The indicator unit is, It estimates the user's emotions and determines the priority of music recommendations based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 47) The indicator unit is, When issuing instructions for music generation, the system takes the user's geographical location into consideration to provide the most optimal instructions. The system described in Appendix 4, characterized by the features described herein. (Note 48) The indicator unit is, When issuing music generation instructions, the system analyzes the user's social media activity to improve the accuracy of the instructions. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0214] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that acquires information on the user's facial expressions and posture, An estimation unit that estimates emotions and fatigue levels based on the information acquired by the acquisition unit, A generation unit that generates optimal background music based on the emotions, fatigue level, and BGM playback history estimated by the estimation unit, The system includes a playback unit that plays the background music generated by the generation unit. A system characterized by the following features.

2. It features a conversion unit that converts humming into music. The system according to feature 1.

3. It is equipped with an information acquisition unit that obtains blood pressure and heart rate information from a smartwatch. The system according to feature 1.

4. It includes a control unit that generates music based on user instructions. The system according to feature 1.

5. The estimation unit, Emotions and fatigue levels are estimated using collaborative filtering, emotion estimation, and posture estimation techniques. The system according to feature 1.

6. The conversion unit is Using speech recognition, speech synthesis, and music generation technologies, we convert humming into music. The system according to feature 2.

7. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of acquiring facial and posture information based on the estimated emotions. The system according to feature 1.

8. The acquisition unit is, The system analyzes the user's past facial expressions and posture history to select the optimal acquisition method. The system according to feature 1.

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