system

Abstract

The system according to this embodiment aims to analyze emotional data and optimize the sound environment. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a customization unit. The collection unit collects emotional data. The analysis unit analyzes the emotional data collected by the collection unit. The generation unit generates a sound environment based on the results analyzed by the analysis unit. The customization unit customizes the sound environment 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, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance 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 conventional technology, stress management is difficult and productivity may decrease.

[0005] The system according to the embodiment aims to analyze emotion data and optimize the sound environment.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a customization unit. The collection unit collects emotion data. The analysis unit analyzes the emotion data collected by the collection unit. The generation unit generates a sound environment based on the result analyzed by the analysis unit. The customization unit customizes the sound environment generated by the generation unit.

Effects of the Invention

[0007] The system according to this embodiment can analyze emotional data and optimize the sound environment. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that analyzes emotional data to optimize the environment and enhance concentration. This system uses AI to analyze the user's emotions and generates an optimal sound environment in real time. First, it collects information such as heart rate, facial expression, and voice, and the AI ​​analyzes this data. Next, based on the analysis results, it generates a sound environment tailored to the user's desired state. The user can customize the sound environment using a voice-controlled interface. This system reduces stress and improves concentration. For example, it collects information such as the user's heart rate, facial expression, and voice. For example, if the user wants to relax, it is expected that their heart rate will decrease and their facial expression will become calmer. Next, the AI ​​analyzes the collected data and determines the user's emotional state. For example, if the heart rate is high and the facial expression is tense, it is determined that the user is feeling stressed. Based on the analysis results, the AI ​​generates an optimal sound environment. For example, if the user wants to relax, it generates calm music or nature sounds. Conversely, if the user wants to enhance concentration, it generates fast-paced music or ambient sounds. The user can customize the sound environment using a voice-controlled interface. For example, by giving a voice command such as "I want to relax," the AI ​​generates a sound environment suitable for relaxation. This system allows users to easily set a sound environment that matches their emotional state. This reduces stress and improves concentration. For example, if you feel stressed at work, playing relaxing music can reduce stress and help you regain concentration. Also, if you lose concentration while studying, playing fast-paced music can help improve your focus. This system supports stress reduction and improved concentration by providing music and ambient sounds based on an individual's emotions and desired state. For example, if a user wants to relax, playing calming music can help them relax. Also, if a user wants to concentrate, playing fast-paced music can improve their concentration. This system analyzes emotional data to optimize the environment and enhance concentration, and its key feature is that it uses AI to generate the optimal sound environment in real time.This allows users to easily set up a sound environment that matches their emotional state, thereby reducing stress and improving productivity. The system can then provide an optimal sound environment tailored to the user's emotional state.

[0029] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a customization unit. The collection unit collects emotional data. Emotional data includes, but is not limited to, heart rate, facial expressions, and voice. The collection unit can, for example, measure heart rate using a heart rate sensor. The collection unit can also analyze facial expressions using a camera. Furthermore, the collection unit can collect voice using a microphone. For example, the collection unit attaches a heart rate sensor to the user's wrist and measures heart rate in real time. The camera photographs the user's face and analyzes changes in facial expressions. The microphone collects the user's voice and stores it as voice data. The analysis unit analyzes the emotional data collected by the collection unit. The analysis unit can, for example, use AI to analyze the emotional data and determine the user's emotional state. The analysis unit can, for example, analyze fluctuations in heart rate to determine whether the user is relaxed or stressed. The analysis unit can also analyze changes in facial expressions to determine the user's emotional state. Furthermore, the analysis unit can analyze audio data and determine the user's emotional state. For example, the analysis unit can analyze heart rate fluctuation patterns to determine whether the user is relaxed or stressed. Changes in facial expressions are determined by analyzing features such as smiles and frown lines. Audio data is analyzed by analyzing voice tone and speed to determine the user's emotional state. The generation unit generates a sound environment based on the results analyzed by the analysis unit. The generation unit can generate a sound environment tailored to the user's desired state, for example, using AI. For example, if the user wants to relax, the generation unit can generate calm music or nature sounds. The generation unit can also generate fast-paced music or ambient sounds if the user wants to improve their concentration. Furthermore, the generation unit can customize the sound environment to match the user's desired state. For example, if the user wants to relax, the generation unit can generate calm music or nature sounds. If the user wants to improve their concentration, it can generate fast-paced music or ambient sounds. The customization unit customizes the sound environment generated by the generation unit. The customization unit allows the user to customize the sound environment, for example, using a voice-controlled interface.The customization unit can generate a sound environment suitable for relaxation, for example, when the user gives a voice command such as "I want to relax." The customization unit can also generate a sound environment that enhances concentration when the user gives a voice command such as "I want to concentrate." Thus, the system according to this embodiment can provide an optimal sound environment tailored to the user's emotional state.

[0030] The data collection unit collects emotional data. This emotional data includes, but is not limited to, heart rate, facial expressions, and voice. The data collection unit can, for example, measure heart rate using a heart rate sensor. It can also analyze facial expressions using a camera. Furthermore, it can collect voice using a microphone. Specifically, the data collection unit attaches a heart rate sensor to the user's wrist and measures heart rate in real time. The heart rate sensor uses photoplethysmography (PPG) to detect blood flow and measure heart rate. The camera photographs the user's face and analyzes changes in facial expressions. The camera identifies the user's face using facial recognition technology and tracks changes in facial expressions in real time. For example, it identifies expressions such as smiles, anger, and sadness and estimates the emotional state. The microphone collects the user's voice and stores it as voice data. The voice data is analyzed using speech recognition technology to extract features such as voice tone, speed, and volume. This allows the data collection unit to gain a multifaceted understanding of the user's emotional state. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and generation units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the emotional data collected by the data collection unit. For example, the analysis unit uses AI to analyze the emotional data and determine the user's emotional state. Specifically, it analyzes heart rate variability to determine whether the user is relaxed or stressed. Heart rate variability is evaluated using, for example, heart rate variability (HRV) analysis. HRV is an index that shows the variation in heart rate intervals; HRV tends to be high in a relaxed state and low in a stressed state. The analysis unit analyzes the HRV pattern to evaluate the user's stress level. It can also analyze changes in facial expressions to determine the user's emotional state. Facial expression analysis uses a combination of facial recognition technology and machine learning algorithms. For example, it extracts facial feature points to identify expressions such as smiles, anger, and sadness. Furthermore, it can also analyze voice data to determine the user's emotional state. Voice analysis uses speech recognition technology and natural language processing (NLP) technology. It analyzes features such as tone, speed, and volume of the voice to estimate the user's emotional state. For example, if the voice tone is high and the speed is fast, the user may be excited. This allows the analysis unit to quickly and accurately analyze the collected data and understand the user's emotional state in real time. Furthermore, the analysis unit can utilize historical data and statistical information to evaluate long-term fluctuations in emotional state. This enables the analysis unit to comprehensively evaluate the user's emotional state and improve the reliability and accuracy of the entire system.

[0032] The generation unit generates a sound environment based on the results analyzed by the analysis unit. For example, the generation unit uses AI to create a sound environment tailored to the user's desired state. Specifically, if the user wants to relax, it generates calming music or nature sounds. The generation unit uses a music generation algorithm to generate music appropriate to the user's emotional state in real time. For example, if the user wants to relax, it generates music combining calm melodies and nature sounds (such as waves or birdsong). It can also generate fast-paced music or ambient sounds if the user wants to improve concentration. The generation unit adjusts the tempo, volume, and timbre of the music according to the user's emotional state and preferences. Furthermore, the generation unit can collect user feedback and continuously improve the quality of the generated sound environment. For example, it collects user impressions and evaluations after listening to music and adjusts the parameters of the generation algorithm. This allows the generation unit to provide an optimal sound environment for the user's emotional state and improve user satisfaction. Additionally, the generation unit can combine multiple sound sources to create a richer sound environment. For example, it can combine nature sounds and music to enhance the relaxation effect. Furthermore, the generation unit can select music from specific genres or artists according to the user's preferences. This allows the generation unit to provide a diverse sound environment tailored to the user's emotional state, thereby enhancing the user experience.

[0033] The customization unit customizes the sound environment generated by the generation unit. For example, the customization unit allows users to customize the sound environment using a voice control interface. Specifically, by voice-instructing the user to "relax," the unit generates a sound environment suitable for relaxation. The voice control interface analyzes the user's instructions using voice recognition technology and generates an appropriate sound environment. For example, if the user instructs to "concentrate," it generates music or ambient sounds that enhance concentration. The customization unit can also adjust the music genre and volume according to the user's preferences. For example, if the user instructs to "listen to classical music," it plays classical music. Furthermore, the customization unit can collect user feedback and continuously improve the accuracy and effectiveness of the customization function. For example, it collects user feedback and evaluations after listening to music and adjusts the parameters of the customization algorithm. This allows the customization unit to provide the optimal sound environment for the user's emotional state and preferences, thereby improving user satisfaction. Additionally, the customization unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the customization unit to quickly and reliably provide users with a suitable sound environment, thereby improving the user experience.

[0034] The data collection unit can collect information such as heart rate, facial expressions, and voice. The data collection unit can, for example, measure heart rate using a heart rate sensor. The data collection unit can, for example, attach a heart rate sensor to the user's wrist and measure heart rate in real time. The data collection unit can also analyze facial expressions using a camera. The data collection unit can, for example, photograph the user's face using a camera and analyze changes in facial expressions. The data collection unit can also collect voice using a microphone. The data collection unit can, for example, collect the user's voice using a microphone and save it as voice data. In this way, the data collection unit can accurately grasp the user's emotional state by collecting information such as heart rate, facial expressions, and voice. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired by the heart rate sensor into a generating AI, and the generating AI can analyze the heart rate data to determine the emotional state.

[0035] The analysis unit can analyze the collected data and determine the user's emotional state. The analysis unit can, for example, use AI to analyze emotional data and determine the user's emotional state. The analysis unit can, for example, analyze heart rate fluctuations to determine whether the user is relaxed or stressed. The analysis unit can, for example, analyze heart rate fluctuation patterns to determine whether the user is relaxed or stressed. The analysis unit can also, for example, analyze changes in facial expressions to determine the user's emotional state. The analysis unit can, for example, analyze changes in facial expressions to determine the user's emotional state. The analysis unit can also, for example, analyze audio data to determine the user's emotional state. The analysis unit can, for example, analyze audio data and determine the user's emotional state. As a result, the analysis unit can generate an appropriate sound environment by analyzing the collected data and determining the user's emotional state. 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 collected data into a generating AI, which can analyze the data and determine the emotional state.

[0036] The generation unit can generate a sound environment tailored to the user's desired state based on the analysis results. The generation unit can, for example, use AI to generate a sound environment tailored to the user's desired state. For example, if the user wants to relax, the generation unit can generate calm music or nature sounds. For example, if the user wants to concentrate, the generation unit can also generate fast-paced music or ambient sounds. In this way, the generation unit can provide a sound environment tailored to the user's desired state by generating a sound environment based on the analysis results. 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 analysis results into a generation AI, and the generation AI can generate the sound environment.

[0037] The customization unit can customize the sound environment using a voice-controlled interface. For example, the customization unit allows the user to customize the sound environment using a voice-controlled interface. For example, the customization unit can generate a sound environment suitable for relaxation when the user gives a voice command such as "I want to relax." The customization unit can also generate a sound environment that enhances concentration when the user gives a voice command such as "I want to concentrate." This allows the user to easily customize the sound environment using a voice-controlled interface. Some or all of the above-described processes in the customization unit may be performed using AI, or not. For example, the customization unit can input a voice-controlled interface into a generating AI, which can then customize the sound environment.

[0038] The generation unit can generate calming music or nature sounds when the user wants to relax, and fast-paced music or ambient sounds when the user wants to concentrate. For example, the generation unit can generate calming music or nature sounds when the user wants to relax. For example, the generation unit can also generate fast-paced music or ambient sounds when the user wants to concentrate. For example, the generation unit can generate fast-paced music or ambient sounds when the user wants to concentrate. In this way, the generation unit can provide an optimal environment according to the user's emotional state by generating a sound environment suitable for relaxation or improved concentration. 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 analysis results for when the user wants to relax into a generation AI, and the generation AI can generate calming music or nature sounds.

[0039] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0040] The data collection unit can analyze the user's activity history and determine the optimal timing for data collection. For example, if a user has a habit of jogging every morning, heart rate data can be collected primarily during that time. Similarly, if a user has time to relax in the evening, facial expression data can be collected during that time. Furthermore, if a user wants to improve their concentration while working, voice data can be collected immediately after starting work. In this way, the data collection unit can optimize the timing of data collection based on the user's activity history, enabling the collection of more accurate emotional data.

[0041] The data collection unit can adjust the frequency of data collection considering the battery level of the user's device. For example, if the battery level is low, the data collection frequency can be reduced. Conversely, if the battery level is sufficient, the data collection frequency can be increased. Furthermore, if the battery level is moderate, the data collection frequency can be appropriately adjusted. In this way, the data collection unit can achieve efficient data collection by adjusting the data collection frequency based on the device's battery level.

[0042] The data collection unit can adjust the timing of data collection based on the user's device location. For example, if the user is at home, it can collect data related to relaxation. If the user is at the office, it can collect data related to concentration. Furthermore, if the user is out, it can collect data related to stress. In this way, the data collection unit can achieve efficient data collection by adjusting the timing of data collection based on the device's location.

[0043] The following briefly describes the processing flow for example form 1.

[0044] Step 1: The data collection unit collects emotional data. This emotional data includes heart rate, facial expressions, and voice. The data collection unit measures heart rate using a heart rate sensor, analyzes facial expressions using a camera, and collects voice using a microphone. For example, a heart rate sensor is attached to the user's wrist to measure heart rate in real time. The camera captures the user's face and analyzes changes in facial expressions. The microphone collects the user's voice and stores it as voice data. Step 2: The analysis unit analyzes the emotional data collected by the collection unit. The analysis unit uses AI to analyze the emotional data and determine the user's emotional state. For example, it analyzes heart rate fluctuations to determine whether the user is relaxed or stressed. It analyzes changes in facial expressions to determine the user's emotional state. It analyzes voice data, analyzing the tone and speed of the voice to determine the user's emotional state. Step 3: The generation unit generates a sound environment based on the results analyzed by the analysis unit. The generation unit uses AI to generate a sound environment tailored to the user's desired state. For example, if the user wants to relax, it generates calm music or nature sounds. If the user wants to improve their concentration, it generates fast-paced music or ambient sounds. Step 4: The customization unit customizes the sound environment generated by the generation unit. The customization unit allows the user to customize the sound environment using a voice control interface. For example, the user can voice-instruct "I want to relax" to generate a sound environment suitable for relaxation. The user can also voice-instruct "I want to concentrate" to generate a sound environment that enhances concentration.

[0045] (Example of form 2) The system according to an embodiment of the present invention is a system that analyzes emotional data to optimize the environment and enhance concentration. This system uses AI to analyze the user's emotions and generates an optimal sound environment in real time. First, it collects information such as heart rate, facial expression, and voice, and the AI ​​analyzes this data. Next, based on the analysis results, it generates a sound environment tailored to the user's desired state. The user can customize the sound environment using a voice-controlled interface. This system reduces stress and improves concentration. For example, it collects information such as the user's heart rate, facial expression, and voice. For example, if the user wants to relax, it is expected that their heart rate will decrease and their facial expression will become calmer. Next, the AI ​​analyzes the collected data and determines the user's emotional state. For example, if the heart rate is high and the facial expression is tense, it is determined that the user is feeling stressed. Based on the analysis results, the AI ​​generates an optimal sound environment. For example, if the user wants to relax, it generates calm music or nature sounds. Conversely, if the user wants to enhance concentration, it generates fast-paced music or ambient sounds. The user can customize the sound environment using a voice-controlled interface. For example, by giving a voice command such as "I want to relax," the AI ​​generates a sound environment suitable for relaxation. This system allows users to easily set a sound environment that matches their emotional state. This reduces stress and improves concentration. For example, if you feel stressed at work, playing relaxing music can reduce stress and help you regain concentration. Also, if you lose concentration while studying, playing fast-paced music can help improve your focus. This system supports stress reduction and improved concentration by providing music and ambient sounds based on an individual's emotions and desired state. For example, if a user wants to relax, playing calming music can help them relax. Also, if a user wants to concentrate, playing fast-paced music can improve their concentration. This system analyzes emotional data to optimize the environment and enhance concentration, and its key feature is that it uses AI to generate the optimal sound environment in real time.This allows users to easily set up a sound environment that matches their emotional state, thereby reducing stress and improving productivity. The system can then provide an optimal sound environment tailored to the user's emotional state.

[0046] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a customization unit. The collection unit collects emotional data. Emotional data includes, but is not limited to, heart rate, facial expressions, and voice. The collection unit can, for example, measure heart rate using a heart rate sensor. The collection unit can also analyze facial expressions using a camera. Furthermore, the collection unit can collect voice using a microphone. For example, the collection unit attaches a heart rate sensor to the user's wrist and measures heart rate in real time. The camera photographs the user's face and analyzes changes in facial expressions. The microphone collects the user's voice and stores it as voice data. The analysis unit analyzes the emotional data collected by the collection unit. The analysis unit can, for example, use AI to analyze the emotional data and determine the user's emotional state. The analysis unit can, for example, analyze fluctuations in heart rate to determine whether the user is relaxed or stressed. The analysis unit can also analyze changes in facial expressions to determine the user's emotional state. Furthermore, the analysis unit can analyze audio data and determine the user's emotional state. For example, the analysis unit can analyze heart rate fluctuation patterns to determine whether the user is relaxed or stressed. Changes in facial expressions are determined by analyzing features such as smiles and frown lines. Audio data is analyzed by analyzing voice tone and speed to determine the user's emotional state. The generation unit generates a sound environment based on the results analyzed by the analysis unit. The generation unit can generate a sound environment tailored to the user's desired state, for example, using AI. For example, if the user wants to relax, the generation unit can generate calm music or nature sounds. The generation unit can also generate fast-paced music or ambient sounds if the user wants to improve their concentration. Furthermore, the generation unit can customize the sound environment to match the user's desired state. For example, if the user wants to relax, the generation unit can generate calm music or nature sounds. If the user wants to improve their concentration, it can generate fast-paced music or ambient sounds. The customization unit customizes the sound environment generated by the generation unit. The customization unit allows the user to customize the sound environment, for example, using a voice-controlled interface.The customization unit can generate a sound environment suitable for relaxation, for example, when the user gives a voice command such as "I want to relax." The customization unit can also generate a sound environment that enhances concentration when the user gives a voice command such as "I want to concentrate." Thus, the system according to this embodiment can provide an optimal sound environment tailored to the user's emotional state.

[0047] The data collection unit collects emotional data. This emotional data includes, but is not limited to, heart rate, facial expressions, and voice. The data collection unit can, for example, measure heart rate using a heart rate sensor. It can also analyze facial expressions using a camera. Furthermore, it can collect voice using a microphone. Specifically, the data collection unit attaches a heart rate sensor to the user's wrist and measures heart rate in real time. The heart rate sensor uses photoplethysmography (PPG) to detect blood flow and measure heart rate. The camera photographs the user's face and analyzes changes in facial expressions. The camera identifies the user's face using facial recognition technology and tracks changes in facial expressions in real time. For example, it identifies expressions such as smiles, anger, and sadness and estimates the emotional state. The microphone collects the user's voice and stores it as voice data. The voice data is analyzed using speech recognition technology to extract features such as voice tone, speed, and volume. This allows the data collection unit to gain a multifaceted understanding of the user's emotional state. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and generation units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0048] The analysis unit analyzes the emotional data collected by the data collection unit. For example, the analysis unit uses AI to analyze the emotional data and determine the user's emotional state. Specifically, it analyzes heart rate variability to determine whether the user is relaxed or stressed. Heart rate variability is evaluated using, for example, heart rate variability (HRV) analysis. HRV is an index that shows the variation in heart rate intervals; HRV tends to be high in a relaxed state and low in a stressed state. The analysis unit analyzes the HRV pattern to evaluate the user's stress level. It can also analyze changes in facial expressions to determine the user's emotional state. Facial expression analysis uses a combination of facial recognition technology and machine learning algorithms. For example, it extracts facial feature points to identify expressions such as smiles, anger, and sadness. Furthermore, it can also analyze voice data to determine the user's emotional state. Voice analysis uses speech recognition technology and natural language processing (NLP) technology. It analyzes features such as tone, speed, and volume of the voice to estimate the user's emotional state. For example, if the voice tone is high and the speed is fast, the user may be excited. This allows the analysis unit to quickly and accurately analyze the collected data and understand the user's emotional state in real time. Furthermore, the analysis unit can utilize historical data and statistical information to evaluate long-term fluctuations in emotional state. This enables the analysis unit to comprehensively evaluate the user's emotional state and improve the reliability and accuracy of the entire system.

[0049] The generation unit generates a sound environment based on the results analyzed by the analysis unit. For example, the generation unit uses AI to create a sound environment tailored to the user's desired state. Specifically, if the user wants to relax, it generates calming music or nature sounds. The generation unit uses a music generation algorithm to generate music appropriate to the user's emotional state in real time. For example, if the user wants to relax, it generates music combining calm melodies and nature sounds (such as waves or birdsong). It can also generate fast-paced music or ambient sounds if the user wants to improve concentration. The generation unit adjusts the tempo, volume, and timbre of the music according to the user's emotional state and preferences. Furthermore, the generation unit can collect user feedback and continuously improve the quality of the generated sound environment. For example, it collects user impressions and evaluations after listening to music and adjusts the parameters of the generation algorithm. This allows the generation unit to provide an optimal sound environment for the user's emotional state and improve user satisfaction. Additionally, the generation unit can combine multiple sound sources to create a richer sound environment. For example, it can combine nature sounds and music to enhance the relaxation effect. Furthermore, the generation unit can select music from specific genres or artists according to the user's preferences. This allows the generation unit to provide a diverse sound environment tailored to the user's emotional state, thereby enhancing the user experience.

[0050] The customization unit customizes the sound environment generated by the generation unit. For example, the customization unit allows users to customize the sound environment using a voice control interface. Specifically, by voice-instructing the user to "relax," the unit generates a sound environment suitable for relaxation. The voice control interface analyzes the user's instructions using voice recognition technology and generates an appropriate sound environment. For example, if the user instructs to "concentrate," it generates music or ambient sounds that enhance concentration. The customization unit can also adjust the music genre and volume according to the user's preferences. For example, if the user instructs to "listen to classical music," it plays classical music. Furthermore, the customization unit can collect user feedback and continuously improve the accuracy and effectiveness of the customization function. For example, it collects user feedback and evaluations after listening to music and adjusts the parameters of the customization algorithm. This allows the customization unit to provide the optimal sound environment for the user's emotional state and preferences, thereby improving user satisfaction. Additionally, the customization unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the customization unit to quickly and reliably provide users with a suitable sound environment, thereby improving the user experience.

[0051] The data collection unit can collect information such as heart rate, facial expressions, and voice. The data collection unit can, for example, measure heart rate using a heart rate sensor. The data collection unit can, for example, attach a heart rate sensor to the user's wrist and measure heart rate in real time. The data collection unit can also analyze facial expressions using a camera. The data collection unit can, for example, photograph the user's face using a camera and analyze changes in facial expressions. The data collection unit can also collect voice using a microphone. The data collection unit can, for example, collect the user's voice using a microphone and save it as voice data. In this way, the data collection unit can accurately grasp the user's emotional state by collecting information such as heart rate, facial expressions, and voice. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired by the heart rate sensor into a generating AI, and the generating AI can analyze the heart rate data to determine the emotional state.

[0052] The analysis unit can analyze the collected data and determine the user's emotional state. The analysis unit can, for example, use AI to analyze emotional data and determine the user's emotional state. The analysis unit can, for example, analyze heart rate fluctuations to determine whether the user is relaxed or stressed. The analysis unit can, for example, analyze heart rate fluctuation patterns to determine whether the user is relaxed or stressed. The analysis unit can also, for example, analyze changes in facial expressions to determine the user's emotional state. The analysis unit can, for example, analyze changes in facial expressions to determine the user's emotional state. The analysis unit can also, for example, analyze audio data to determine the user's emotional state. The analysis unit can, for example, analyze audio data and determine the user's emotional state. As a result, the analysis unit can generate an appropriate sound environment by analyzing the collected data and determining the user's emotional state. 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 collected data into a generating AI, which can analyze the data and determine the emotional state.

[0053] The generation unit can generate a sound environment tailored to the user's desired state based on the analysis results. The generation unit can, for example, use AI to generate a sound environment tailored to the user's desired state. For example, if the user wants to relax, the generation unit can generate calm music or nature sounds. For example, if the user wants to concentrate, the generation unit can also generate fast-paced music or ambient sounds. In this way, the generation unit can provide a sound environment tailored to the user's desired state by generating a sound environment based on the analysis results. 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 analysis results into a generation AI, and the generation AI can generate the sound environment.

[0054] The customization unit can customize the sound environment using a voice-controlled interface. For example, the customization unit allows the user to customize the sound environment using a voice-controlled interface. For example, the customization unit can generate a sound environment suitable for relaxation when the user gives a voice command such as "I want to relax." The customization unit can also generate a sound environment that enhances concentration when the user gives a voice command such as "I want to concentrate." This allows the user to easily customize the sound environment using a voice-controlled interface. Some or all of the above-described processes in the customization unit may be performed using AI, or not. For example, the customization unit can input a voice-controlled interface into a generating AI, which can then customize the sound environment.

[0055] The generation unit can generate calming music or nature sounds when the user wants to relax, and fast-paced music or ambient sounds when the user wants to concentrate. For example, the generation unit can generate calming music or nature sounds when the user wants to relax. For example, the generation unit can also generate fast-paced music or ambient sounds when the user wants to concentrate. For example, the generation unit can generate fast-paced music or ambient sounds when the user wants to concentrate. In this way, the generation unit can provide an optimal environment according to the user's emotional state by generating a sound environment suitable for relaxation or improved concentration. 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 analysis results for when the user wants to relax into a generation AI, and the generation AI can generate calming music or nature sounds.

[0056] The data collection unit can estimate the user's emotions and adjust the timing of collecting information such as heart rate, facial expressions, and voice based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect heart rate more frequently because the fluctuations in heart rate will be greater. For example, if the user is relaxed, the data collection unit will reduce the frequency of collecting facial expression data because the changes in facial expressions will be less frequent. For example, if the user is focused, the data collection unit will prioritize collecting voice data and adjust the frequency of collecting other data. In this way, the data collection unit can collect more accurate emotional data by adjusting the timing of information collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the collection unit may be performed using AI, or not using AI. For example, the collection unit can input data for estimating the user's emotions into the generating AI, the generating AI can estimate the emotions, and the timing of information collection can be adjusted based on the result.

[0057] The data collection unit can analyze the user's past emotional data and select the optimal data collection method. For example, the data collection unit can analyze the time periods in which the user has felt stressed in the past and focus on collecting data during those times. For example, the data collection unit can analyze the environments in which the user has felt relaxed in the past and select a data collection method suited to those environments. For example, the data collection unit can select the types of data to collect during specific activities based on the user's past emotional data. By doing so, the data collection unit can select the optimal data collection method by analyzing past emotional data and efficiently collect data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past emotional data into a generating AI, which can analyze the data and select the optimal data collection method.

[0058] The data collection unit can filter emotional data based on the user's current activity and environment. For example, if the user is exercising, the data collection unit prioritizes collecting heart rate data and filters out other data. For example, if the user is in a quiet environment, the data collection unit refrains from collecting voice data and prioritizes facial expression data. For example, if the user is in a meeting, the data collection unit filters out voice data and collects heart rate and facial expression data. This allows the data collection unit to efficiently collect necessary data by filtering data based on the user's activity and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit inputs data about the user's current activities and environment into the generating AI, which can then filter the data.

[0059] The data collection unit can estimate the user's emotions and determine the priority of emotional data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting heart rate data. For example, if the user is relaxed, the data collection unit will prioritize collecting facial expression data. For example, if the user is focused, the data collection unit will prioritize collecting voice data. In this way, the data collection unit can prioritize collecting important data by determining the priority of data based on the user's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data for estimating the user's emotions into a generating AI, the generating AI will estimate the emotions, and the data priority will be determined based on the result.

[0060] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting emotional data. For example, if the user is at home, the data collection unit will prioritize the collection of data related to relaxation. For example, if the user is at the office, the data collection unit will prioritize the collection of data related to concentration. For example, if the user is out, the data collection unit will prioritize the collection of data related to stress. This allows the data collection unit to efficiently collect highly relevant data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0061] The data collection unit can analyze the user's social media activity and collect relevant data when collecting emotional data. For example, if the user posts on social media indicating they are feeling stressed, the data collection unit will prioritize collecting heart rate data. For example, if the user posts on social media indicating they are relaxed, the data collection unit will prioritize collecting facial expression data. For example, if the user posts on social media indicating they are focused, the data collection unit will prioritize collecting audio data. This allows the data collection unit to efficiently collect data related to the user's emotions by analyzing their social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI, which can then collect relevant data.

[0062] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can apply an algorithm specifically designed to reduce stress. For example, if the user is relaxed, the analysis unit can apply an algorithm to maintain that relaxed state. For example, if the user is focused, the analysis unit can apply an algorithm to enhance their concentration. By adjusting the algorithm based on the user's emotions, the analysis unit can perform a more accurate analysis. 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 data for estimating the user's emotions into a generating AI, the generating AI can estimate the emotions, and the algorithm can be adjusted based on the result.

[0063] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data during the analysis. For example, if heart rate data is important, the analysis unit will perform a detailed analysis. For example, if facial expression data is important, the analysis unit will perform a detailed analysis. For example, if voice data is important, the analysis unit will perform a detailed analysis. This allows the analysis unit to perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 importance of the collected data into a generating AI, which can then adjust the level of detail of the analysis.

[0064] The analysis unit can apply different analysis methods depending on the data category during analysis. For example, the analysis unit can apply time variability analysis to heart rate data. For example, the analysis unit can apply image analysis to facial expression data. For example, the analysis unit can apply voice analysis to voice data. By applying analysis methods according to the data category, the analysis unit can perform more accurate analysis. 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 data category into a generating AI, which can then apply an appropriate analysis method.

[0065] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit will prioritize the analysis of stress-related data. For example, if the user is relaxed, the analysis unit will prioritize the analysis of relaxation-related data. For example, if the user is focused, the analysis unit will prioritize the analysis of concentration-related data. In this way, the analysis unit can prioritize the analysis of important data by determining the priority of analysis based on the user's emotions. 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 data for estimating the user's emotions into a generating AI, the generating AI will estimate the emotions, and the analysis priority will be determined based on the results.

[0066] The analysis unit can adjust the order of analysis based on the data collection timing during analysis. For example, the analysis unit prioritizes analyzing the most recent data. For example, the analysis unit analyzes while referring to past data. For example, the analysis unit prioritizes analyzing data from a specific time period. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the data collection timing. 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 data collection timing into a generating AI, which can then adjust the order of analysis.

[0067] The analysis unit can adjust its analysis method based on the relevance of the data during analysis. For example, the analysis unit prioritizes analyzing highly relevant data. The analysis unit filters out less relevant data. The analysis unit selects an analysis method according to the relevance of the data. This allows the analysis unit to perform more accurate analysis by adjusting the analysis method based on the relevance of the 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 the relevance of the data into a generating AI, which can then apply an appropriate analysis method.

[0068] The generation unit can estimate the user's emotions and determine the type of sound environment to generate based on the estimated emotions. For example, if the user wants to relax, the generation unit generates calm music. For example, if the user wants to concentrate, the generation unit generates fast-paced music. For example, if the user wants to concentrate, the generation unit generates natural sounds. For example, if the user is stressed, the generation unit generates natural sounds. In this way, the generation unit can provide the optimal sound environment by determining the type of sound environment based on the user's emotions. 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 data for estimating the user's emotions into a generation AI, the generation AI can estimate the emotions, and the type of sound environment can be determined based on the result.

[0069] The generation unit can adjust the level of detail of the sound environment it generates based on the importance of the analysis results during generation. For example, the generation unit generates a detailed sound environment based on important analysis results. For example, the generation unit generates a simplified sound environment based on less important analysis results. For example, the generation unit generates a simplified sound environment based on less important analysis results. For example, the generation unit adjusts the level of detail of the sound environment according to the importance of the analysis results. This enables the generation unit to efficiently generate sound environments by adjusting the level of detail of the sound environment based on the importance of the analysis results. 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 importance of the analysis results to the generation AI, which can then adjust the level of detail of the sound environment.

[0070] The generation unit can apply different sound environment generation algorithms depending on the user's desired state during generation. For example, if the user wants to relax, the generation unit applies an algorithm specifically designed for relaxation. For example, if the user wants to concentrate, the generation unit applies an algorithm specifically designed for concentration. For example, if the user wants to concentrate, the generation unit applies an algorithm specifically designed for concentration. For example, if the user wants to reduce stress, the generation unit applies an algorithm specifically designed for stress reduction. In this way, the generation unit can provide the optimal sound environment by applying a sound environment generation algorithm that matches the user's desired state. 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 desired state into the generation AI, which can then apply an appropriate sound environment generation algorithm.

[0071] The generation unit can estimate the user's emotions and determine the priority of the sound environment to generate based on the estimated user emotions. For example, if the user wants to relax, the generation unit will prioritize generating a sound environment suitable for relaxation. For example, if the user wants to concentrate, the generation unit will prioritize generating a sound environment suitable for concentration. For example, if the user wants to concentrate, the generation unit will prioritize generating a sound environment suitable for concentration. For example, if the user wants to reduce stress, the generation unit will prioritize generating a sound environment suitable for stress reduction. In this way, the generation unit can provide the optimal sound environment by determining the priority of the sound environment based on the user's emotions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input data to estimate the user's emotions into the generation AI, which then estimates the emotions and determines the priority of the sound environment based on the results.

[0072] The generation unit can adjust the type of sound environment it generates based on the user's activity status during generation. For example, if the user is working, the generation unit generates a sound environment that enhances concentration. For example, if the user is relaxing, the generation unit generates a sound environment suitable for relaxation. For example, if the user is exercising, the generation unit generates fast-paced music. In this way, the generation unit can provide an optimal sound environment by adjusting the type of sound environment based on the user's activity status. 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 activity status into a generation AI, and the generation AI can generate an appropriate sound environment.

[0073] The generation unit can generate an optimal sound environment by referring to the user's past sound environment usage history during generation. For example, the generation unit can generate a similar sound environment based on a sound environment in which the user has previously relaxed. For example, the generation unit can generate a similar sound environment based on a sound environment in which the user has previously relaxed. For example, the generation unit can generate a similar sound environment based on a sound environment in which the user has previously enhanced their concentration. For example, the generation unit can generate an optimal sound environment by analyzing the user's past sound environment usage history. In this way, the generation unit can provide an optimal sound environment by referring to the user's past sound environment usage history. 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 past sound environment usage history into a generation AI, and the generation AI can generate an optimal sound environment.

[0074] The customization unit can estimate the user's emotions and adjust the customization interface based on the estimated emotions. For example, if the user is stressed, the customization unit can provide a simple interface. For example, if the user is relaxed, the customization unit can provide detailed customization options. For example, if the user is focused, the customization unit can provide an intuitive interface. This allows the customization unit to provide an optimal interface by adjusting the customization interface based on the user's emotions. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input data for estimating the user's emotions into a generating AI, the generating AI can estimate the emotions, and the interface can be adjusted based on the result.

[0075] The customization unit can select the optimal customization method by referring to the user's past customization history during customization. For example, the customization unit may prioritize suggesting customization methods that the user has used in the past. The customization unit may, for example, suggest the optimal customization option from the user's past customization history. The customization unit may, for example, suggest the optimal customization option from the user's past customization history. The customization unit may, for example, analyze the user's past customization history and select the optimal customization method. The customization unit may, for example, analyze the user's past customization history and select the optimal customization method. In this way, the customization unit can provide the optimal customization method by referring to the user's past customization history. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's past customization history into a generating AI, and the generating AI can select the optimal customization method.

[0076] The customization unit can estimate the user's emotions and determine the priority of customizations based on the estimated emotions. For example, if the user is feeling stressed, the customization unit will prioritize customizations specifically for stress reduction. For example, if the user is relaxed, the customization unit will prioritize customizations specifically for relaxation. For example, if the user is focused, the customization unit will prioritize customizations that enhance concentration. In this way, the customization unit can provide the optimal customization by determining the priority of customizations based on the user's emotions. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input data for estimating the user's emotions into a generating AI, the generating AI will estimate the emotions, and the customization priority will be determined based on the result.

[0077] The customization unit can select the optimal customization method by considering the user's device information during customization. For example, if the user is using a smartphone, the customization unit will propose a customization method optimized for smartphones. For example, if the user is using a tablet, the customization unit will propose a customization method optimized for tablets. For example, if the user is using a tablet, the customization unit will propose a customization method optimized for tablets. For example, if the user is using a desktop, the customization unit will propose a customization method optimized for desktops. In this way, the customization unit can provide the optimal customization method by considering the user's device information. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's device information into a generating AI, which can then select the optimal customization method.

[0078] The customization unit can select the optimal customization method by considering the user's current activity status during customization. For example, if the user is working, the customization unit can suggest a customization method that enhances concentration. For example, if the user is relaxing, the customization unit can suggest a customization method specifically for relaxation. For example, if the user is exercising, the customization unit can suggest a customization method suitable for exercise. In this way, the customization unit can provide the optimal customization method by considering the user's current activity status. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's current activity status into a generating AI, which can then select the optimal customization method.

[0079] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0080] The analysis unit can estimate the user's emotions and, based on those emotions, suggest music genres that match the user's preferences. For example, if the user wants to relax, it can suggest classical music or jazz. If the user wants to concentrate, it can suggest electronica or rock. Furthermore, if the user is feeling stressed, it can suggest nature sounds or ambient music. In this way, the analysis unit can provide a sound environment that suits the user's preferences by suggesting music genres based on the user's emotions.

[0081] The data collection unit can analyze the user's activity history and determine the optimal timing for data collection. For example, if a user has a habit of jogging every morning, heart rate data can be collected primarily during that time. Similarly, if a user has time to relax in the evening, facial expression data can be collected during that time. Furthermore, if a user wants to improve their concentration while working, voice data can be collected immediately after starting work. In this way, the data collection unit can optimize the timing of data collection based on the user's activity history, enabling the collection of more accurate emotional data.

[0082] The analysis unit can estimate the user's emotions and visualize the user's stress level based on those estimated emotions. For example, if the user is feeling stressed, the stress level can be displayed in a graph or chart. If the user is relaxed, the relaxation level can be displayed. Furthermore, if the user is focused, the concentration level can be displayed. In this way, the analysis unit makes it easier for users to understand their own emotional state by visualizing their stress level based on their emotions.

[0083] The sound generation unit can estimate the user's emotions and adjust the volume of the sound environment based on those emotions. For example, if the user wants to relax, the volume can be set lower. If the user wants to concentrate, the volume can be set to a moderate level. Furthermore, if the user is stressed, the volume can be gently adjusted. In this way, the sound generation unit can provide an optimal sound environment by adjusting the volume based on the user's emotions.

[0084] The customization section can estimate the user's emotions and adjust the display order of customization options based on those emotions. For example, if the user is stressed, options related to stress reduction can be displayed first. If the user is relaxed, options related to relaxation can be displayed first. Furthermore, if the user is focused, options related to improving focus can be displayed first. In this way, the customization section can adjust the display order of customization options based on the user's emotions, allowing the user to quickly access the options they need.

[0085] The data collection unit can adjust the frequency of data collection considering the battery level of the user's device. For example, if the battery level is low, the data collection frequency can be reduced. Conversely, if the battery level is sufficient, the data collection frequency can be increased. Furthermore, if the battery level is moderate, the data collection frequency can be appropriately adjusted. In this way, the data collection unit can achieve efficient data collection by adjusting the data collection frequency based on the device's battery level.

[0086] The analysis unit can estimate the user's emotions and, based on those emotions, assess the user's health status. For example, if the user is stressed, it can assess the stress level and warn of health risks. If the user is relaxed, it can assess the relaxation level and indicate that their health status is good. Furthermore, if the user is focused, it can assess the concentration level and indicate that their health status is stable. In this way, the analysis unit makes it easier for users to understand their own health status by assessing their health status based on their emotions.

[0087] The sound generation unit can estimate the user's emotions and adjust the playback speed of the sound environment based on those emotions. For example, if the user wants to relax, the playback speed can be slowed down. If the user wants to concentrate, the playback speed can be increased. Furthermore, if the user is stressed, the playback speed can be gently adjusted. In this way, the sound generation unit can provide an optimal sound environment by adjusting the playback speed based on the user's emotions.

[0088] The customization section can estimate the user's emotions and adjust the color scheme of the customizable interface based on those emotions. For example, if the user is stressed, calm colors can be used. If the user is relaxed, bright colors can be used. Furthermore, if the user is focused, simple colors can be used. In this way, the customization section can provide a comfortable environment for the user by adjusting the interface color scheme based on the user's emotions.

[0089] The data collection unit can adjust the timing of data collection based on the user's device location. For example, if the user is at home, it can collect data related to relaxation. If the user is at the office, it can collect data related to concentration. Furthermore, if the user is out, it can collect data related to stress. In this way, the data collection unit can achieve efficient data collection by adjusting the timing of data collection based on the device's location.

[0090] The following briefly describes the processing flow for example form 2.

[0091] Step 1: The data collection unit collects emotional data. This emotional data includes heart rate, facial expressions, and voice. The data collection unit measures heart rate using a heart rate sensor, analyzes facial expressions using a camera, and collects voice using a microphone. For example, a heart rate sensor is attached to the user's wrist to measure heart rate in real time. The camera captures the user's face and analyzes changes in facial expressions. The microphone collects the user's voice and stores it as voice data. Step 2: The analysis unit analyzes the emotional data collected by the collection unit. The analysis unit uses AI to analyze the emotional data and determine the user's emotional state. For example, it analyzes heart rate fluctuations to determine whether the user is relaxed or stressed. It analyzes changes in facial expressions to determine the user's emotional state. It analyzes voice data, analyzing the tone and speed of the voice to determine the user's emotional state. Step 3: The generation unit generates a sound environment based on the results analyzed by the analysis unit. The generation unit uses AI to generate a sound environment tailored to the user's desired state. For example, if the user wants to relax, it generates calm music or nature sounds. If the user wants to improve their concentration, it generates fast-paced music or ambient sounds. Step 4: The customization unit customizes the sound environment generated by the generation unit. The customization unit allows the user to customize the sound environment using a voice control interface. For example, the user can voice-instruct "I want to relax" to generate a sound environment suitable for relaxation. The user can also voice-instruct "I want to concentrate" to generate a sound environment that enhances concentration.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, and customization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects heart rate, facial expressions, and voice using the heart rate sensor, camera, and microphone of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to determine the user's emotional state. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates an optimal sound environment based on the analysis results. The customization unit is implemented in the control unit 46A of the smart device 14, for example, and allows the user to customize the sound environment using a voice control interface. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0096] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, and customization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects heart rate, facial expressions, and voice using the heart rate sensor, camera, and microphone of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to determine the user's emotional state. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates an optimal sound environment based on the analysis results. The customization unit is implemented in the control unit 46A of the smart glasses 214, for example, and allows the user to customize the sound environment using a voice control interface. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0112] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and customization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects heart rate, facial expressions, and voice using the heart rate sensor, camera, and microphone of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to determine the user's emotional state. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates an optimal sound environment based on the analysis results. The customization unit is implemented in the control unit 46A of the headset terminal 314, for example, and can customize the sound environment using an interface controlled by the user's voice. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0128] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, and customization unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects heart rate, facial expressions, and voice using the heart rate sensor, camera, and microphone of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to determine the user's emotional state. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates an optimal sound environment based on the analysis results. The customization unit is implemented in the control unit 46A of the robot 414, for example, and can customize the sound environment using a voice control interface. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] (Note 1) A collection unit that collects emotional data, An analysis unit analyzes the emotional data collected by the aforementioned collection unit, A generation unit that generates a sound environment based on the results of the analysis performed by the aforementioned analysis unit, The system includes a customization unit for customizing the sound environment generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects information such as heart rate, facial expressions, and voice. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to determine the user's emotional state. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Based on the analysis results, a sound environment tailored to the user's desired state is generated. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned customization unit is Customize the sound environment using a voice-controlled interface. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is If you want to relax, generate calming music or nature sounds; if you want to improve your concentration, generate fast-paced music or ambient sounds. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting information such as heart rate, facial expressions, and voice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze users' past emotional data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting sentiment data, filtering is performed based on the user's current activities and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of emotional data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting sentiment data, the system prioritizes collecting 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 aforementioned collection unit is When collecting sentiment data, analyze users' social media activity and collect relevant data. 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, adjust the level of detail based on the importance of the collected data. 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 data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis 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 order of analysis is adjusted based on the data collection period. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the analysis method is adjusted based on the relationships between the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and determines the type of sound environment to generate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the level of detail in the generated sound environment is adjusted based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, different sound environment generation algorithms are applied depending on the user's desired 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 determines the priority of the sound environment to be generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, adjust the type of sound environment generated based on the user's activity. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the system references the user's past sound environment usage history to generate the optimal sound environment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned customization unit is It estimates the user's emotions and adjusts the customized interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned customization unit is During customization, the system selects the optimal customization method by referring to the user's past customization history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned customization unit is It estimates the user's emotions and determines the priority of customization based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned customization unit is During customization, the optimal customization method is selected by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned customization unit is During customization, the optimal customization method is selected by considering the user's current activity status. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0164] 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 collection unit that collects emotional data, An analysis unit analyzes the emotional data collected by the aforementioned collection unit, A generation unit that generates a sound environment based on the results of the analysis performed by the aforementioned analysis unit, The system includes a customization unit for customizing the sound environment generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is It collects information such as heart rate, facial expressions, and voice. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to determine the user's emotional state. The system according to feature 1.

4. The generating unit is Based on the analysis results, a sound environment tailored to the user's desired state is generated. The system according to feature 1.

5. The aforementioned customization unit is Customize the sound environment using a voice-controlled interface. The system according to feature 1.

6. The generating unit is If you want to relax, generate calming music or nature sounds; if you want to improve your concentration, generate fast-paced music or ambient sounds. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting information such as heart rate, facial expressions, and voice based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze users' past emotional data and select the optimal data collection method. The system according to feature 1.

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