system

Abstract

The system according to this embodiment aims to automatically provide the most suitable background music based on the user's psychological state. [Solution] The system according to the embodiment comprises a detection unit, an analysis unit, a generation unit, and a provision unit. The detection unit detects the psychological state. The analysis unit analyzes the data detected by the detection unit. The generation unit generates background music (BGM) based on the analysis results obtained by the analysis unit. The provision unit provides 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 method for controlling a persona chatbot, which is performed by at least one processor, and includes 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, there is a problem that it is difficult to automatically select and provide music that suits the user's psychological state, and the concentration and relaxation effects cannot be maximized.

[0005] The system according to the embodiment aims to automatically provide an optimal BGM based on the user's psychological state.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a detection unit, an analysis unit, a generation unit, and a provision unit. The detection unit detects the psychological state. The analysis unit analyzes the data detected by the detection unit. The generation unit generates background music (BGM) based on the analysis results obtained by the analysis unit. The provision unit provides the BGM generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically provide the optimal background music based on the user's psychological state. [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, and the like. The communication I / F controls 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 ⒊0, 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 receiving 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 receiving 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 MoodSync system according to an embodiment of the present invention is a system that uses AI and sensors to detect the user's psychological state and automatically plays the optimal background music (BGM). The MoodSync system solves the problem that it is difficult to choose music that suits one's psychological state, and that it is often difficult to maximize the effects of concentration and relaxation. It also solves the problem that biofeedback data from wearable devices and other sources is not easily utilized, and there is a lack of tools that automatically optimize mental care and concentration improvement. The MoodSync system analyzes the user's psychological state in real time using AI and sensors and automatically generates and selects the optimal BGM. For example, sensors detect changes in heart rate and emotion, and the AI ​​analyzes this data. Based on the analysis results, it provides energizing BGM when the user wants to concentrate and calming BGM when the user wants to relax. Furthermore, the AI ​​learns from the user's feedback and provides a personalized music experience over time. This service integrates with daily life through coexistence with bone conduction earphones and open earphones, allowing users to easily become aware of and adjust their psychological state. For example, by selecting modes such as focus mode, boost mode, relaxation mode, and meditation mode, it supports even intentional changes in consciousness. It also has a function to enhance self-esteem along with background music, supporting users in leading a fulfilling life centered on themselves. This system allows users to easily become aware of and adjust their psychological state. For example, when feeling stressed, selecting relaxation mode and listening to calming background music can reduce stress. Also, when wanting to improve concentration, selecting focus mode and listening to energizing background music can improve concentration. Furthermore, with functions that enhance self-esteem and motivation, users can lead a fulfilling life centered on themselves. In this way, the MoodSync system can maximize the effects of concentration and relaxation by automatically detecting the user's psychological state and providing the optimal background music.

[0029] The MoodSync system according to this embodiment comprises a detection unit, an analysis unit, a generation unit, and a provision unit. The detection unit detects the user's psychological state. For example, the detection unit detects changes in heart rate and emotion using sensors. The detection unit can measure the user's heart rate in real time using a heart rate sensor. The detection unit can also detect changes in the user's emotion using facial expression recognition technology. Furthermore, the detection unit can analyze the tone and speed of the user's voice using voice analysis technology to detect changes in emotion. The analysis unit analyzes the data detected by the detection unit. For example, the analysis unit uses AI to analyze heart rate and changes in emotion and determine the user's psychological state. The analysis unit can use machine learning algorithms to analyze the detected data and classify the user's psychological state. Furthermore, the analysis unit can use deep learning technology to analyze complex data patterns and make a more accurate determination of the psychological state. Furthermore, the analysis unit can use natural language processing technology to analyze the user's voice data and detect changes in emotion. The generation unit generates background music (BGM) based on the analysis results obtained by the analysis unit. The generation unit can, for example, use AI to automatically generate BGM that corresponds to the user's psychological state. The generation unit can create BGM that is optimal for the user's psychological state using a music generation algorithm. The generation unit can also select the most suitable song from an existing music library and provide it to the user. Furthermore, the generation unit can learn from user feedback and provide a personalized music experience over time. The delivery unit provides the BGM generated by the generation unit. The delivery unit provides BGM to the user using, for example, bone conduction earphones or open earphones. The delivery unit can deliver BGM in real time using streaming technology. The delivery unit can also download BGM to the user's device so that it can be played offline. Furthermore, the delivery unit can collect user feedback and optimize the method of providing BGM. As a result, the MoodSync system according to this embodiment can maximize concentration and relaxation effects by automatically detecting the user's psychological state and providing optimal BGM.

[0030] The detection unit detects the user's psychological state. For example, it uses sensors to detect changes in heart rate and emotion. Specifically, it can measure the user's heart rate in real time using a heart rate sensor. The heart rate sensor detects even minute fluctuations in heart rate with high precision by making direct contact with the user's skin. This allows for real-time understanding of the user's stress level and relaxation state. The detection unit can also detect changes in the user's emotions using facial recognition technology. Facial recognition technology uses a camera to capture the user's face and analyzes facial feature points to identify emotions such as joy, sadness, and anger. Furthermore, the detection unit can also use voice analysis technology to analyze the tone and speed of the user's voice and detect changes in emotion. Voice analysis technology analyzes not only the content of the user's speech but also voice features such as pitch, intensity, and rhythm to detect changes in emotion with high precision. As a result, the detection unit can capture the user's psychological state from multiple angles and provide more accurate data. Furthermore, the detection unit centrally manages this data and transmits it to the analysis unit in real time, thereby improving the overall efficiency of the system.

[0031] The analysis unit analyzes the data detected by the detection unit. For example, the analysis unit uses AI to analyze heart rate and emotional changes to determine the user's psychological state. Specifically, it can use machine learning algorithms to analyze detected data and classify the user's psychological state. Machine learning algorithms learn from past data and predict the user's psychological state with high accuracy from heart rate, facial expressions, and voice characteristics. The analysis unit can also use deep learning technology to analyze complex data patterns and make more accurate determinations of the psychological state. Deep learning technology uses multi-layered neural networks to extract features from large amounts of data and classify psychological states with high accuracy. Furthermore, the analysis unit can use natural language processing technology to analyze the user's voice data and detect emotional changes. Natural language processing technology analyzes the user's speech content and determines emotional changes and stress levels with high accuracy. As a result, the analysis unit can integrate the diverse data provided by the detection unit and comprehensively evaluate the user's psychological state. Furthermore, the analysis unit can process data in real time and respond quickly to changes in the user's psychological state.

[0032] The generation unit generates background music (BGM) based on the analysis results obtained by the analysis unit. For example, the generation unit can use AI to automatically generate BGM tailored to the user's psychological state. Specifically, it can create BGM optimally suited to the user's psychological state using a music generation algorithm. The music generation algorithm adjusts tempo, melody, and harmony based on the user's heart rate and emotional changes to generate music that promotes relaxation and concentration. The generation unit can also select and provide the most suitable song from an existing music library. The music library contains songs of various genres and moods, allowing for quick selection of the most suitable song for the user's psychological state. Furthermore, the generation unit can learn from user feedback and provide a personalized music experience over time. Based on user feedback, it adjusts the music generation algorithm to provide a more individualized music experience. This allows the generation unit to provide BGM optimally suited to the user's psychological state, maximizing relaxation and concentration.

[0033] The provider unit provides background music (BGM) generated by the generator unit. The provider unit delivers BGM to users using, for example, bone conduction earphones or open-type earphones. Specifically, bone conduction earphones transmit sound without blocking the ears, allowing users to enjoy BGM without being affected by external sounds. Open-type earphones fit lightly in the ear, allowing for comfortable BGM enjoyment even during prolonged use. The provider unit can deliver BGM in real time using streaming technology. Streaming technology delivers high-quality music in real time over the internet, enabling users to enjoy BGM without interruption. The provider unit can also download BGM to the user's device for offline playback. This allows users to enjoy BGM even in environments with unstable internet connections. Furthermore, the provider unit can collect user feedback and optimize the BGM delivery method. Based on user feedback, it adjusts volume and playback methods to provide a more comfortable music experience. This allows the provider unit to provide users with optimal BGM, maximizing relaxation and concentration.

[0034] The service provider can learn from user feedback and provide a personalized music experience. For example, the service provider can collect user ratings and usage history and analyze the feedback using AI. The service provider can input user feedback data into a machine learning model to learn user preferences and behavioral patterns. The service provider can also adjust the BGM selection criteria based on user feedback to provide a more personalized music experience. Furthermore, the service provider can reflect user feedback in real time and optimize the way BGM is provided. This allows for a more personalized music experience by learning from user feedback. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user feedback data into a generating AI and have the generating AI perform the provision of a personalized music experience.

[0035] The service provider can have functions to enhance self-esteem. For example, the service provider can provide users with positive messages to enhance their self-esteem. The service provider can use AI to generate positive messages tailored to the user's psychological state. The service provider can also adjust music selection criteria to provide background music that enhances the user's self-esteem. Furthermore, the service provider can learn from user feedback and optimize functions to enhance self-esteem. This allows the service provider to support the user's mental health by enhancing their self-esteem. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user psychological state data into a generating AI and have the generating AI generate messages to enhance self-esteem.

[0036] The service provider can include functions to select modes such as concentration mode, boost mode, relaxation mode, and meditation mode. For example, the service provider can select a mode according to the user's purpose and optimize their psychological state. The service provider can use AI to analyze the user's psychological state data and suggest the optimal mode. The service provider can also learn from user feedback and optimize the settings for each mode. Furthermore, the service provider can adjust the BGM selection criteria based on the user's selected mode to provide an optimal musical experience. This allows the user's psychological state to be optimized by selecting a mode according to their purpose. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's psychological state data into a generating AI and have the generating AI execute the settings for each mode.

[0037] The detection unit can detect changes in heart rate and emotion using sensors. For example, the detection unit measures the user's heart rate in real time using a heart rate sensor. The detection unit collects changes in heart rate as data and can understand the user's psychological state. The detection unit can also detect changes in the user's emotion using facial recognition technology. The detection unit can capture the user's facial expression using a camera and analyze changes in facial expression using AI. Furthermore, the detection unit can analyze the tone and speed of the user's voice using voice analysis technology and detect changes in emotion. The detection unit can collect the user's voice using a microphone and analyze the voice data using AI. As a result, by detecting changes in heart rate and emotion, the psychological state can be understood in real time. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data acquired by the heart rate sensor into a generating AI and analyze changes in heart rate.

[0038] The analysis unit can analyze detected data and determine the user's psychological state. For example, the analysis unit can use AI to analyze heart rate and emotional changes to determine the user's psychological state. The analysis unit can use machine learning algorithms to analyze detected data and classify the user's psychological state. Furthermore, the analysis unit can use deep learning technology to analyze complex data patterns and make more accurate determinations of the psychological state. In addition, the analysis unit can use natural language processing technology to analyze the user's voice data and detect emotional changes. The analysis unit can convert the voice data into text and use AI to analyze emotional changes. This allows for accurate determination of the user's psychological state by analyzing the detected data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input detected data into a generating AI and have the generating AI perform the psychological state determination.

[0039] The detection unit can select the optimal detection method by referring to the user's past psychological state data when detection occurs. For example, the detection unit can focus detection on time periods when the user has previously experienced stress. The detection unit can retrieve past psychological state data from a database and analyze it using AI. The detection unit can also predict emotional changes in specific events or situations based on the user's past psychological state data and adjust the detection method accordingly. Furthermore, the detection unit can analyze the user's past psychological state data and select the most effective detection method. This allows for the selection of a more effective detection method by referring to past data. Some or all of the above-described processes in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input past psychological state data into a generating AI and have the generating AI select the optimal detection method.

[0040] The detection unit can adjust the sensor sensitivity based on the user's current activity and environment upon detection. For example, if the user is exercising, the detection unit can increase the sensor sensitivity to detect changes in heart rate in detail. The detection unit can measure the amount of exercise with the sensor and analyze it using AI. Furthermore, if the user is in a quiet environment, the detection unit can lower the sensor sensitivity to detect subtle emotional changes. In addition, if the user is in a noisy environment, the detection unit can adjust the sensor sensitivity to eliminate noise and obtain accurate data. In this way, accurate data can be obtained by adjusting the sensor sensitivity according to the activity and environment. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user activity data into a generating AI and have the generating AI perform the sensor sensitivity adjustment.

[0041] The detection unit can prioritize the detection of highly relevant data by considering the user's geographical location information when detection occurs. For example, if the user is at home, the detection unit will prioritize the detection of data related to relaxation. The detection unit can identify the user's geographical location using GPS data and analyze it using AI. Furthermore, if the user is at work, the detection unit can prioritize the detection of data related to concentration. In addition, if the user is in a public place, the detection unit can prioritize the detection of data related to stress. In this way, by considering geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's geographical location data into a generating AI and cause the generating AI to perform the priority detection of highly relevant data.

[0042] The detection unit can analyze the user's social media activity and detect related psychological states upon detection. For example, if a user posts on social media indicating they are stressed, the detection unit can detect that psychological state. The detection unit can analyze the content of social media posts using AI and estimate the user's emotions. The detection unit can also detect the psychological state if a user posts on social media indicating they are relaxed. Furthermore, the detection unit can detect the psychological state if a user posts on social media indicating they are focused. This allows for a more accurate detection of the user's psychological state by analyzing social media activity. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's social media data into a generating AI and have the generating AI perform the psychological state detection.

[0043] The analysis unit can improve the accuracy of its analysis by referring to past analysis data during the analysis process. For example, the analysis unit can refer to the user's past psychological state data and improve accuracy by comparing it with the current analysis results. The analysis unit can retrieve psychological state data from past databases and analyze it using AI. The analysis unit can also extract specific patterns from past analysis data and apply them to the current analysis. Furthermore, the analysis unit can optimize the analysis algorithm based on past analysis data to improve accuracy. In this way, the accuracy of the analysis can be improved by referring to past data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0044] The analysis unit can apply different analysis methods depending on the category of psychological state during analysis. For example, in the case of a stressed state, the analysis unit applies an analysis method specifically designed for stress reduction. The analysis unit can use AI to analyze the user's psychological state data and select the optimal analysis method. Furthermore, in the case of a relaxed state, the analysis unit can apply an analysis method to maintain that relaxed state. In addition, in the case of a focused state, the analysis unit can apply an analysis method to enhance concentration. By applying an analysis method appropriate to the psychological state, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's psychological state data into a generating AI and have the generating AI select the analysis method.

[0045] The analysis unit can determine the priority of analysis based on the submission timing of the detected data during analysis. For example, the analysis unit may prioritize the analysis of data detected in real time. The analysis unit can identify the submission timing of data using timestamps and perform analysis using AI. The analysis unit can also refer to past data and prioritize the analysis of data of high importance. Furthermore, the analysis unit can determine the priority of analysis based on the user's schedule. This allows for the priority analysis of important data by determining priorities based on submission timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission timing of the detected data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0046] The analysis unit can improve the accuracy of its analysis by referring to relevant literature data during the analysis process. For example, the analysis unit can refer to the latest research papers and optimize its analysis algorithm. The analysis unit can retrieve academic papers and technical reports from a database and analyze them using AI. The analysis unit can also extract specific patterns from relevant literature data and apply them to the analysis. Furthermore, the analysis unit can verify the analysis results based on the literature data and improve their accuracy. In this way, the accuracy of the analysis can be improved by referring to literature data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0047] The generation unit can optimize its generation algorithm by referring to past BGM generation data during generation. For example, the generation unit can refer to the user's past BGM generation data and optimize the current generation algorithm. The generation unit can retrieve BGM generation data from a past database and analyze it using AI. The generation unit can also extract specific patterns from past BGM generation data and apply them to the current generation. Furthermore, the generation unit can optimize the generation algorithm and improve accuracy based on past BGM generation data. In this way, the generation algorithm can be optimized by referring to past data. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past BGM generation data into a generation AI and have the generation AI perform the optimization of the generation algorithm.

[0048] The generation unit can apply different BGM generation methods depending on the category of psychological state during generation. For example, if the user is stressed, the generation unit can apply a BGM generation method that has a relaxing effect. The generation unit can use AI to analyze the user's psychological state data and select the optimal BGM generation method. Furthermore, if the user is relaxed, the generation unit can apply a BGM generation method to maintain that relaxed state. In addition, if the user is focused, the generation unit can apply a BGM generation method to enhance concentration. By applying a BGM generation method according to the psychological state, more appropriate BGM can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's psychological state data into a generation AI and have the generation AI select a BGM generation method.

[0049] The generation unit can determine the generation priority based on the submission timing of the background music (BGM) at the time of generation. For example, the generation unit can prioritize the generation of BGM needed in real time. The generation unit can identify the submission timing of BGM using timestamps and analyze it using AI. The generation unit can also prioritize the generation of necessary BGM based on the user's schedule. Furthermore, the generation unit can refer to past data and prioritize the generation of high-priority BGM. This allows for the provision of appropriate BGM at the required time by determining priorities based on submission timing. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the BGM submission timing into the generation AI and have the generation AI perform the generation priority determination.

[0050] The generation unit can improve the accuracy of generation by referring to relevant music data during the generation process. For example, the generation unit can refer to the latest music data and optimize the generation algorithm. The generation unit can retrieve music libraries and music metadata from a database and analyze them using AI. The generation unit can also extract specific patterns from relevant music data and apply them to generation. Furthermore, the generation unit can verify the generation results based on the music data and improve accuracy. In this way, generation accuracy can be improved by referring to music data. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input relevant music data into a generation AI and have the generation AI perform the generation accuracy improvement.

[0051] The service provider can select the optimal service delivery method by referring to the user's past feedback at the time of delivery. For example, the service provider can refer to the user's past feedback and provide the optimal background music. The service provider can retrieve past feedback data from a database and analyze it using AI. The service provider can also extract specific patterns from past feedback and optimize the service delivery method. Furthermore, the service provider can customize the service delivery method based on the user's feedback. This allows the service provider to select the optimal service delivery method by referring to past feedback. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past feedback data into a generating AI and have the generating AI select the service delivery method.

[0052] The service provider can customize the means of providing background music (BGM) based on the user's current activity level at the time of service. For example, if the user is exercising, the service provider can provide BGM through bone conduction earphones. The service provider can acquire user activity data using sensors and analyze it using AI. The service provider can also provide BGM through open earphones if the user is relaxed. Furthermore, if the user is concentrating, the service provider can provide BGM through noise-canceling earphones. This allows for the provision of more appropriate BGM by customizing the means of service according to the user's activity level. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user activity data into a generating AI and have the generating AI perform the customization of the service provider.

[0053] The service provider can provide optimal background music (BGM) by considering the user's geographical location information at the time of delivery. For example, if the user is at home, the service provider can provide relaxing BGM. The service provider can identify the user's geographical location using GPS data and analyze it using AI. Furthermore, if the user is at work, the service provider can provide BGM that enhances concentration. In addition, if the user is in a public place, the service provider can provide BGM that reduces stress. In this way, the service provider can provide optimal BGM by considering geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing optimal BGM.

[0054] The service provider can analyze the user's social media activity and propose a method for providing background music (BGM) at the time of delivery. For example, if the service provider is posting on social media that indicates stress, it can provide BGM that matches that psychological state. The service provider can use AI to analyze the content of social media posts and estimate the user's emotions. Furthermore, if the service provider is posting on social media that indicates relaxation, it can also provide BGM that matches that psychological state. In addition, if the service provider is posting on social media that indicates concentration, it can also provide BGM that matches that psychological state. In this way, by analyzing social media activity, it is possible to provide more appropriate BGM. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI execute a proposal for a method of provision.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The MoodSync system can also include an exercise suggestion unit that provides exercise recommendations based on the user's psychological state. For example, the exercise suggestion unit can suggest yoga or stretching when the user wants to relax, and light exercise when the user wants to concentrate. Furthermore, the exercise suggestion unit can learn from user feedback and provide personalized exercise recommendations over time. It can also automatically adjust the type and intensity of exercise according to the user's psychological state to suggest the optimal workout. This allows users to more effectively regulate their psychological state through both music and exercise.

[0057] The MoodSync system can also include a meal suggestion unit that provides meal recommendations based on the user's psychological state. For example, the meal suggestion unit can suggest a light snack when the user wants to relax and a meal to replenish energy when the user wants to concentrate. Furthermore, the meal suggestion unit can learn from user feedback and provide personalized meal recommendations over time. It can also automatically adjust the type and quantity of food according to the user's psychological state to suggest the optimal meal. This allows users to more effectively manage their psychological state through both music and food.

[0058] The MoodSync system can also include a break suggestion unit that proposes break timings based on the user's psychological state. For example, the break suggestion unit can suggest short breaks when the user is feeling stressed and longer breaks when their concentration is low. Furthermore, the break suggestion unit can learn from user feedback and provide personalized break suggestions over time. It can also automatically adjust the frequency and length of breaks according to the user's psychological state, suggesting the optimal break. This allows users to more effectively manage their psychological state through both music and breaks.

[0059] The MoodSync system can also include a learning suggestion unit that provides learning suggestions based on the user's psychological state. For example, the learning suggestion unit can suggest learning content with a relaxing effect when the user wants to relax, and learning content that enhances concentration when the user wants to concentrate. The learning suggestion unit can also learn from user feedback and provide personalized learning suggestions over time. Furthermore, the learning suggestion unit can automatically adjust the type and difficulty level of learning according to the user's psychological state and suggest the optimal learning. This allows users to more effectively adjust their psychological state through both music and learning.

[0060] The MoodSync system can also include a communication suggestion unit that proposes communication based on the user's psychological state. For example, the communication suggestion unit can suggest relaxing conversations when the user wants to relax, and conversations that enhance concentration when the user wants to concentrate. The communication suggestion unit can also learn from user feedback and provide personalized communication suggestions over time. Furthermore, the communication suggestion unit can automatically adjust the content and frequency of communication according to the user's psychological state and propose the optimal communication. This allows users to more effectively adjust their psychological state through both music and communication.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The detection unit detects the psychological state. The detection unit uses sensors to detect changes in heart rate and emotion. For example, it can measure the user's heart rate in real time using a heart rate sensor and detect changes in the user's emotion using facial recognition technology. It can also analyze the tone and speed of the user's voice using voice analysis technology to detect changes in emotion. Step 2: The analysis unit analyzes the data detected by the detection unit. The analysis unit uses AI to analyze heart rate and emotional changes to determine the user's psychological state. Machine learning algorithms and deep learning technologies are used to analyze the detected data and classify the user's psychological state. It can also use natural language processing technology to analyze the user's voice data and detect changes in emotion. Step 3: The generation unit generates background music (BGM) based on the analysis results obtained by the analysis unit. The generation unit uses AI to automatically generate BGM that matches the user's psychological state. It can create the optimal BGM for the user's psychological state using a music generation algorithm, or it can select the best song from an existing music library. Furthermore, it can learn from user feedback and provide a personalized music experience over time. Step 4: The provider unit provides the background music (BGM) generated by the generator unit. The provider unit delivers the BGM to the user using bone conduction earphones or open-type earphones. It is also possible to deliver the BGM in real time using streaming technology, and to allow the BGM to be downloaded to the user's device for offline playback. Furthermore, user feedback can be collected to optimize the BGM delivery method.

[0063] (Example of form 2) The MoodSync system according to an embodiment of the present invention is a system that uses AI and sensors to detect the user's psychological state and automatically plays the optimal background music (BGM). The MoodSync system solves the problem that it is difficult to choose music that suits one's psychological state, and that it is often difficult to maximize the effects of concentration and relaxation. It also solves the problem that biofeedback data from wearable devices and other sources is not easily utilized, and there is a lack of tools that automatically optimize mental care and concentration improvement. The MoodSync system analyzes the user's psychological state in real time using AI and sensors and automatically generates and selects the optimal BGM. For example, sensors detect changes in heart rate and emotion, and the AI ​​analyzes this data. Based on the analysis results, it provides energizing BGM when the user wants to concentrate and calming BGM when the user wants to relax. Furthermore, the AI ​​learns from the user's feedback and provides a personalized music experience over time. This service integrates with daily life through coexistence with bone conduction earphones and open earphones, allowing users to easily become aware of and adjust their psychological state. For example, by selecting modes such as focus mode, boost mode, relaxation mode, and meditation mode, it supports even intentional changes in consciousness. It also has a function to enhance self-esteem along with background music, supporting users in leading a fulfilling life centered on themselves. This system allows users to easily become aware of and adjust their psychological state. For example, when feeling stressed, selecting relaxation mode and listening to calming background music can reduce stress. Also, when wanting to improve concentration, selecting focus mode and listening to energizing background music can improve concentration. Furthermore, with functions that enhance self-esteem and motivation, users can lead a fulfilling life centered on themselves. In this way, the MoodSync system can maximize the effects of concentration and relaxation by automatically detecting the user's psychological state and providing the optimal background music.

[0064] The MoodSync system according to this embodiment comprises a detection unit, an analysis unit, a generation unit, and a provision unit. The detection unit detects the user's psychological state. For example, the detection unit detects changes in heart rate and emotion using sensors. The detection unit can measure the user's heart rate in real time using a heart rate sensor. The detection unit can also detect changes in the user's emotion using facial expression recognition technology. Furthermore, the detection unit can analyze the tone and speed of the user's voice using voice analysis technology to detect changes in emotion. The analysis unit analyzes the data detected by the detection unit. For example, the analysis unit uses AI to analyze heart rate and changes in emotion and determine the user's psychological state. The analysis unit can use machine learning algorithms to analyze the detected data and classify the user's psychological state. Furthermore, the analysis unit can use deep learning technology to analyze complex data patterns and make a more accurate determination of the psychological state. Furthermore, the analysis unit can use natural language processing technology to analyze the user's voice data and detect changes in emotion. The generation unit generates background music (BGM) based on the analysis results obtained by the analysis unit. The generation unit can, for example, use AI to automatically generate BGM that corresponds to the user's psychological state. The generation unit can create BGM that is optimal for the user's psychological state using a music generation algorithm. The generation unit can also select the most suitable song from an existing music library and provide it to the user. Furthermore, the generation unit can learn from user feedback and provide a personalized music experience over time. The delivery unit provides the BGM generated by the generation unit. The delivery unit provides BGM to the user using, for example, bone conduction earphones or open earphones. The delivery unit can deliver BGM in real time using streaming technology. The delivery unit can also download BGM to the user's device so that it can be played offline. Furthermore, the delivery unit can collect user feedback and optimize the method of providing BGM. As a result, the MoodSync system according to this embodiment can maximize concentration and relaxation effects by automatically detecting the user's psychological state and providing optimal BGM.

[0065] The detection unit detects the user's psychological state. For example, it uses sensors to detect changes in heart rate and emotion. Specifically, it can measure the user's heart rate in real time using a heart rate sensor. The heart rate sensor detects even minute fluctuations in heart rate with high precision by making direct contact with the user's skin. This allows for real-time understanding of the user's stress level and relaxation state. The detection unit can also detect changes in the user's emotions using facial recognition technology. Facial recognition technology uses a camera to capture the user's face and analyzes facial feature points to identify emotions such as joy, sadness, and anger. Furthermore, the detection unit can also use voice analysis technology to analyze the tone and speed of the user's voice and detect changes in emotion. Voice analysis technology analyzes not only the content of the user's speech but also voice features such as pitch, intensity, and rhythm to detect changes in emotion with high precision. As a result, the detection unit can capture the user's psychological state from multiple angles and provide more accurate data. Furthermore, the detection unit centrally manages this data and transmits it to the analysis unit in real time, thereby improving the overall efficiency of the system.

[0066] The analysis unit analyzes the data detected by the detection unit. For example, the analysis unit uses AI to analyze heart rate and emotional changes to determine the user's psychological state. Specifically, it can use machine learning algorithms to analyze detected data and classify the user's psychological state. Machine learning algorithms learn from past data and predict the user's psychological state with high accuracy from heart rate, facial expressions, and voice characteristics. The analysis unit can also use deep learning technology to analyze complex data patterns and make more accurate determinations of the psychological state. Deep learning technology uses multi-layered neural networks to extract features from large amounts of data and classify psychological states with high accuracy. Furthermore, the analysis unit can use natural language processing technology to analyze the user's voice data and detect emotional changes. Natural language processing technology analyzes the user's speech content and determines emotional changes and stress levels with high accuracy. As a result, the analysis unit can integrate the diverse data provided by the detection unit and comprehensively evaluate the user's psychological state. Furthermore, the analysis unit can process data in real time and respond quickly to changes in the user's psychological state.

[0067] The generation unit generates background music (BGM) based on the analysis results obtained by the analysis unit. For example, the generation unit can use AI to automatically generate BGM tailored to the user's psychological state. Specifically, it can create BGM optimally suited to the user's psychological state using a music generation algorithm. The music generation algorithm adjusts tempo, melody, and harmony based on the user's heart rate and emotional changes to generate music that promotes relaxation and concentration. The generation unit can also select and provide the most suitable song from an existing music library. The music library contains songs of various genres and moods, allowing for quick selection of the most suitable song for the user's psychological state. Furthermore, the generation unit can learn from user feedback and provide a personalized music experience over time. Based on user feedback, it adjusts the music generation algorithm to provide a more individualized music experience. This allows the generation unit to provide BGM optimally suited to the user's psychological state, maximizing relaxation and concentration.

[0068] The provider unit provides background music (BGM) generated by the generator unit. The provider unit delivers BGM to users using, for example, bone conduction earphones or open-type earphones. Specifically, bone conduction earphones transmit sound without blocking the ears, allowing users to enjoy BGM without being affected by external sounds. Open-type earphones fit lightly in the ear, allowing for comfortable BGM enjoyment even during prolonged use. The provider unit can deliver BGM in real time using streaming technology. Streaming technology delivers high-quality music in real time over the internet, enabling users to enjoy BGM without interruption. The provider unit can also download BGM to the user's device for offline playback. This allows users to enjoy BGM even in environments with unstable internet connections. Furthermore, the provider unit can collect user feedback and optimize the BGM delivery method. Based on user feedback, it adjusts volume and playback methods to provide a more comfortable music experience. This allows the provider unit to provide users with optimal BGM, maximizing relaxation and concentration.

[0069] The service provider can learn from user feedback and provide a personalized music experience. For example, the service provider can collect user ratings and usage history and analyze the feedback using AI. The service provider can input user feedback data into a machine learning model to learn user preferences and behavioral patterns. The service provider can also adjust the BGM selection criteria based on user feedback to provide a more personalized music experience. Furthermore, the service provider can reflect user feedback in real time and optimize the way BGM is provided. This allows for a more personalized music experience by learning from user feedback. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user feedback data into a generating AI and have the generating AI perform the provision of a personalized music experience.

[0070] The service provider can have functions to enhance self-esteem. For example, the service provider can provide users with positive messages to enhance their self-esteem. The service provider can use AI to generate positive messages tailored to the user's psychological state. The service provider can also adjust music selection criteria to provide background music that enhances the user's self-esteem. Furthermore, the service provider can learn from user feedback and optimize functions to enhance self-esteem. This allows the service provider to support the user's mental health by enhancing their self-esteem. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user psychological state data into a generating AI and have the generating AI generate messages to enhance self-esteem.

[0071] The service provider can include functions to select modes such as concentration mode, boost mode, relaxation mode, and meditation mode. For example, the service provider can select a mode according to the user's purpose and optimize their psychological state. The service provider can use AI to analyze the user's psychological state data and suggest the optimal mode. The service provider can also learn from user feedback and optimize the settings for each mode. Furthermore, the service provider can adjust the BGM selection criteria based on the user's selected mode to provide an optimal musical experience. This allows the user's psychological state to be optimized by selecting a mode according to their purpose. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's psychological state data into a generating AI and have the generating AI execute the settings for each mode.

[0072] The detection unit can detect changes in heart rate and emotion using sensors. For example, the detection unit measures the user's heart rate in real time using a heart rate sensor. The detection unit collects changes in heart rate as data and can understand the user's psychological state. The detection unit can also detect changes in the user's emotion using facial recognition technology. The detection unit can capture the user's facial expression using a camera and analyze changes in facial expression using AI. Furthermore, the detection unit can analyze the tone and speed of the user's voice using voice analysis technology and detect changes in emotion. The detection unit can collect the user's voice using a microphone and analyze the voice data using AI. As a result, by detecting changes in heart rate and emotion, the psychological state can be understood in real time. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data acquired by the heart rate sensor into a generating AI and analyze changes in heart rate.

[0073] The analysis unit can analyze detected data and determine the user's psychological state. For example, the analysis unit can use AI to analyze heart rate and emotional changes to determine the user's psychological state. The analysis unit can use machine learning algorithms to analyze detected data and classify the user's psychological state. Furthermore, the analysis unit can use deep learning technology to analyze complex data patterns and make more accurate determinations of the psychological state. In addition, the analysis unit can use natural language processing technology to analyze the user's voice data and detect emotional changes. The analysis unit can convert the voice data into text and use AI to analyze emotional changes. This allows for accurate determination of the user's psychological state by analyzing the detected data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input detected data into a generating AI and have the generating AI perform the psychological state determination.

[0074] The detection unit can estimate the user's emotions and adjust the timing of detecting heart rate and emotional changes based on the estimated emotions. For example, if the user is stressed, the detection unit can frequently detect changes in heart rate and track emotional changes in real time. The detection unit can measure the user's heart rate in real time using a heart rate sensor and analyze the stress level. The detection unit can also reduce the frequency of heart rate detection when the user is relaxed and observe long-term emotional changes. Furthermore, if the user is concentrating, the detection unit can moderately detect changes in heart rate to support the maintenance of concentration. By adjusting the detection timing based on the user's emotions, more accurate data 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. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user emotion data into the generating AI and have the generating AI adjust the detection timing.

[0075] The detection unit can select the optimal detection method by referring to the user's past psychological state data when detection occurs. For example, the detection unit can focus detection on time periods when the user has previously experienced stress. The detection unit can retrieve past psychological state data from a database and analyze it using AI. The detection unit can also predict emotional changes in specific events or situations based on the user's past psychological state data and adjust the detection method accordingly. Furthermore, the detection unit can analyze the user's past psychological state data and select the most effective detection method. This allows for the selection of a more effective detection method by referring to past data. Some or all of the above-described processes in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input past psychological state data into a generating AI and have the generating AI select the optimal detection method.

[0076] The detection unit can adjust the sensor sensitivity based on the user's current activity and environment upon detection. For example, if the user is exercising, the detection unit can increase the sensor sensitivity to detect changes in heart rate in detail. The detection unit can measure the amount of exercise with the sensor and analyze it using AI. Furthermore, if the user is in a quiet environment, the detection unit can lower the sensor sensitivity to detect subtle emotional changes. In addition, if the user is in a noisy environment, the detection unit can adjust the sensor sensitivity to eliminate noise and obtain accurate data. In this way, accurate data can be obtained by adjusting the sensor sensitivity according to the activity and environment. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user activity data into a generating AI and have the generating AI perform the sensor sensitivity adjustment.

[0077] The detection unit can estimate the user's emotions and determine the priority of data to detect based on the estimated emotions. For example, if the user is stressed, the detection unit will prioritize detecting heart rate and respiratory rate data. The detection unit can measure the user's biometric data in real time using the heart rate sensor and respiratory sensor and analyze it using AI. The detection unit can also prioritize detecting skin electrical activity and heart rate variability data if the user is relaxed. Furthermore, the detection unit can prioritize detecting electroencephalogram (EEG) and heart rate data if the user is focused. This allows for the priority detection of important data by determining data priority based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input user emotion data into a generative AI and have the generative AI perform data priority determination.

[0078] The detection unit can prioritize the detection of highly relevant data by considering the user's geographical location information when detection occurs. For example, if the user is at home, the detection unit will prioritize the detection of data related to relaxation. The detection unit can identify the user's geographical location using GPS data and analyze it using AI. Furthermore, if the user is at work, the detection unit can prioritize the detection of data related to concentration. In addition, if the user is in a public place, the detection unit can prioritize the detection of data related to stress. In this way, by considering geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's geographical location data into a generating AI and cause the generating AI to perform the priority detection of highly relevant data.

[0079] The detection unit can analyze the user's social media activity and detect related psychological states upon detection. For example, if a user posts on social media indicating they are stressed, the detection unit can detect that psychological state. The detection unit can analyze the content of social media posts using AI and estimate the user's emotions. The detection unit can also detect the psychological state if a user posts on social media indicating they are relaxed. Furthermore, the detection unit can detect the psychological state if a user posts on social media indicating they are focused. This allows for a more accurate detection of the user's psychological state by analyzing social media activity. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's social media data into a generating AI and have the generating AI perform the psychological state detection.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can apply an analysis algorithm specifically designed for stress reduction. The analysis unit can use AI to analyze the user's emotional data and select the optimal algorithm. The analysis unit can also apply an analysis algorithm to maintain a relaxed state if the user is relaxed. Furthermore, if the user is concentrating, the analysis unit can apply an analysis algorithm to enhance concentration. By adjusting the analysis algorithm based on emotions, the accuracy of the analysis can be improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotional data into the generative AI and have the generative AI perform the adjustment of the analysis algorithm.

[0081] The analysis unit can improve the accuracy of its analysis by referring to past analysis data during the analysis process. For example, the analysis unit can refer to the user's past psychological state data and improve accuracy by comparing it with the current analysis results. The analysis unit can retrieve psychological state data from past databases and analyze it using AI. The analysis unit can also extract specific patterns from past analysis data and apply them to the current analysis. Furthermore, the analysis unit can optimize the analysis algorithm based on past analysis data to improve accuracy. In this way, the accuracy of the analysis can be improved by referring to past data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0082] The analysis unit can apply different analysis methods depending on the category of psychological state during analysis. For example, in the case of a stressed state, the analysis unit applies an analysis method specifically designed for stress reduction. The analysis unit can use AI to analyze the user's psychological state data and select the optimal analysis method. Furthermore, in the case of a relaxed state, the analysis unit can apply an analysis method to maintain that relaxed state. In addition, in the case of a focused state, the analysis unit can apply an analysis method to enhance concentration. By applying an analysis method appropriate to the psychological state, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's psychological state data into a generating AI and have the generating AI select the analysis method.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. The analysis unit can use AI to analyze the user's emotion data and select the optimal display method. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is focused, the analysis unit can provide a display method that focuses on the key points. By adjusting the display method based on emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0084] The analysis unit can determine the priority of analysis based on the submission timing of the detected data during analysis. For example, the analysis unit may prioritize the analysis of data detected in real time. The analysis unit can identify the submission timing of data using timestamps and perform analysis using AI. The analysis unit can also refer to past data and prioritize the analysis of data of high importance. Furthermore, the analysis unit can determine the priority of analysis based on the user's schedule. This allows for the priority analysis of important data by determining priorities based on submission timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission timing of the detected data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0085] The analysis unit can improve the accuracy of its analysis by referring to relevant literature data during the analysis process. For example, the analysis unit can refer to the latest research papers and optimize its analysis algorithm. The analysis unit can retrieve academic papers and technical reports from a database and analyze them using AI. The analysis unit can also extract specific patterns from relevant literature data and apply them to the analysis. Furthermore, the analysis unit can verify the analysis results based on the literature data and improve their accuracy. In this way, the accuracy of the analysis can be improved by referring to literature data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0086] The generation unit can estimate the user's emotions and adjust the BGM generation method based on the estimated emotions. For example, if the user is stressed, the generation unit can generate relaxing BGM. The generation unit can use AI to analyze the user's emotional data and generate optimal BGM. The generation unit can also generate BGM to maintain the relaxed state if the user is relaxed. Furthermore, if the user is concentrating, the generation unit can generate BGM to enhance concentration. In this way, by adjusting the BGM generation method based on emotions, more appropriate BGM can be provided. 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. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotional data into the generation AI and have the generation AI adjust the BGM generation method.

[0087] The generation unit can optimize its generation algorithm by referring to past BGM generation data during generation. For example, the generation unit can refer to the user's past BGM generation data and optimize the current generation algorithm. The generation unit can retrieve BGM generation data from a past database and analyze it using AI. The generation unit can also extract specific patterns from past BGM generation data and apply them to the current generation. Furthermore, the generation unit can optimize the generation algorithm and improve accuracy based on past BGM generation data. In this way, the generation algorithm can be optimized by referring to past data. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past BGM generation data into a generation AI and have the generation AI perform the optimization of the generation algorithm.

[0088] The generation unit can apply different BGM generation methods depending on the category of psychological state during generation. For example, if the user is stressed, the generation unit can apply a BGM generation method that has a relaxing effect. The generation unit can use AI to analyze the user's psychological state data and select the optimal BGM generation method. Furthermore, if the user is relaxed, the generation unit can apply a BGM generation method to maintain that relaxed state. In addition, if the user is focused, the generation unit can apply a BGM generation method to enhance concentration. By applying a BGM generation method according to the psychological state, more appropriate BGM can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's psychological state data into a generation AI and have the generation AI select a BGM generation method.

[0089] The generation unit can estimate the user's emotions and adjust the length of the generated background music (BGM) based on the estimated emotions. For example, if the user is stressed, the generation unit can generate longer, relaxing BGM. The generation unit can use AI to analyze the user's emotional data and determine the optimal BGM length. Furthermore, if the user is relaxed, the generation unit can generate BGM of an appropriate length to maintain that relaxed state. Additionally, if the user is concentrating, the generation unit can generate shorter BGM to enhance concentration. This allows for a more appropriate musical experience by adjusting the BGM length based on emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user emotional data into the generation AI and have the generation AI adjust the BGM length.

[0090] The generation unit can determine the generation priority based on the submission timing of the background music (BGM) at the time of generation. For example, the generation unit can prioritize the generation of BGM needed in real time. The generation unit can identify the submission timing of BGM using timestamps and analyze it using AI. The generation unit can also prioritize the generation of necessary BGM based on the user's schedule. Furthermore, the generation unit can refer to past data and prioritize the generation of high-priority BGM. This allows for the provision of appropriate BGM at the required time by determining priorities based on submission timing. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the BGM submission timing into the generation AI and have the generation AI perform the generation priority determination.

[0091] The generation unit can improve the accuracy of generation by referring to relevant music data during the generation process. For example, the generation unit can refer to the latest music data and optimize the generation algorithm. The generation unit can retrieve music libraries and music metadata from a database and analyze them using AI. The generation unit can also extract specific patterns from relevant music data and apply them to generation. Furthermore, the generation unit can verify the generation results based on the music data and improve accuracy. In this way, generation accuracy can be improved by referring to music data. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input relevant music data into a generation AI and have the generation AI perform the generation accuracy improvement.

[0092] The service provider can estimate the user's emotions and adjust the way background music (BGM) is provided based on those emotions. For example, if the user is feeling stressed, the service provider will prioritize providing relaxing BGM. The service provider can use AI to analyze the user's emotional data and select the optimal service provider. Furthermore, if the user is relaxed, the service provider can provide BGM to maintain that relaxed state. In addition, if the user is concentrating, the service provider can provide BGM to enhance their concentration. By adjusting the service provider based on emotions, more appropriate BGM can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotional data into a generative AI and have the generative AI adjust the service provider.

[0093] The service provider can select the optimal service delivery method by referring to the user's past feedback at the time of delivery. For example, the service provider can refer to the user's past feedback and provide the optimal background music. The service provider can retrieve past feedback data from a database and analyze it using AI. The service provider can also extract specific patterns from past feedback and optimize the service delivery method. Furthermore, the service provider can customize the service delivery method based on the user's feedback. This allows the service provider to select the optimal service delivery method by referring to past feedback. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past feedback data into a generating AI and have the generating AI select the service delivery method.

[0094] The service provider can customize the means of providing background music (BGM) based on the user's current activity level at the time of service. For example, if the user is exercising, the service provider can provide BGM through bone conduction earphones. The service provider can acquire user activity data using sensors and analyze it using AI. The service provider can also provide BGM through open earphones if the user is relaxed. Furthermore, if the user is concentrating, the service provider can provide BGM through noise-canceling earphones. This allows for the provision of more appropriate BGM by customizing the means of service according to the user's activity level. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user activity data into a generating AI and have the generating AI perform the customization of the service provider.

[0095] The service provider can estimate the user's emotions and determine the priority of BGM (background music) provision based on the estimated emotions. For example, if the user is feeling stressed, the service provider will prioritize providing relaxing BGM. The service provider can use AI to analyze the user's emotional data and determine the optimal provision priority. Furthermore, if the user is relaxed, the service provider can provide BGM to maintain that relaxed state. In addition, if the user is concentrating, the service provider can provide BGM to enhance their concentration. In this way, more appropriate BGM can be provided by determining the provision priority based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotional data into a generative AI and have the generative AI perform the determination of the provision priority.

[0096] The service provider can provide optimal background music (BGM) by considering the user's geographical location information at the time of delivery. For example, if the user is at home, the service provider can provide relaxing BGM. The service provider can identify the user's geographical location using GPS data and analyze it using AI. Furthermore, if the user is at work, the service provider can provide BGM that enhances concentration. In addition, if the user is in a public place, the service provider can provide BGM that reduces stress. In this way, the service provider can provide optimal BGM by considering geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing optimal BGM.

[0097] The service provider can analyze the user's social media activity and propose a method for providing background music (BGM) at the time of delivery. For example, if the service provider is posting on social media that indicates stress, it can provide BGM that matches that psychological state. The service provider can use AI to analyze the content of social media posts and estimate the user's emotions. Furthermore, if the service provider is posting on social media that indicates relaxation, it can also provide BGM that matches that psychological state. In addition, if the service provider is posting on social media that indicates concentration, it can also provide BGM that matches that psychological state. In this way, by analyzing social media activity, it is possible to provide more appropriate BGM. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI execute a proposal for a method of provision.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The MoodSync system can also include a lighting control unit that adjusts the color and brightness of the lighting based on the user's psychological state. For example, the lighting control unit can provide warm, soft light when the user wants to relax and bright, white light when the user wants to concentrate. The lighting control unit can also learn from user feedback and provide a personalized lighting environment over time. Furthermore, the lighting control unit can automatically adjust the brightness of the lighting according to the user's psychological state to provide the optimal environment. This allows the user to more effectively adjust their psychological state through both music and lighting.

[0100] The MoodSync system can also include a fragrance control unit that adjusts scents based on the user's psychological state. For example, the fragrance control unit can provide lavender when the user wants to relax and peppermint when they want to concentrate. Furthermore, the fragrance control unit can learn from user feedback and provide a personalized fragrance environment over time. It can also automatically adjust the intensity of the scent according to the user's psychological state to provide an optimal environment. This allows users to more effectively adjust their psychological state through both music and scent.

[0101] The MoodSync system can also include a temperature control unit that adjusts the temperature based on the user's psychological state. For example, the temperature control unit can provide a warm temperature when the user wants to relax and a cool temperature when they want to concentrate. Furthermore, the temperature control unit can learn from user feedback and provide a personalized temperature environment over time. In addition, the temperature control unit can automatically adjust the temperature according to the user's psychological state to provide the optimal environment. This allows the user to more effectively regulate their psychological state through both music and temperature.

[0102] The MoodSync system can also include a vibration control unit that adjusts vibrations based on the user's psychological state. For example, the vibration control unit can provide gentle vibrations when the user wants to relax and rhythmic vibrations when they want to concentrate. Furthermore, the vibration control unit can learn from user feedback and provide a personalized vibration environment over time. It can also automatically adjust the intensity of vibrations according to the user's psychological state, providing an optimal environment. This allows the user to more effectively regulate their psychological state through both music and vibration.

[0103] The MoodSync system can also include a seat control unit that adjusts the seat position based on the user's psychological state. For example, the seat control unit can provide a reclining function when the user wants to relax and an upright position when they want to concentrate. Furthermore, the seat control unit can learn from user feedback and provide a personalized seat position over time. In addition, the seat control unit can automatically adjust the seat angle and height according to the user's psychological state to provide an optimal environment. This allows the user to more effectively regulate their psychological state through both music and seating.

[0104] The MoodSync system can also include an exercise suggestion unit that provides exercise recommendations based on the user's psychological state. For example, the exercise suggestion unit can suggest yoga or stretching when the user wants to relax, and light exercise when the user wants to concentrate. Furthermore, the exercise suggestion unit can learn from user feedback and provide personalized exercise recommendations over time. It can also automatically adjust the type and intensity of exercise according to the user's psychological state to suggest the optimal workout. This allows users to more effectively regulate their psychological state through both music and exercise.

[0105] The MoodSync system can also include a meal suggestion unit that provides meal recommendations based on the user's psychological state. For example, the meal suggestion unit can suggest a light snack when the user wants to relax and a meal to replenish energy when the user wants to concentrate. Furthermore, the meal suggestion unit can learn from user feedback and provide personalized meal recommendations over time. It can also automatically adjust the type and quantity of food according to the user's psychological state to suggest the optimal meal. This allows users to more effectively manage their psychological state through both music and food.

[0106] The MoodSync system can also include a break suggestion unit that proposes break timings based on the user's psychological state. For example, the break suggestion unit can suggest short breaks when the user is feeling stressed and longer breaks when their concentration is low. Furthermore, the break suggestion unit can learn from user feedback and provide personalized break suggestions over time. It can also automatically adjust the frequency and length of breaks according to the user's psychological state, suggesting the optimal break. This allows users to more effectively manage their psychological state through both music and breaks.

[0107] The MoodSync system can also include a learning suggestion unit that provides learning suggestions based on the user's psychological state. For example, the learning suggestion unit can suggest learning content with a relaxing effect when the user wants to relax, and learning content that enhances concentration when the user wants to concentrate. The learning suggestion unit can also learn from user feedback and provide personalized learning suggestions over time. Furthermore, the learning suggestion unit can automatically adjust the type and difficulty level of learning according to the user's psychological state and suggest the optimal learning. This allows users to more effectively adjust their psychological state through both music and learning.

[0108] The MoodSync system can also include a communication suggestion unit that proposes communication based on the user's psychological state. For example, the communication suggestion unit can suggest relaxing conversations when the user wants to relax, and conversations that enhance concentration when the user wants to concentrate. The communication suggestion unit can also learn from user feedback and provide personalized communication suggestions over time. Furthermore, the communication suggestion unit can automatically adjust the content and frequency of communication according to the user's psychological state and propose the optimal communication. This allows users to more effectively adjust their psychological state through both music and communication.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The detection unit detects the psychological state. The detection unit uses sensors to detect changes in heart rate and emotion. For example, it can measure the user's heart rate in real time using a heart rate sensor and detect changes in the user's emotion using facial recognition technology. It can also analyze the tone and speed of the user's voice using voice analysis technology to detect changes in emotion. Step 2: The analysis unit analyzes the data detected by the detection unit. The analysis unit uses AI to analyze heart rate and emotional changes to determine the user's psychological state. Machine learning algorithms and deep learning technologies are used to analyze the detected data and classify the user's psychological state. It can also use natural language processing technology to analyze the user's voice data and detect changes in emotion. Step 3: The generation unit generates background music (BGM) based on the analysis results obtained by the analysis unit. The generation unit uses AI to automatically generate BGM that matches the user's psychological state. It can create the optimal BGM for the user's psychological state using a music generation algorithm, or it can select the best song from an existing music library. Furthermore, it can learn from user feedback and provide a personalized music experience over time. Step 4: The provider unit provides the background music (BGM) generated by the generator unit. The provider unit delivers the BGM to the user using bone conduction earphones or open-type earphones. It is also possible to deliver the BGM in real time using streaming technology, and to allow the BGM to be downloaded to the user's device for offline playback. Furthermore, user feedback can be collected to optimize the BGM delivery method.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the detection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the detection unit uses the sensors of the smart device 14 to detect changes in heart rate and emotion. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the psychological state using AI. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates optimal background music based on the analysis results. The provision unit provides the background music to the user using the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the detection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the detection unit uses the sensors of the smart glasses 214 to detect changes in heart rate and emotion. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the psychological state using AI. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates optimal background music based on the analysis results. The provision unit provides the background music to the user using the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the detection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the detection unit uses the sensors of the headset terminal 314 to detect changes in heart rate and emotion. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the psychological state using AI. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates optimal background music based on the analysis results. The provision unit provides the background music to the user using the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the detection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the detection unit uses the robot 414's sensors to detect changes in heart rate and emotion. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the psychological state using AI. 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 analysis results. The provision unit provides the background music to the user using the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A detection unit that detects psychological state, An analysis unit analyzes the data detected by the aforementioned detection unit, A generation unit that generates background music based on the analysis results obtained by the analysis unit, The system includes a providing unit that provides background music generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, Learn from user feedback and provide a personalized music experience. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Features that enhance self-esteem The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, It features the ability to select modes such as focus mode, boost mode, relaxation mode, and meditation mode. The system described in Appendix 1, characterized by the features described herein. (Note 5) The detection unit is Sensors detect changes in heart rate and emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, The detected data is analyzed to determine the psychological state. The system described in Appendix 1, characterized by the features described herein. (Note 7) The detection unit is It estimates the user's emotions and adjusts the timing of detecting heart rate and emotional changes based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The detection unit is During detection, the system selects the optimal detection method by referring to the user's past psychological state data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The detection unit is When detection occurs, the sensor sensitivity is adjusted based on the user's current activity status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The detection unit is It estimates the user's emotions and determines the priority of data to detect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The detection unit is During detection, the system prioritizes detecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The detection unit is Upon detection, the system analyzes the user's social media activity and detects related psychological states. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, past analysis data is referenced to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analytical methods are applied depending on the category of psychological state. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the detection data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant literature data to improve the accuracy of the analysis. 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 During generation, the generation algorithm is optimized by referring to past BGM generation data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, different background music generation methods are applied depending on the category of psychological state. 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 adjusts the length of the background music generated based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the generation priority is determined based on the submission timing of the background music. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the system references related music data to improve generation accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the way background music is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, we will refer to past user feedback to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the method of providing background music will be customized based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the user's emotions and determines the priority of background music (BGM) provision based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the system will offer the most suitable background music considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and propose a method for providing background music. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 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. A detection unit that detects psychological state, An analysis unit analyzes the data detected by the aforementioned detection unit, A generation unit that generates background music based on the analysis results obtained by the analysis unit, The system includes a providing unit that provides background music generated by the generation unit. A system characterized by the following features.

2. The aforementioned supply unit is, Learn from user feedback and provide a personalized music experience. The system according to feature 1.

3. The aforementioned supply unit is, Features that enhance self-esteem The system according to feature 1.

4. The aforementioned supply unit is, It features the ability to select modes such as focus mode, boost mode, relaxation mode, and meditation mode. The system according to feature 1.

5. The detection unit is Sensors detect changes in heart rate and emotions. The system according to feature 1.

6. The aforementioned analysis unit, The detected data is analyzed to determine the psychological state. The system according to feature 1.

7. The detection unit is It estimates the user's emotions and adjusts the timing of detecting heart rate and emotional changes based on the estimated emotions. The system according to feature 1.

8. The detection unit is During detection, the system selects the optimal detection method by referring to the user's past psychological state data. The system according to feature 1.

9. The detection unit is When detection occurs, the sensor sensitivity is adjusted based on the user's current activity status and environment. The system according to feature 1.

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