system

Abstract

Provide a system. [Solution] a means for collecting biometric information of a user; means for assessing a user's current state based on the collected biometric information; means for generating optimal music according to the user's evaluated state; means for playing the generated music; a means for monitoring user reactions during playback; means for optimizing a music generation algorithm based on monitored user responses; A system including:

Description

[Technical Field]

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

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

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

[0004] In modern society, where stress and fatigue accumulate, there is a need for systems that can grasp users' moods and physical conditions in real time and provide optimal music that responds to them. However, conventional music playback systems have difficulty providing improvised music that matches the user's condition. Furthermore, there is no established method yet for providing music at appropriate times based on the user's biometric information or schedule information to improve the user's quality of life. To solve these issues, a new system is needed that can accurately assess the user's condition and generate and provide improvised music based on that information. [Means for solving the problem]

[0005] The present invention provides a system that collects a user's biometric information, evaluates the user's current state based on that information, and generates optimal music according to that state. Specifically, the system includes a means for collecting the user's biometric information, a means for evaluating the user's current state based on the collected biometric information, a means for generating optimal music according to the user's evaluated state, a means for playing the generated music, a means for monitoring the user's reaction during playback, and a means for optimizing a music generation algorithm based on the monitored user's reaction. Furthermore, the system can improve the user's quality of life by adding a means for acquiring the user's schedule information and providing optimal music for a specific situation, and a means for analyzing facial expression data to evaluate the user's mood and emotions.

[0006] "Biometric information" refers to physiological data such as the user's body temperature, pulse rate, and facial expression.

[0007] "User profile" refers to information that integrates data about a user's biometric information, schedule information, and music preferences.

[0008] "Status assessment" refers to the process of identifying and analyzing a user's current physical condition and mood based on collected biometric information.

[0009] "Music generation algorithm" refers to a computational method or model for generating optimal improvised music based on an assessment of the user's state.

[0010] "Monitoring" refers to the process of observing and recording the user's biometric information while music is being played and monitoring their reactions.

[0011] "Optimization" refers to the process of adjusting and improving the music generation algorithm based on monitored user responses.

[0012] "Schedule information" refers to time-related information such as a user's schedule or events.

[0013] "Situation" refers to a specific situation or scene in which the user finds themselves.

[0014] "Facial expression recognition technology" refers to technology that uses cameras or other devices to identify and analyze a user's facial expressions.

[0015] "Music playback" refers to the process of playing the generated music to a user through a device.

[0016] These definitions will provide a better understanding of each element of the present invention and ensure proper patent scope. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11]FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] This invention is a system that collects a user's biometric information in real time, evaluates the user's condition based on that information, and then improvises and provides optimal music. Furthermore, it monitors the user's reactions while the music is playing, and optimizes the music generation algorithm based on that data.

[0039] The device (smartwatch or smartphone) collects biometric information such as the user's body temperature, pulse rate, and facial expression. For example, a smartwatch uses a temperature sensor and a pulse sensor to measure body temperature and pulse rate. A smartphone uses a camera to capture the user's facial expression data. This data is sent to a server in real time.

[0040] The server integrates the received biometric information and uses an AI model to assess the user's current state. For example, if the pulse rate is higher than normal, the AI ​​will assess that the user is feeling stressed. Based on the assessment results, the server uses an AI composition model to generate music that is optimal for the user's state. For example, it will create relaxing music for a user who is feeling stressed.

[0041] The generated music is sent from the server to the device, which then plays it. The playback volume is also optimized according to the user's condition. While the music is playing, the device again monitors the user's biometric information. By comparing the data before and after playback, the server evaluates the user's reaction and provides feedback to the AI ​​model. This information is used to optimize the music generation algorithm.

[0042] In addition, the system collects information about the user's schedule and provides music that is optimal for specific situations. For example, calming music can be provided when the user wants to relax. Using facial expression recognition technology, the camera can analyze the user's facial expressions to more accurately assess the user's mood and emotions.

[0043] As a concrete example of this system, suppose a user has an important meeting in the morning and their smartwatch detects a high pulse rate. The server receives this data and determines that the user is feeling stressed. The AI ​​composition model generates relaxing music and plays it on the device. If the user's body temperature and pulse rate return to normal during playback, the system determines that the music was appropriate. This feedback is reflected in future music generation, providing a more personalized music experience.

[0044] This allows users to always receive the most appropriate music for their situation, improving their quality of life. This system is highly effective for daily stress management, relaxation, and performance improvement.

[0045] The processing flow will be explained below.

[0046] Step 1:

[0047] Devices use sensors to collect biometric information about users: for example, a smartwatch measures a user's temperature and pulse, while a smartphone uses a camera to capture facial expression data.

[0048] Step 2:

[0049] The device transmits the collected biometric information to a server in real time, where body temperature, pulse rate, and facial expression data are integrated and uploaded to the server as a user profile.

[0050] Step 3:

[0051] The server uses an AI model to evaluate the user's current state based on the received biometric information. For example, if the heart rate is higher than normal, the AI ​​will determine that the user is feeling stressed.

[0052] Step 4:

[0053] The server uses an AI composition model to generate optimal music based on the user's assessed state: if the user is feeling stressed, music with a relaxing effect will be generated.

[0054] Step 5:

[0055] The server sends the generated music data to the terminal, which then imports the received music data and prepares for playback.

[0056] Step 6:

[0057] The device will play music at an optimal volume, and the volume settings will also be adjusted based on the user's state.

[0058] Step 7:

[0059] The device continues to monitor the user's biometric information while music is playing, and transmits the playback data to the server in real time.

[0060] Step 8:

[0061] The server compares the biometric data before and during playback to evaluate the user's reaction, for example, checking whether the pulse rate stabilizes after music playback.

[0062] Step 9:

[0063] The server will then provide feedback on the evaluation results and update the AI ​​model, which will optimize future music generation.

[0064] Step 10:

[0065] The device collects the user's schedule information and provides music tailored to specific situations, such as calming music during times when relaxation is needed.

[0066] Step 11:

[0067] The device uses the camera to analyze the user's facial expression data to assess their mood and emotions, which can then provide a more personalized music experience.

[0068] Through this series of steps, the system can provide the user with music that best suits their condition, improving their quality of life.

[0069] Example 1

[0070] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0071] Conventional music generation systems were unable to properly reflect the user's biological state or mood, making it difficult to provide optimal music for each individual user. Furthermore, they lacked the ability to monitor the user's reactions in real time during playback and adjust the system accordingly, making it difficult to maximize the effect of the music. Another issue was the inability to provide music based on the user's schedule information or specific situations.

[0072] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0073] In this invention, the server includes a means for transmitting a user's biometric information to the server in real time, a means for integrating the transmitted biometric information to evaluate the user's current state, and a means for generating optimal music based on the evaluation results. This makes it possible to accurately evaluate the user's state and provide optimal music based on that evaluation. Furthermore, by optimizing the music generation algorithm based on the monitored user's reactions, the effectiveness of the music can be maximized. Furthermore, music can be provided based on the user's schedule information or specific situations, allowing for a personalized music experience for each user.

[0074] "User's biometric information" is data that indicates the user's physical condition, such as body temperature, pulse rate, and facial expression.

[0075] "Means of collection" refers to devices or technologies such as sensors or cameras used to acquire biometric information.

[0076] "Transmitting means" refers to the communication technology or device used to transmit the collected data to the server.

[0077] "Means of integration" refers to techniques or methods for processing multiple pieces of data into one.

[0078] The "assessment means" refers to algorithms or models that analyze the integrated data to determine the user's condition.

[0079] The "generative means" refers to an AI composition model or software that creates music based on the evaluation results.

[0080] "Means for transmitting and playing" refers to the technology and devices that send the generated music data to a terminal and play it back as sound.

[0081] "Monitoring means" refers to technology or devices that monitor and record the user's biometric information again while music is being played.

[0082] "Optimizing means" refers to methods for improving the music generation algorithm based on the monitored data to provide better results.

[0083] "Schedule information" is data related to the user's plans and schedules.

[0084] A "situation" is a specific situation or environment, or a specific time or event that a user faces.

[0085] "Mood and emotions" refers to information that indicates the user's psychological state and feelings.

[0086] "Means of analysis" refers to the technology and algorithms used to analyze facial expression data and determine the user's mood and emotions.

[0087] This invention is a system that collects a user's biometric information in real time, evaluates the user's condition based on that information, and then improvises and provides optimal music. Furthermore, it monitors the user's reactions while the music is playing, and optimizes the music generation algorithm based on that data.

[0088] Collection and transmission methods

[0089] Devices (smartwatches and smartphones)

[0090] The device collects biometric information such as the user's body temperature, pulse rate, and facial expression. Specific hardware used includes a temperature sensor and pulse rate sensor in the smartwatch and a smartphone camera. This data is sent to a server in real time. Bluetooth and Wi-Fi are used as communication methods.

[0091] Examples:

[0092] The smartwatch measures body temperature with a temperature sensor and heart rate with a pulse sensor. This data is sent to a smartphone via Bluetooth, and then to a server via Wi-Fi. At the same time, the smartphone's camera captures facial expression data using biometric authentication technology, which is also sent to the server.

[0093] Evaluation methods

[0094] server

[0095] The server integrates the received biometric information and uses an AI model to assess the user's current state. Specifically, it analyzes the integrated data and assesses that the user is feeling stressed based on data such as "body temperature: 37.2°C, pulse rate: 110 BPM, facial expression: stressed facial expression."

[0096] Examples:

[0097] The server receives the data "Temperature: 37.2°C, Pulse: 110 BPM, Facial Expression: Stressed Facial Expression" and uses the AI ​​model to evaluate that "the user is feeling stressed." Based on this evaluation, the prompt sentence for the music to be generated is determined as follows: "User's biological information: Pulse 110 BPM, Body temperature 37.2°C, Facial expression: Stressed. Purpose of the music to be generated: Relaxation."

[0098] A means of generating and playing music

[0099] server

[0100] Based on the evaluation results, an AI composition model is used to generate music that is optimal for the user's state, such as music with a relaxing effect aimed at reducing stress.

[0101] Terminal

[0102] The generated music is sent from the server to the device, where it is played back, with the volume and tone optimized according to the user's state.

[0103] Examples:

[0104] The server generates slow-tempo music as "stress-reducing music" and sends it to the smartphone, where it plays the music and automatically adjusts the volume.

[0105] Monitoring and optimization measures

[0106] Terminal

[0107] While the music is playing, the device again monitors the user's body temperature, pulse rate, and facial expressions, and by comparing the data before and after playback, the effect of the music can be evaluated.

[0108] server

[0109] The server compares the data before and after playback and optimizes the music generation algorithm based on the monitored user reactions. This feedback updates the AI ​​model and is reflected in the next music generation.

[0110] Examples:

[0111] If the data before playback was "body temperature: 37.2°C, pulse rate: 110 BPM, facial expression: stressed expression," and the data after playback changed to "body temperature: 36.8°C, pulse rate: 75 BPM, facial expression: relaxed," the server would evaluate the music as effective and update the AI ​​model.

[0112] Collection and utilization of schedule information

[0113] Terminal

[0114] The system also collects information about the user's schedule and provides music that is optimal for a specific situation. For example, if the user has an important meeting in the morning, the system will provide relaxing music based on that information.

[0115] Examples:

[0116] If a user has entered in their schedule that they have an important meeting coming up in the morning, the server will receive that information and send relaxing music to the device to play to help ease tension.

[0117] This step-by-step approach allows us to provide a music experience that is optimized for each user's condition, improving their quality of life.

[0118] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0119] Step 1:

[0120] Biometric data collection

[0121] The device (smartwatch or smartphone) uses sensors and cameras to collect biometric information such as the user's body temperature, pulse rate, and facial expression. Specifically, the smartwatch measures body temperature with a temperature sensor and heart rate with a pulse sensor. The smartphone camera also takes a picture of the user's face and captures facial expression data.

[0122] Input: User's body temperature, pulse rate, facial expression

[0123] Output: Collected biometric information (body temperature, pulse, facial expression)

[0124] Step 2:

[0125] Sending data to the server

[0126] The device transmits the collected biometric information to a server in real time using Bluetooth or Wi-Fi. For example, data from a smartwatch is transmitted to a smartphone via Bluetooth, and then to the server via Wi-Fi.

[0127] Input: Collected biometric information (body temperature, pulse, facial expression)

[0128] Output: Biometric information sent to the server

[0129] Step 3:

[0130] Data integration and condition assessment

[0131] The server integrates the received biometric information and uses an AI model to evaluate the user's current condition. For example, if data such as "Temperature: 37.2°C, Pulse: 110 BPM, Facial Expression: Stressed Facial Expression" is received, the AI ​​model will analyze this data and conclude that "the user is feeling stressed."

[0132] Input: Biometric information sent to the server

[0133] Output: User's current status evaluation result

[0134] Step 4:

[0135] Music Generation

[0136] Based on the evaluation results, the server uses an AI composition model to generate music that best suits the user's state of mind. For example, it generates music with a relaxing effect for a user who is feeling stressed.

[0137] Input: User's current status evaluation result

[0138] Output: Generated music files

[0139] Step 5:

[0140] Sending and playing music

[0141] The server sends the generated music to the device, which then plays it. The volume and tone of the music are automatically adjusted according to the user's state. Specifically, the server sends music data to the smartphone, which then plays it through its speaker.

[0142] Input: Generated music files

[0143] Output: Music being played

[0144] Step 6:

[0145] Monitoring while music is playing

[0146] While music is playing, the device again monitors the user's body temperature, pulse rate, and facial expressions. This allows data to be collected before and after playback and records any changes. For example, while music is playing, the smartwatch again measures the user's body temperature and pulse rate, and the smartphone captures facial expressions.

[0147] Input: Monitoring results of biological information while music is playing

[0148] Output: Monitored vital signs

[0149] Step 7:

[0150] Reaction evaluation and algorithm optimization

[0151] The server compares the data before and after playback and evaluates the user's reaction. Based on the evaluation results, it optimizes the music generation algorithm and updates the AI ​​model. This feedback is used for the next music generation.

[0152] Input: Monitored biometric data (before and after playback)

[0153] Output: Optimized music generation algorithm

[0154] (Application example 1)

[0155] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0156] Currently, stress management and relaxation while in a vehicle primarily depend on manually selected music and entertainment information. However, manual selection makes it difficult to achieve an appropriate relaxation effect that matches the user's real-time state. Furthermore, conventional music playback systems do not optimize the system to reflect the user's real-time biometric information, resulting in a lack of immediate effects on stress reduction and mood improvement. The present invention aims to solve these problems and provide a system that highly automates relaxation and stress management in a vehicle.

[0157] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0158] In this invention, the server includes means for collecting biometric information of a user, means for evaluating the user's current state based on the collected biometric information, means for generating optimal music according to the evaluated state of the user, means for playing the generated music, means for monitoring the user's reaction during playback, means for optimizing a music generation algorithm based on the monitored user's reaction, and means for linking with the on-board system of the autonomous vehicle, thereby enabling the provision of optimal relaxation music in real time and stress management for the user.

[0159] "User's biometric information" refers to data related to the user's health and emotional state that can be obtained in real time, such as body temperature, pulse rate, and facial expression.

[0160] "User's current state" refers to the user's mental or physical state, such as stress level or degree of relaxation, assessed based on collected biometric information.

[0161] "Optimal music" is music created based on the user's biometric information and current condition to maximize the desired effect, such as relaxation or improved concentration.

[0162] "Generated music" is music that is improvised using artificial intelligence models or the like, depending on the user's current assessed state.

[0163] "Means for playing" refers to a device or system that outputs the generated music so that the user can listen to it.

[0164] "Monitoring means" refers to devices or software that recollect biometric information such as the user's body temperature, pulse rate, and facial expressions while music is being played, and track changes.

[0165] A "music generation algorithm" is a computer program or procedure for generating optimal music based on a user's biometric information and condition.

[0166] An "autonomous vehicle" is a vehicle that operates automatically and can travel safely without user intervention.

[0167] "In-vehicle systems" is a general term for information processing devices, communication devices, entertainment systems, etc. installed in autonomous vehicles.

[0168] This invention is a system that collects a user's biometric information in real time and generates and provides music based on that information. Its purpose is to automate relaxation and stress management for users in self-driving vehicles. Specific embodiments of the invention are described below.

[0169] composition

[0170] The system mainly consists of the following components:

[0171] 1. Biometric information collection device: Collects biometric information such as the user's body temperature, pulse rate, and facial expression. Specifically, sensors installed in smartwatches and smartphones are used.

[0172] 2. Biometric information processing server (hereinafter referred to as the server): Integrates collected biometric information and evaluates the user's current condition. An AI model is used for the evaluation.

[0173] 3. Music Generation Server: Generates optimal music based on the user's state evaluation results. AI composition model is used.

[0174] 4. In-vehicle system for autonomous vehicles: Plays generated music and continuously monitors the user's biometric information.

[0175] Processing flow

[0176] First, a smartwatch or smartphone collects the user's biometric information. The collected data is sent to a server in real time. The server then integrates this data and uses an AI model to evaluate the user's current state. For example, if the user's pulse rate is high, it is determined that the user is feeling stressed.

[0177] Based on the evaluation results, the music generation server generates appropriate music—for example, classical or ambient music for a user who is feeling stressed—and the generated music track is sent to the autonomous vehicle's in-vehicle system for playback inside the vehicle.

[0178] While music is playing, the smartwatch or smartphone continues to collect biometric information and transmits it to the server. By comparing the biometric information before and after playback, the server evaluates the user's reaction and uses this information as feedback to optimize the music generation algorithm.

[0179] Explanation and examples

[0180] As a concrete example, consider a situation where a user experiences a high pulse rate during a long drive. The server evaluates this as a stressful state and generates a relaxing classical music track. This track is then played on the in-car system. If the user's pulse rate returns to normal after playback, the system considers the music track appropriate and reflects this in future music generation.

[0181] Example prompt for a generative AI model:

[0182] "Generate optimal music based on the user's current biometric information. Evaluate their condition based on the information below and select a music track of an appropriate genre.

[0183] Heart rate: 110, temperature: 37.5, facial expression: neutral."

[0184] This makes it possible to provide optimal relaxation music in real time and manage the user's stress, improving the user's comfort in autonomous vehicles and providing a stress-free travel experience.

[0185] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0186] Step 1:

[0187] Smartwatches and smartphones collect users' biometric information.

[0188] Specifically, the smartwatch's pulse and temperature sensors and the smartphone's camera are used to measure the user's body temperature, pulse, and facial expression.

[0189] Input: Real-time biometric information of the user (body temperature, pulse rate, facial expression).

[0190] Output: Collected biometric data.

[0191] Step 2:

[0192] The terminal transmits the collected biometric information to the server.

[0193] The collected data is transferred in real time to a server via the Internet.

[0194] Input: Collected biometric data.

[0195] Output: Biometric data sent to the server.

[0196] Step 3:

[0197] The server integrates the received biometric information to assess the user's current condition.

[0198] The server uses an AI model to assess, for example, a high pulse rate as a state of stress, and a low pulse rate as a state of relaxation.

[0199] Input: Biometric data sent to the server.

[0200] Output: User's current state assessment (e.g., stressed, relaxed, neutral).

[0201] Step 4:

[0202] The server generates optimal music based on the evaluation results.

[0203] Using an AI composition model, it improvises relaxing music based on the user's condition.

[0204] Input: The user's current condition assessment result.

[0205] Output: The generated music track data.

[0206] Step 5:

[0207] The server transmits the generated music track data to the on-board system of the autonomous vehicle.

[0208] The in-car system receives this music data and prepares it for playback.

[0209] Input: The generated music track data.

[0210] Output: Music track data sent to the in-car system.

[0211] Step 6:

[0212] The in-car system plays music.

[0213] Users can enjoy a relaxing effect by listening to the music that is played.

[0214] Input: Music track data sent to the in-car system.

[0215] Output: Music played in the car.

[0216] Step 7:

[0217] While music is playing, the smartwatch or smartphone continues to collect biometric information.

[0218] The biometric information is sent to the server again.

[0219] Input: Real-time biometric information of the user (data while music is playing).

[0220] Output: Continuous biometric data sent to the server.

[0221] Step 8:

[0222] The server compares biometric information before and after music playback to evaluate the user's reaction.

[0223] Based on this evaluation, feedback is provided to the AI ​​model to optimize the music generation algorithm.

[0224] Input: Biometric data before and after playback.

[0225] Output: User response evaluation results and optimized music generation algorithm.

[0226] The above is the processing flow of the system of this invention, which makes it possible to provide optimal relaxation music in real time and manage the user's stress.

[0227] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0228] This invention combines an emotion engine with a system that collects a user's biometric information in real time, evaluates the user's condition based on that information, and improvises and provides optimal music. Furthermore, by monitoring the user's reactions during music playback and optimizing the music generation algorithm based on that data, a more precise and personalized music experience is provided.

[0229] The device (smartwatch or smartphone) collects biometric information such as the user's body temperature, pulse rate, and facial expression. For example, a smartwatch uses a temperature sensor and a pulse sensor to measure body temperature and pulse rate. A smartphone uses a camera to capture the user's facial expression data and analyzes it using an emotion engine. This data is sent to a server in real time.

[0230] The server integrates the received biometric information and facial expression data and uses an emotion engine to recognize the user's emotions. For example, it analyzes the user's facial features from camera footage, and the emotion engine identifies the user's emotions from those expressions (e.g., joy, anger, sadness, and happiness). It then uses an AI model to evaluate the user's current state (e.g., stress level, fatigue level). For example, if the heart rate is higher than normal or if the emotion engine recognizes it as "stressed," the AI ​​will determine that the user is feeling stressed.

[0231] Based on the emotions and biometric information recognized by the emotion engine, the server uses an AI composition model to generate optimal music. For example, if you are feeling stressed, music with a relaxing effect will be generated. The generated music is sent from the server to the device, which then plays the music. At this time, the volume and tempo of the music are also adjusted according to the emotions recognized by the emotion engine.

[0232] While music is playing, the device again monitors the user's biometric information and transmits the playback data to the server in real time. The server compares the biometric information and emotional data before and during playback to evaluate the user's reaction. For example, if the pulse rate stabilizes after music playback and the emotion engine recognizes a "relaxed" feeling from the user's facial expression, the system will determine that the music was appropriate. Based on this feedback, the AI ​​model continues to learn and optimizes the music generation algorithm.

[0233] Furthermore, the device can obtain the user's schedule information and provide music tailored to specific situations, such as calming music during times when relaxation is needed. Furthermore, by using facial expression recognition technology and an emotion engine, the camera can analyze the user's facial expressions and more accurately assess the user's mood and emotions.

[0234] This system can provide users with an advanced music experience to improve their quality of life, including daily stress management, relaxation, and performance improvement. As a specific example, if a user has an important meeting in the morning, the smartwatch detects a high pulse rate and the emotion engine recognizes that the user's face is tense. The server receives this data and evaluates the user as feeling stressed. The AI ​​composition model generates music with a relaxing effect and plays it on the device. If the user's body temperature and pulse rate return to normal during playback and the emotion engine recognizes a relaxed facial expression, the system will evaluate the music as appropriate and reflect this in future music generation.

[0235] This allows users to always receive the most appropriate music for their situation, improving their quality of life. By integrating emotion recognition and biometric information, this system achieves a highly personalized music experience that goes beyond conventional music playback systems.

[0236] The processing flow will be explained below.

[0237] Step 1:

[0238] Devices (smartwatches and smartphones) collect biometric information from users. For example, smartwatches measure body temperature and pulse, and smartphones use cameras to capture facial expression data.

[0239] Step 2:

[0240] The device transmits the collected biometric information and facial expression data to a server in real time. Body temperature, pulse rate, and facial expression data are integrated and uploaded to the server as a user profile.

[0241] Step 3:

[0242] The server uses an emotion engine to recognize the user's emotions based on the received biometric information and facial expression data. For example, it analyzes the user's facial features from camera footage and identifies emotions from their expressions (e.g., joy, anger, sadness, or happiness).

[0243] Step 4:

[0244] The server uses an AI model based on the emotions and biometric information recognized through the emotion engine to evaluate the user's current state (e.g., stress level, fatigue level). For example, if the heart rate is higher than normal and the emotion engine recognizes this as "stress," it will determine that the user is feeling stressed.

[0245] Step 5:

[0246] The server uses an AI composition model to generate optimal music based on the user's assessed state. For example, it generates relaxing music for a user who is feeling stressed.

[0247] Step 6:

[0248] The server sends the generated music data to the terminal, which then imports the received music data and prepares to play the music.

[0249] Step 7:

[0250] The device plays music at the optimal volume. The volume settings are also adjusted based on the user's state. The emotion engine automatically adjusts the tempo and volume of the music based on the emotions it recognizes.

[0251] Step 8:

[0252] The device continues to monitor the user's biometric information while playing music, and the data is sent to the server in real time.

[0253] Step 9:

[0254] The server compares the user's biometric and emotional data before and during playback to evaluate their reaction. For example, if the user's pulse rate stabilizes after music playback and the emotion engine recognizes a relaxed facial expression, the system will determine that the music was appropriate.

[0255] Step 10:

[0256] The server will then provide feedback on the evaluation results and update the AI ​​model, which will optimize the music generation process from the next time onwards.

[0257] Step 11:

[0258] The device collects the user's schedule information and provides music tailored to specific situations, such as calming music during times when relaxation is needed.

[0259] Step 12:

[0260] The device uses the camera to analyze the user's facial expression data to assess their mood and emotions, providing a more personalized music experience.

[0261] Through this series of processes, the system can provide music that is best suited to the user's condition, improving the user's quality of life.

[0262] Example 2

[0263] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0264] Conventional music playback systems have had difficulty collecting a user's emotions and biometric information in real time and providing optimal music based on that information. In particular, there is a need to accurately assess a user's current state and emotions, and generate and provide personalized music based on that assessment. Furthermore, there is a need to provide a more precise and personalized music experience by monitoring the user's reactions during music playback in real time and incorporating the results into the music generation algorithm.

[0265] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0266] In this invention, the server includes means for collecting biometric information of a user, means for evaluating the user's current state based on the collected biometric information, means for generating optimal music according to the user's evaluated state, means for playing the generated music, means for monitoring the user's reaction during playback, means for optimizing a music generation algorithm based on the monitored user's reaction, means for integrating the biometric information and facial expression data and recognizing the user's emotion using an emotion engine, and means for transmitting the biometric information during music playback to the server in real time. This makes it possible to generate optimal personalized music based on the user's biometric information and emotion data and provide it in real time.

[0267] "Biometric information" refers to data related to individual vital activities, such as a user's body temperature, pulse rate, and respiratory rate.

[0268] "Facial expression data" is visual information based on a user's facial features and muscle movements, and is data used for emotion recognition.

[0269] An "emotion engine" is an algorithm or model that analyzes input facial expressions and biometric information to recognize the user's emotions and psychological state.

[0270] An "AI composition model" is an artificial intelligence algorithm used to generate personalized music based on a user's emotions and state.

[0271] "Monitoring" is the act of continuously observing the state or changes of an object and collecting data.

[0272] "Optimization" refers to adjusting parameters and algorithms to maximize the performance of a system or process according to specific objectives and conditions.

[0273] "Condition assessment" refers to analyzing and judging the user's current health and psychological state based on collected biometric information and facial expression data.

[0274] "Music generation" is the process of using specific algorithms and models to create music that responds to the user's state and emotions.

[0275] "Real-time" means that processing and response are nearly instantaneous, with almost no delay.

[0276] A "server" is a computer system that processes and manages data within a network and provides services in response to requests from terminals.

[0277] This invention is an advanced system that collects biometric and facial expression data from users, evaluates their emotions and state based on that data, and generates and delivers optimal music. Furthermore, it monitors users' reactions during music playback and optimizes the music generation algorithm based on the results, providing a precise and personalized music experience.

[0278] 1. Collection of biometric information

[0279] The device (smartwatch or smartphone) collects biometric information such as the user's body temperature, pulse rate, and facial expression. The smartwatch uses a temperature sensor and pulse rate sensor to measure the user's body temperature and pulse rate. The smartphone uses a camera to capture the user's facial expression data and analyzes it using an emotion engine.

[0280] Examples:

[0281] The smartwatch measures body temperature and pulse every second and transmits the data to a smartphone in real time.

[0282] The smartphone camera captures facial expressions, and the emotion engine analyzes the facial data to recognize the emotions of joy, anger, sadness, and happiness.

[0283] 2. Data submission and analysis

[0284] The biometric information collected by the device and analyzed facial expression data are sent to a server in real time. The server integrates this data and uses an emotion engine to recognize emotions. It also uses an AI model to assess the user's current state (e.g., stress level, fatigue level).

[0285] Specific server behavior:

[0286] The server stores the received biometric information and facial expression data in a database and passes the data to the analysis module.

[0287] The emotion engine checks the facial expression analysis results and identifies states such as "stressed" or "relaxed."

[0288] 3. Music Generation and Playback

[0289] The emotion engine recognizes the user's emotions, and the AI ​​composition model generates optimal music based on biometric information. The generated music is sent from the server to the device, where it is played in real time.

[0290] Examples:

[0291] If the user is assessed as feeling stressed, the server's AI composition model will generate relaxing music.

[0292] The generated music file is sent to the device, which then plays the music through its speakers.

[0293] 4. Monitoring and optimization during music playback

[0294] While music is playing, the device again monitors the user's biometric information and transmits it to the server in real time. The server compares the biometric information before and after playback and evaluates the user's reaction. Based on this feedback data, the AI ​​model continues to learn and optimizes the music generation algorithm.

[0295] Specific server behavior:

[0296] Biometric information before and during playback is compared to evaluate whether the user's pulse rate and facial expression are relaxed.

[0297] The AI ​​model is updated based on the feedback data, optimizing the next music generation parameters.

[0298] The system analyzes users' biometric and emotional data in real time and provides personalized music experiences, improving the quality of their lives.

[0299] Example prompt sentence:

[0300] "We want to design a system that collects biometric information from users and evaluates their emotions and state based on that data. We will then use an emotion engine and an AI composition model to generate optimal music and deliver it to them in real time."

[0301] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0302] Step 1:

[0303] Biometric data collection

[0304] The device (smartwatch) measures the user's body temperature and pulse rate.

[0305] Input: User's body temperature and pulse information

[0306] How it works: The smartwatch's sensors detect body temperature and pulse every second and collect this data.

[0307] Output: Temperature and pulse data set

[0308] Step 2:

[0309] Capturing facial expression data

[0310] The device (smartphone) uses a camera to capture the user's face and send it to the emotion engine.

[0311] Input: Video of the user's face

[0312] Specific operation: The smartphone camera captures facial expressions every second and passes the image data to the emotion engine.

[0313] Output: Analyzed facial expression data

[0314] Step 3:

[0315] Transmission of biometric and facial expression data

[0316] The device sends the collected biometric information and analyzed facial expression data to the server.

[0317] Input: Body temperature, pulse rate dataset and facial expression data

[0318] Specific operation: The terminal divides this data into packets and sends them to a server via the Internet.

[0319] Output: Biometric information and facial expression data received by the server

[0320] Step 4:

[0321] Data integration and analysis

[0322] The server integrates the received biometric information and facial expression data and analyzes them using an emotion engine.

[0323] Input: Biometric information and facial expression data received by the server

[0324] Specific operation: The server stores these data in a database and passes them to the analysis module.

[0325] Data calculation: The emotion engine on the server checks the facial expression analysis results and identifies the emotion.

[0326] Output: Identified emotions and integrated biometric information

[0327] Step 5:

[0328] Condition assessment

[0329] The server uses an AI model to assess the user's condition based on the identified emotions and biometric information.

[0330] Input: identified emotions and integrated biometric information

[0331] Specific operation: The AI ​​model analyzes biometric information and evaluates the user's stress level and fatigue level.

[0332] Data calculation: AI model calculates stress and fatigue levels.

[0333] Output: Evaluated user state (e.g., stress level, fatigue level)

[0334] Step 6:

[0335] Music Generation

[0336] The server uses an AI composition model to generate optimal music based on the user's evaluated state.

[0337] Input: Evaluated user status

[0338] How it works: The AI ​​composition model analyzes the input data and determines musical parameters (e.g., tempo, melody).

[0339] Data calculation: AI composition model generates relaxing music.

[0340] Output: Generated music data

[0341] Step 7:

[0342] Sending music data

[0343] The server transmits the generated music data to the terminal.

[0344] Input: Generated music data

[0345] Specific operation: The server divides the music data into packets and sends them to the terminal.

[0346] Output: Music data arriving on the device

[0347] Step 8:

[0348] Playing music

[0349] The terminal plays the received music data.

[0350] Input: Music data arriving on the device

[0351] What it does: The device decodes the music and plays it through the speaker, adjusting the volume and tempo in real time as needed.

[0352] Output: Music played to the user

[0353] Step 9:

[0354] Monitoring biological information during regeneration

[0355] The device monitors the user's biometric information even while music is playing.

[0356] Input: Biometric information during music playback

[0357] How it works: The smartwatch continuously measures your body temperature and pulse rate while music is playing.

[0358] Output: Body temperature and pulse data during playback

[0359] Step 10:

[0360] Sending monitoring results

[0361] The device transmits the biometric information being played back to the server in real time.

[0362] Input: Body temperature and pulse data during playback

[0363] Specific operation: The device divides the biometric information into packets and sends them to the server.

[0364] Output: Biometric information received by the server during playback

[0365] Step 11:

[0366] Feedback and Optimization

[0367] The server compares biometric information before and after music playback and optimizes the music generation algorithm based on an AI model.

[0368] Input: Biometric information before and during playback

[0369] What it does: The server analyzes the feedback data and adds new patterns and trends to the AI ​​model.

[0370] Data computation: AI models continue to learn and optimize their algorithms.

[0371] Output: Optimized music generation algorithm

[0372] (Application example 2)

[0373] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0374] Modern society demands methods to optimize users' mental and physical states in real time. However, conventional music generation systems have been unable to fully utilize users' biometric and emotional data, making it difficult to provide personalized music experiences. Furthermore, these systems lack the ability to utilize feedback to optimize music generation algorithms, making it difficult to provide a sustained, high-quality music experience. Furthermore, they lack the ability to provide music according to users' schedules and monitor their reactions in real time, preventing personalized music experiences that would improve users' quality of life.

[0375] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting biometric information of a user, means for evaluating the user's current state based on the collected biometric information, means for generating optimal music according to the user's evaluated state, means for playing the generated music, means for monitoring the user's reaction during playback, means for optimizing a music generation algorithm based on the monitored user's reaction, means for analyzing the user's facial expression data to evaluate the user's emotions, means for adjusting the volume and tempo based on the user's emotions using an emotion engine, and means for the terminal to link with external data in real time and instantly provide the generated music. This makes it possible to provide music optimized for the user's mental and physical state by integrating the user's biometric information and emotional data, and to continuously improve accuracy through real-time reaction monitoring and feedback.

[0376] "User's biometric information" refers to data relating to the user's body, such as body temperature, pulse rate, and facial expression.

[0377] "Means for collection" refers to devices and methods for acquiring biometric information of users using various sensors and devices.

[0378] "Means of evaluation" refers to the process or device that analyzes and judges the user's current condition based on the collected biometric information.

[0379] "Generative means" refers to the process or technology that produces optimal music based on the analyzed state.

[0380] "Means for playing" refers to a device or method for letting a user listen to the generated music.

[0381] A "monitoring means" is a process or device that continuously monitors the user's reaction during playback.

[0382] "Optimization methods" are processes or techniques that improve music generation algorithms based on monitored user response data.

[0383] "Facial expression data" is information relating to the facial expression of the user.

[0384] The "analysis means" is a process or device that analyzes the collected data to evaluate the user's emotions and state.

[0385] The "emotion engine" is a technology that recognizes the user's emotions from facial expression data and other data, and reflects this in the music generation.

[0386] A "terminal" is a device that acquires, sends, and receives data, such as a smartphone or smartwatch.

[0387] "External data" is additional or contextual information obtained from the internet or other services.

[0388] "Means of collaboration" refers to mechanisms and technologies that allow terminals to share information with external data in real time.

[0389] "Relaxing music" is music created with the aim of reducing tension and stress in the user and making them feel relaxed.

[0390] This invention is a system that collects biometric information from a user in real time and generates optimal music using an emotion engine and a generative AI model. Specific embodiments of this system are described below.

[0391] First, the system is equipped with a means to collect biometric information such as the user's body temperature, pulse rate, and facial expression. Specific examples of devices include smartwatches and smartphones. The smartwatch uses a temperature sensor and a pulse sensor to measure body temperature and pulse rate. The smartphone uses a camera to capture the user's facial expression data, which is then analyzed by the emotion engine.

[0392] The collected biometric information and facial expression data are sent to a server in real time. The server integrates this information and uses an emotion engine to recognize the user's emotions. For example, the server analyzes the user's facial features from camera footage, and the emotion engine identifies the user's emotions from those expressions (e.g., joy, anger, sadness, or happiness). Furthermore, an AI model is used to evaluate the user's current state (e.g., stress level, fatigue level). Specifically, if the heart rate is higher than normal or if the emotion engine recognizes it as "stress," the AI ​​will determine that the user is feeling stressed.

[0393] The server then uses an AI composition model to generate optimal music based on the emotions and biometric information recognized by the emotion engine. For example, if it determines that the user is feeling stressed, it will generate music with a relaxing effect. The generated music is sent from the server to the device and played on the device. At this time, the volume and tempo of the music are also adjusted according to the emotions recognized by the emotion engine.

[0394] While music is playing, the device again monitors the user's biometric information and transmits the playback data to the server in real time. The server compares the biometric and emotional data before and during playback to evaluate the user's reaction. For example, if the user's pulse rate stabilizes after music playback and the emotion engine recognizes a "relaxed" feeling from the user's facial expression, the system will determine that the music was appropriate. Based on this feedback, the AI ​​composition model continues to learn and optimizes the music generation algorithm.

[0395] As a specific example, if a user has an important meeting in the morning, the smartwatch detects a high pulse rate, and the smartphone's emotion engine recognizes the user's tension. The server receives this data and evaluates the user as feeling stressed. The AI ​​composition model then generates relaxing music and plays it on the device. If the user's body temperature and pulse rate return to normal during playback and the emotion engine recognizes a relaxed facial expression, the system will determine that the music was appropriate and can reflect this in future playbacks.

[0396] The following is an example of a prompt sentence:

[0397] Generate optimal relaxing music for the user based on the following biometric and emotional data:

[0398] Body temperature: 37.5°C

[0399] Pulse rate: 85 bpm

[0400] Emotion: Tension (facial expression data)

[0401] This invention allows users to always receive the most appropriate music for their situation, thereby improving their quality of life. The system provides users with a highly personalized music experience by integrating emotion recognition and biometric information.

[0402] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0403] Step 1:

[0404] The devices (smartwatch and smartphone) collect the user's biometric information and facial expression data. Specifically, the smartwatch's temperature sensor and pulse sensor are used to measure body temperature and pulse, and the smartphone's camera is used to capture facial expression data. The input is sensor data and camera images, and the output is the collected biometric information and facial expression data.

[0405] Step 2:

[0406] The device transmits the collected biometric information and facial expression data to the server in real time. The input is the data collected in step 1, and the output is the data transmitted to the server.

[0407] Step 3:

[0408] The server integrates the received biometric information and facial expression data and uses an emotion engine to recognize the user's emotions. Specifically, it analyzes facial features from camera footage and identifies emotions based on the facial expressions. The input is the biometric information and facial expression data sent from the device, and the output is the user's emotional data.

[0409] Step 4:

[0410] The server uses an AI composition model to generate optimal music based on the user's emotional data and biometric information. For example, if the user is feeling stressed, it will generate music with a relaxing effect. The input is the user's emotional data and biometric information, and the output is the generated music data.

[0411] Step 5:

[0412] The server sends the generated music data to the terminal. The input is the generated music data, and the output is the music data sent to the terminal.

[0413] Step 6:

[0414] The device plays the received music data. Specifically, the smartphone plays the music and adjusts the volume and tempo according to the emotion recognized by the emotion engine. The input is the music data sent from the server, and the output is the music being played.

[0415] Step 7:

[0416] The device again monitors the user's biometric information while music is playing and transmits it to the server in real time. The input is the biometric information collected during playback, and the output is the data transmitted to the server.

[0417] Step 8:

[0418] The server compares the biometric and emotional data before and during playback to evaluate the user's reaction. The input is the data before and during playback, and the output is the evaluated reaction data.

[0419] Step 9:

[0420] The server optimizes the music generation algorithm based on the evaluated reaction data. Specifically, if the user's pulse stabilizes after playback and the emotion engine recognizes "relaxation," it trains the AI ​​composition model and improves the algorithm. The input is the evaluated reaction data, and the output is an optimized music generation algorithm.

[0421] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0422] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0423] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0424] [Second embodiment]

[0425] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0426] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0427] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0428] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0429] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0430] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0431] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0432] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0433] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0434] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0435] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0436] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0437] This invention is a system that collects a user's biometric information in real time, evaluates the user's condition based on that information, and then improvises and provides optimal music. Furthermore, it monitors the user's reactions while the music is playing, and optimizes the music generation algorithm based on that data.

[0438] The device (smartwatch or smartphone) collects biometric information such as the user's body temperature, pulse rate, and facial expression. For example, a smartwatch uses a temperature sensor and a pulse sensor to measure body temperature and pulse rate. A smartphone uses a camera to capture the user's facial expression data. This data is sent to a server in real time.

[0439] The server integrates the received biometric information and uses an AI model to assess the user's current state. For example, if the pulse rate is higher than normal, the AI ​​will assess that the user is feeling stressed. Based on the assessment results, the server uses an AI composition model to generate music that is optimal for the user's state. For example, it will create relaxing music for a user who is feeling stressed.

[0440] The generated music is sent from the server to the device, which then plays it. The playback volume is also optimized according to the user's condition. While the music is playing, the device again monitors the user's biometric information. By comparing the data before and after playback, the server evaluates the user's reaction and provides feedback to the AI ​​model. This information is used to optimize the music generation algorithm.

[0441] In addition, the system collects information about the user's schedule and provides music that is optimal for specific situations. For example, calming music can be provided when the user wants to relax. Using facial expression recognition technology, the camera can analyze the user's facial expressions to more accurately assess the user's mood and emotions.

[0442] As a concrete example of this system, suppose a user has an important meeting in the morning and their smartwatch detects a high pulse rate. The server receives this data and determines that the user is feeling stressed. The AI ​​composition model generates relaxing music and plays it on the device. If the user's body temperature and pulse rate return to normal during playback, the system determines that the music was appropriate. This feedback is reflected in future music generation, providing a more personalized music experience.

[0443] This allows users to always receive the most appropriate music for their situation, improving their quality of life. This system is highly effective for daily stress management, relaxation, and performance improvement.

[0444] The processing flow will be explained below.

[0445] Step 1:

[0446] Devices use sensors to collect biometric information about users: for example, a smartwatch measures a user's temperature and pulse, while a smartphone uses a camera to capture facial expression data.

[0447] Step 2:

[0448] The device transmits the collected biometric information to a server in real time, where body temperature, pulse rate, and facial expression data are integrated and uploaded to the server as a user profile.

[0449] Step 3:

[0450] The server uses an AI model to evaluate the user's current state based on the received biometric information. For example, if the heart rate is higher than normal, the AI ​​will determine that the user is feeling stressed.

[0451] Step 4:

[0452] The server uses an AI composition model to generate optimal music based on the user's assessed state: if the user is feeling stressed, music with a relaxing effect will be generated.

[0453] Step 5:

[0454] The server sends the generated music data to the terminal, which then imports the received music data and prepares for playback.

[0455] Step 6:

[0456] The device will play music at an optimal volume, and the volume settings will also be adjusted based on the user's state.

[0457] Step 7:

[0458] The device continues to monitor the user's biometric information while music is playing, and transmits the playback data to the server in real time.

[0459] Step 8:

[0460] The server compares the biometric data before and during playback to evaluate the user's reaction, for example, checking whether the pulse rate stabilizes after music playback.

[0461] Step 9:

[0462] The server will then provide feedback on the evaluation results and update the AI ​​model, which will optimize future music generation.

[0463] Step 10:

[0464] The device collects the user's schedule information and provides music tailored to specific situations, such as calming music during times when relaxation is needed.

[0465] Step 11:

[0466] The device uses the camera to analyze the user's facial expression data to assess their mood and emotions, which can then provide a more personalized music experience.

[0467] Through this series of steps, the system can provide the user with music that best suits their condition, improving their quality of life.

[0468] Example 1

[0469] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0470] Conventional music generation systems were unable to properly reflect the user's biological state or mood, making it difficult to provide optimal music for each individual user. Furthermore, they lacked the ability to monitor the user's reactions in real time during playback and adjust the system accordingly, making it difficult to maximize the effect of the music. Another issue was the inability to provide music based on the user's schedule information or specific situations.

[0471] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0472] In this invention, the server includes a means for transmitting a user's biometric information to the server in real time, a means for integrating the transmitted biometric information to evaluate the user's current state, and a means for generating optimal music based on the evaluation results. This makes it possible to accurately evaluate the user's state and provide optimal music based on that evaluation. Furthermore, by optimizing the music generation algorithm based on the monitored user's reactions, the effectiveness of the music can be maximized. Furthermore, music can be provided based on the user's schedule information or specific situations, allowing for a personalized music experience for each user.

[0473] "User's biometric information" is data that indicates the user's physical condition, such as body temperature, pulse rate, and facial expression.

[0474] "Means of collection" refers to devices or technologies such as sensors or cameras used to acquire biometric information.

[0475] "Transmitting means" refers to the communication technology or device used to transmit the collected data to the server.

[0476] "Means of integration" refers to techniques or methods for processing multiple pieces of data into one.

[0477] The "assessment means" refers to algorithms or models that analyze the integrated data to determine the user's condition.

[0478] The "generative means" refers to an AI composition model or software that creates music based on the evaluation results.

[0479] "Means for transmitting and playing" refers to the technology and devices that send the generated music data to a terminal and play it back as sound.

[0480] "Monitoring means" refers to technology or devices that monitor and record the user's biometric information again while music is being played.

[0481] "Optimizing means" refers to methods for improving the music generation algorithm based on the monitored data to provide better results.

[0482] "Schedule information" is data related to the user's plans and schedules.

[0483] A "situation" is a specific situation or environment, or a specific time or event that a user faces.

[0484] "Mood and emotions" refers to information that indicates the user's psychological state and feelings.

[0485] "Means of analysis" refers to the technology and algorithms used to analyze facial expression data and determine the user's mood and emotions.

[0486] This invention is a system that collects a user's biometric information in real time, evaluates the user's condition based on that information, and then improvises and provides optimal music. Furthermore, it monitors the user's reactions while the music is playing, and optimizes the music generation algorithm based on that data.

[0487] Collection and transmission methods

[0488] Devices (smartwatches and smartphones)

[0489] The device collects biometric information such as the user's body temperature, pulse rate, and facial expression. Specific hardware used includes a temperature sensor and pulse rate sensor in the smartwatch and a smartphone camera. This data is sent to a server in real time. Bluetooth and Wi-Fi are used as communication methods.

[0490] Examples:

[0491] The smartwatch measures body temperature with a temperature sensor and heart rate with a pulse sensor. This data is sent to a smartphone via Bluetooth, and then to a server via Wi-Fi. At the same time, the smartphone's camera captures facial expression data using biometric authentication technology, which is also sent to the server.

[0492] Evaluation methods

[0493] server

[0494] The server integrates the received biometric information and uses an AI model to assess the user's current state. Specifically, it analyzes the integrated data and assesses that the user is feeling stressed based on data such as "body temperature: 37.2°C, pulse rate: 110 BPM, facial expression: stressed facial expression."

[0495] Examples:

[0496] The server receives the data "Temperature: 37.2°C, Pulse: 110 BPM, Facial Expression: Stressed Facial Expression" and uses the AI ​​model to evaluate that "the user is feeling stressed." Based on this evaluation, the prompt sentence for the music to be generated is determined as follows: "User's biological information: Pulse 110 BPM, Body temperature 37.2°C, Facial expression: Stressed. Purpose of the music to be generated: Relaxation."

[0497] A means of generating and playing music

[0498] server

[0499] Based on the evaluation results, an AI composition model is used to generate music that is optimal for the user's state, such as music with a relaxing effect aimed at reducing stress.

[0500] Terminal

[0501] The generated music is sent from the server to the device, where it is played back, with the volume and tone optimized according to the user's state.

[0502] Examples:

[0503] The server generates slow-tempo music as "stress-reducing music" and sends it to the smartphone, where it plays the music and automatically adjusts the volume.

[0504] Monitoring and optimization measures

[0505] Terminal

[0506] While the music is playing, the device again monitors the user's body temperature, pulse rate, and facial expressions, and by comparing the data before and after playback, the effect of the music can be evaluated.

[0507] server

[0508] The server compares the data before and after playback and optimizes the music generation algorithm based on the monitored user reactions. This feedback updates the AI ​​model and is reflected in the next music generation.

[0509] Examples:

[0510] If the data before playback was "body temperature: 37.2°C, pulse rate: 110 BPM, facial expression: stressed expression," and the data after playback changed to "body temperature: 36.8°C, pulse rate: 75 BPM, facial expression: relaxed," the server would evaluate the music as effective and update the AI ​​model.

[0511] Collection and utilization of schedule information

[0512] Terminal

[0513] The system also collects information about the user's schedule and provides music that is optimal for a specific situation. For example, if the user has an important meeting in the morning, the system will provide relaxing music based on that information.

[0514] Examples:

[0515] If a user has entered in their schedule that they have an important meeting coming up in the morning, the server will receive that information and send relaxing music to the device to play to help ease tension.

[0516] This step-by-step approach allows us to provide a music experience that is optimized for each user's condition, improving their quality of life.

[0517] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0518] Step 1:

[0519] Biometric data collection

[0520] The device (smartwatch or smartphone) uses sensors and cameras to collect biometric information such as the user's body temperature, pulse rate, and facial expression. Specifically, the smartwatch measures body temperature with a temperature sensor and heart rate with a pulse sensor. The smartphone camera also takes a picture of the user's face and captures facial expression data.

[0521] Input: User's body temperature, pulse rate, facial expression

[0522] Output: Collected biometric information (body temperature, pulse, facial expression)

[0523] Step 2:

[0524] Sending data to the server

[0525] The device transmits the collected biometric information to a server in real time using Bluetooth or Wi-Fi. For example, data from a smartwatch is transmitted to a smartphone via Bluetooth, and then to the server via Wi-Fi.

[0526] Input: Collected biometric information (body temperature, pulse, facial expression)

[0527] Output: Biometric information sent to the server

[0528] Step 3:

[0529] Data integration and condition assessment

[0530] The server integrates the received biometric information and uses an AI model to evaluate the user's current condition. For example, if data such as "Temperature: 37.2°C, Pulse: 110 BPM, Facial Expression: Stressed Facial Expression" is received, the AI ​​model will analyze this data and conclude that "the user is feeling stressed."

[0531] Input: Biometric information sent to the server

[0532] Output: User's current status evaluation result

[0533] Step 4:

[0534] Music Generation

[0535] Based on the evaluation results, the server uses an AI composition model to generate music that best suits the user's state of mind. For example, it generates music with a relaxing effect for a user who is feeling stressed.

[0536] Input: User's current status evaluation result

[0537] Output: Generated music files

[0538] Step 5:

[0539] Sending and playing music

[0540] The server sends the generated music to the device, which then plays it. The volume and tone of the music are automatically adjusted according to the user's state. Specifically, the server sends music data to the smartphone, which then plays it through its speaker.

[0541] Input: Generated music files

[0542] Output: Music being played

[0543] Step 6:

[0544] Monitoring while music is playing

[0545] While music is playing, the device again monitors the user's body temperature, pulse rate, and facial expressions. This allows data to be collected before and after playback and records any changes. For example, while music is playing, the smartwatch again measures the user's body temperature and pulse rate, and the smartphone captures facial expressions.

[0546] Input: Monitoring results of biological information while music is playing

[0547] Output: Monitored vital signs

[0548] Step 7:

[0549] Reaction evaluation and algorithm optimization

[0550] The server compares the data before and after playback and evaluates the user's reaction. Based on the evaluation results, it optimizes the music generation algorithm and updates the AI ​​model. This feedback is used for the next music generation.

[0551] Input: Monitored biometric data (before and after playback)

[0552] Output: Optimized music generation algorithm

[0553] (Application example 1)

[0554] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0555] Currently, stress management and relaxation while in a vehicle primarily depend on manually selected music and entertainment information. However, manual selection makes it difficult to achieve an appropriate relaxation effect that matches the user's real-time state. Furthermore, conventional music playback systems do not optimize the system to reflect the user's real-time biometric information, resulting in a lack of immediate effects on stress reduction and mood improvement. The present invention aims to solve these problems and provide a system that highly automates relaxation and stress management in a vehicle.

[0556] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0557] In this invention, the server includes means for collecting biometric information of a user, means for evaluating the user's current state based on the collected biometric information, means for generating optimal music according to the evaluated state of the user, means for playing the generated music, means for monitoring the user's reaction during playback, means for optimizing a music generation algorithm based on the monitored user's reaction, and means for linking with the on-board system of the autonomous vehicle, thereby enabling the provision of optimal relaxation music in real time and stress management for the user.

[0558] "User's biometric information" refers to data related to the user's health and emotional state that can be obtained in real time, such as body temperature, pulse rate, and facial expression.

[0559] "User's current state" refers to the user's mental or physical state, such as stress level or degree of relaxation, assessed based on collected biometric information.

[0560] "Optimal music" is music created based on the user's biometric information and current condition to maximize the desired effect, such as relaxation or improved concentration.

[0561] "Generated music" is music that is improvised using artificial intelligence models or the like, depending on the user's current assessed state.

[0562] "Means for playing" refers to a device or system that outputs the generated music so that the user can listen to it.

[0563] "Monitoring means" refers to devices or software that recollect biometric information such as the user's body temperature, pulse rate, and facial expressions while music is being played, and track changes.

[0564] A "music generation algorithm" is a computer program or procedure for generating optimal music based on a user's biometric information and condition.

[0565] An "autonomous vehicle" is a vehicle that operates automatically and can travel safely without user intervention.

[0566] "In-vehicle systems" is a general term for information processing devices, communication devices, entertainment systems, etc. installed in autonomous vehicles.

[0567] This invention is a system that collects a user's biometric information in real time and generates and provides music based on that information. Its purpose is to automate relaxation and stress management for users in self-driving vehicles. Specific embodiments of the invention are described below.

[0568] composition

[0569] The system mainly consists of the following components:

[0570] 1. Biometric information collection device: Collects biometric information such as the user's body temperature, pulse rate, and facial expression. Specifically, sensors installed in smartwatches and smartphones are used.

[0571] 2. Biometric information processing server (hereinafter referred to as the server): Integrates collected biometric information and evaluates the user's current condition. An AI model is used for the evaluation.

[0572] 3. Music Generation Server: Generates optimal music based on the user's state evaluation results. AI composition model is used.

[0573] 4. In-vehicle system for autonomous vehicles: Plays generated music and continuously monitors the user's biometric information.

[0574] Processing flow

[0575] First, a smartwatch or smartphone collects the user's biometric information. The collected data is sent to a server in real time. The server then integrates this data and uses an AI model to evaluate the user's current state. For example, if the user's pulse rate is high, it is determined that the user is feeling stressed.

[0576] Based on the evaluation results, the music generation server generates appropriate music—for example, classical or ambient music for a user who is feeling stressed—and the generated music track is sent to the autonomous vehicle's in-vehicle system for playback inside the vehicle.

[0577] While music is playing, the smartwatch or smartphone continues to collect biometric information and transmits it to the server. By comparing the biometric information before and after playback, the server evaluates the user's reaction and uses this information as feedback to optimize the music generation algorithm.

[0578] Explanation and examples

[0579] As a concrete example, consider a situation where a user experiences a high pulse rate during a long drive. The server evaluates this as a stressful state and generates a relaxing classical music track. This track is then played on the in-car system. If the user's pulse rate returns to normal after playback, the system considers the music track appropriate and reflects this in future music generation.

[0580] Example prompt for a generative AI model:

[0581] "Generate optimal music based on the user's current biometric information. Evaluate their condition based on the information below and select a music track of an appropriate genre.

[0582] Heart rate: 110, temperature: 37.5, facial expression: neutral."

[0583] This makes it possible to provide optimal relaxation music in real time and manage the user's stress, improving the user's comfort in autonomous vehicles and providing a stress-free travel experience.

[0584] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0585] Step 1:

[0586] Smartwatches and smartphones collect users' biometric information.

[0587] Specifically, the smartwatch's pulse and temperature sensors and the smartphone's camera are used to measure the user's body temperature, pulse, and facial expression.

[0588] Input: Real-time biometric information of the user (body temperature, pulse rate, facial expression).

[0589] Output: Collected biometric data.

[0590] Step 2:

[0591] The terminal transmits the collected biometric information to the server.

[0592] The collected data is transferred in real time to a server via the Internet.

[0593] Input: Collected biometric data.

[0594] Output: Biometric data sent to the server.

[0595] Step 3:

[0596] The server integrates the received biometric information to assess the user's current condition.

[0597] The server uses an AI model to assess, for example, a high pulse rate as a state of stress, and a low pulse rate as a state of relaxation.

[0598] Input: Biometric data sent to the server.

[0599] Output: User's current state assessment (e.g., stressed, relaxed, neutral).

[0600] Step 4:

[0601] The server generates optimal music based on the evaluation results.

[0602] Using an AI composition model, it improvises relaxing music based on the user's condition.

[0603] Input: The user's current condition assessment result.

[0604] Output: The generated music track data.

[0605] Step 5:

[0606] The server transmits the generated music track data to the on-board system of the autonomous vehicle.

[0607] The in-car system receives this music data and prepares it for playback.

[0608] Input: The generated music track data.

[0609] Output: Music track data sent to the in-car system.

[0610] Step 6:

[0611] The in-car system plays music.

[0612] Users can enjoy a relaxing effect by listening to the music that is played.

[0613] Input: Music track data sent to the in-car system.

[0614] Output: Music played in the car.

[0615] Step 7:

[0616] While music is playing, the smartwatch or smartphone continues to collect biometric information.

[0617] The biometric information is sent to the server again.

[0618] Input: Real-time biometric information of the user (data while music is playing).

[0619] Output: Continuous biometric data sent to the server.

[0620] Step 8:

[0621] The server compares biometric information before and after music playback to evaluate the user's reaction.

[0622] Based on this evaluation, feedback is provided to the AI ​​model to optimize the music generation algorithm.

[0623] Input: Biometric data before and after playback.

[0624] Output: User response evaluation results and optimized music generation algorithm.

[0625] The above is the processing flow of the system of this invention, which makes it possible to provide optimal relaxation music in real time and manage the user's stress.

[0626] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0627] This invention combines an emotion engine with a system that collects a user's biometric information in real time, evaluates the user's condition based on that information, and improvises and provides optimal music. Furthermore, by monitoring the user's reactions during music playback and optimizing the music generation algorithm based on that data, a more precise and personalized music experience is provided.

[0628] The device (smartwatch or smartphone) collects biometric information such as the user's body temperature, pulse rate, and facial expression. For example, a smartwatch uses a temperature sensor and a pulse sensor to measure body temperature and pulse rate. A smartphone uses a camera to capture the user's facial expression data and analyzes it using an emotion engine. This data is sent to a server in real time.

[0629] The server integrates the received biometric information and facial expression data and uses an emotion engine to recognize the user's emotions. For example, it analyzes the user's facial features from camera footage, and the emotion engine identifies the user's emotions from those expressions (e.g., joy, anger, sadness, and happiness). It then uses an AI model to evaluate the user's current state (e.g., stress level, fatigue level). For example, if the heart rate is higher than normal or if the emotion engine recognizes it as "stressed," the AI ​​will determine that the user is feeling stressed.

[0630] Based on the emotions and biometric information recognized by the emotion engine, the server uses an AI composition model to generate optimal music. For example, if you are feeling stressed, music with a relaxing effect will be generated. The generated music is sent from the server to the device, which then plays the music. At this time, the volume and tempo of the music are also adjusted according to the emotions recognized by the emotion engine.

[0631] While music is playing, the device again monitors the user's biometric information and transmits the playback data to the server in real time. The server compares the biometric information and emotional data before and during playback to evaluate the user's reaction. For example, if the pulse rate stabilizes after music playback and the emotion engine recognizes a "relaxed" feeling from the user's facial expression, the system will determine that the music was appropriate. Based on this feedback, the AI ​​model continues to learn and optimizes the music generation algorithm.

[0632] Furthermore, the device can obtain the user's schedule information and provide music tailored to specific situations, such as calming music during times when relaxation is needed. Furthermore, by using facial expression recognition technology and an emotion engine, the camera can analyze the user's facial expressions and more accurately assess the user's mood and emotions.

[0633] This system can provide users with an advanced music experience to improve their quality of life, including daily stress management, relaxation, and performance improvement. As a specific example, if a user has an important meeting in the morning, the smartwatch detects a high pulse rate and the emotion engine recognizes that the user's face is tense. The server receives this data and evaluates the user as feeling stressed. The AI ​​composition model generates music with a relaxing effect and plays it on the device. If the user's body temperature and pulse rate return to normal during playback and the emotion engine recognizes a relaxed facial expression, the system will evaluate the music as appropriate and reflect this in future music generation.

[0634] This allows users to always receive the most appropriate music for their situation, improving their quality of life. By integrating emotion recognition and biometric information, this system achieves a highly personalized music experience that goes beyond conventional music playback systems.

[0635] The processing flow will be explained below.

[0636] Step 1:

[0637] Devices (smartwatches and smartphones) collect biometric information from users. For example, smartwatches measure body temperature and pulse, and smartphones use cameras to capture facial expression data.

[0638] Step 2:

[0639] The device transmits the collected biometric information and facial expression data to a server in real time. Body temperature, pulse rate, and facial expression data are integrated and uploaded to the server as a user profile.

[0640] Step 3:

[0641] The server uses an emotion engine to recognize the user's emotions based on the received biometric information and facial expression data. For example, it analyzes the user's facial features from camera footage and identifies emotions from their expressions (e.g., joy, anger, sadness, or happiness).

[0642] Step 4:

[0643] The server uses an AI model based on the emotions and biometric information recognized through the emotion engine to evaluate the user's current state (e.g., stress level, fatigue level). For example, if the heart rate is higher than normal and the emotion engine recognizes this as "stress," it will determine that the user is feeling stressed.

[0644] Step 5:

[0645] The server uses an AI composition model to generate optimal music based on the user's assessed state. For example, it generates relaxing music for a user who is feeling stressed.

[0646] Step 6:

[0647] The server sends the generated music data to the terminal, which then imports the received music data and prepares to play the music.

[0648] Step 7:

[0649] The device plays music at the optimal volume. The volume settings are also adjusted based on the user's state. The emotion engine automatically adjusts the tempo and volume of the music based on the emotions it recognizes.

[0650] Step 8:

[0651] The device continues to monitor the user's biometric information while playing music, and the data is sent to the server in real time.

[0652] Step 9:

[0653] The server compares the user's biometric and emotional data before and during playback to evaluate their reaction. For example, if the user's pulse rate stabilizes after music playback and the emotion engine recognizes a relaxed facial expression, the system will determine that the music was appropriate.

[0654] Step 10:

[0655] The server will then provide feedback on the evaluation results and update the AI ​​model, which will optimize the music generation process from the next time onwards.

[0656] Step 11:

[0657] The device collects the user's schedule information and provides music tailored to specific situations, such as calming music during times when relaxation is needed.

[0658] Step 12:

[0659] The device uses the camera to analyze the user's facial expression data to assess their mood and emotions, providing a more personalized music experience.

[0660] Through this series of processes, the system can provide music that is best suited to the user's condition, improving the user's quality of life.

[0661] Example 2

[0662] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0663] Conventional music playback systems have had difficulty collecting a user's emotions and biometric information in real time and providing optimal music based on that information. In particular, there is a need to accurately assess a user's current state and emotions, and generate and provide personalized music based on that assessment. Furthermore, there is a need to provide a more precise and personalized music experience by monitoring the user's reactions during music playback in real time and incorporating the results into the music generation algorithm.

[0664] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0665] In this invention, the server includes means for collecting biometric information of a user, means for evaluating the user's current state based on the collected biometric information, means for generating optimal music according to the user's evaluated state, means for playing the generated music, means for monitoring the user's reaction during playback, means for optimizing a music generation algorithm based on the monitored user's reaction, means for integrating the biometric information and facial expression data and recognizing the user's emotion using an emotion engine, and means for transmitting the biometric information during music playback to the server in real time. This makes it possible to generate optimal personalized music based on the user's biometric information and emotion data and provide it in real time.

[0666] "Biometric information" refers to data related to individual vital activities, such as a user's body temperature, pulse rate, and respiratory rate.

[0667] "Facial expression data" is visual information based on a user's facial features and muscle movements, and is data used for emotion recognition.

[0668] An "emotion engine" is an algorithm or model that analyzes input facial expressions and biometric information to recognize the user's emotions and psychological state.

[0669] An "AI composition model" is an artificial intelligence algorithm used to generate personalized music based on a user's emotions and state.

[0670] "Monitoring" is the act of continuously observing the state or changes of an object and collecting data.

[0671] "Optimization" refers to adjusting parameters and algorithms to maximize the performance of a system or process according to specific objectives and conditions.

[0672] "Condition assessment" refers to analyzing and judging the user's current health and psychological state based on collected biometric information and facial expression data.

[0673] "Music generation" is the process of using specific algorithms and models to create music that responds to the user's state and emotions.

[0674] "Real-time" means that processing and response are nearly instantaneous, with almost no delay.

[0675] A "server" is a computer system that processes and manages data within a network and provides services in response to requests from terminals.

[0676] This invention is an advanced system that collects biometric and facial expression data from users, evaluates their emotions and state based on that data, and generates and delivers optimal music. Furthermore, it monitors users' reactions during music playback and optimizes the music generation algorithm based on the results, providing a precise and personalized music experience.

[0677] 1. Collection of biometric information

[0678] The device (smartwatch or smartphone) collects biometric information such as the user's body temperature, pulse rate, and facial expression. The smartwatch uses a temperature sensor and pulse rate sensor to measure the user's body temperature and pulse rate. The smartphone uses a camera to capture the user's facial expression data and analyzes it using an emotion engine.

[0679] Examples:

[0680] The smartwatch measures body temperature and pulse every second and transmits the data to a smartphone in real time.

[0681] The smartphone camera captures facial expressions, and the emotion engine analyzes the facial data to recognize the emotions of joy, anger, sadness, and happiness.

[0682] 2. Data submission and analysis

[0683] The biometric information collected by the device and analyzed facial expression data are sent to a server in real time. The server integrates this data and uses an emotion engine to recognize emotions. It also uses an AI model to assess the user's current state (e.g., stress level, fatigue level).

[0684] Specific server behavior:

[0685] The server stores the received biometric information and facial expression data in a database and passes the data to the analysis module.

[0686] The emotion engine checks the facial expression analysis results and identifies states such as "stressed" or "relaxed."

[0687] 3. Music Generation and Playback

[0688] The emotion engine recognizes the user's emotions, and the AI ​​composition model generates optimal music based on biometric information. The generated music is sent from the server to the device, where it is played in real time.

[0689] Examples:

[0690] If the user is assessed as feeling stressed, the server's AI composition model will generate relaxing music.

[0691] The generated music file is sent to the device, which then plays the music through its speakers.

[0692] 4. Monitoring and optimization during music playback

[0693] While music is playing, the device again monitors the user's biometric information and transmits it to the server in real time. The server compares the biometric information before and after playback and evaluates the user's reaction. Based on this feedback data, the AI ​​model continues to learn and optimizes the music generation algorithm.

[0694] Specific server behavior:

[0695] Biometric information before and during playback is compared to evaluate whether the user's pulse rate and facial expression are relaxed.

[0696] The AI ​​model is updated based on the feedback data, optimizing the next music generation parameters.

[0697] The system analyzes users' biometric and emotional data in real time and provides personalized music experiences, improving the quality of their lives.

[0698] Example prompt sentence:

[0699] "We want to design a system that collects biometric information from users and evaluates their emotions and state based on that data. We will then use an emotion engine and an AI composition model to generate optimal music and deliver it to them in real time."

[0700] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0701] Step 1:

[0702] Biometric data collection

[0703] The device (smartwatch) measures the user's body temperature and pulse rate.

[0704] Input: User's body temperature and pulse information

[0705] How it works: The smartwatch's sensors detect body temperature and pulse every second and collect this data.

[0706] Output: Temperature and pulse data set

[0707] Step 2:

[0708] Capturing facial expression data

[0709] The device (smartphone) uses a camera to capture the user's face and send it to the emotion engine.

[0710] Input: Video of the user's face

[0711] Specific operation: The smartphone camera captures facial expressions every second and passes the image data to the emotion engine.

[0712] Output: Analyzed facial expression data

[0713] Step 3:

[0714] Transmission of biometric and facial expression data

[0715] The device sends the collected biometric information and analyzed facial expression data to the server.

[0716] Input: Body temperature, pulse rate dataset and facial expression data

[0717] Specific operation: The terminal divides this data into packets and sends them to a server via the Internet.

[0718] Output: Biometric information and facial expression data received by the server

[0719] Step 4:

[0720] Data integration and analysis

[0721] The server integrates the received biometric information and facial expression data and analyzes them using an emotion engine.

[0722] Input: Biometric information and facial expression data received by the server

[0723] Specific operation: The server stores these data in a database and passes them to the analysis module.

[0724] Data calculation: The emotion engine on the server checks the facial expression analysis results and identifies the emotion.

[0725] Output: Identified emotions and integrated biometric information

[0726] Step 5:

[0727] Condition assessment

[0728] The server uses an AI model to assess the user's condition based on the identified emotions and biometric information.

[0729] Input: identified emotions and integrated biometric information

[0730] Specific operation: The AI ​​model analyzes biometric information and evaluates the user's stress level and fatigue level.

[0731] Data calculation: AI model calculates stress and fatigue levels.

[0732] Output: Evaluated user state (e.g., stress level, fatigue level)

[0733] Step 6:

[0734] Music Generation

[0735] The server uses an AI composition model to generate optimal music based on the user's evaluated state.

[0736] Input: Evaluated user status

[0737] How it works: The AI ​​composition model analyzes the input data and determines musical parameters (e.g., tempo, melody).

[0738] Data calculation: AI composition model generates relaxing music.

[0739] Output: Generated music data

[0740] Step 7:

[0741] Sending music data

[0742] The server transmits the generated music data to the terminal.

[0743] Input: Generated music data

[0744] Specific operation: The server divides the music data into packets and sends them to the terminal.

[0745] Output: Music data arriving on the device

[0746] Step 8:

[0747] Playing music

[0748] The terminal plays the received music data.

[0749] Input: Music data arriving on the device

[0750] What it does: The device decodes the music and plays it through the speaker, adjusting the volume and tempo in real time as needed.

[0751] Output: Music played to the user

[0752] Step 9:

[0753] Monitoring biological information during regeneration

[0754] The device monitors the user's biometric information even while music is playing.

[0755] Input: Biometric information during music playback

[0756] How it works: The smartwatch continuously measures your body temperature and pulse rate while music is playing.

[0757] Output: Body temperature and pulse data during playback

[0758] Step 10:

[0759] Sending monitoring results

[0760] The device transmits the biometric information being played back to the server in real time.

[0761] Input: Body temperature and pulse data during playback

[0762] Specific operation: The device divides the biometric information into packets and sends them to the server.

[0763] Output: Biometric information received by the server during playback

[0764] Step 11:

[0765] Feedback and Optimization

[0766] The server compares biometric information before and after music playback and optimizes the music generation algorithm based on an AI model.

[0767] Input: Biometric information before and during playback

[0768] What it does: The server analyzes the feedback data and adds new patterns and trends to the AI ​​model.

[0769] Data computation: AI models continue to learn and optimize their algorithms.

[0770] Output: Optimized music generation algorithm

[0771] (Application example 2)

[0772] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0773] Modern society demands methods to optimize users' mental and physical states in real time. However, conventional music generation systems have been unable to fully utilize users' biometric and emotional data, making it difficult to provide personalized music experiences. Furthermore, these systems lack the ability to utilize feedback to optimize music generation algorithms, making it difficult to provide a sustained, high-quality music experience. Furthermore, they lack the ability to provide music according to users' schedules and monitor their reactions in real time, preventing personalized music experiences that would improve users' quality of life.

[0774] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting biometric information of a user, means for evaluating the user's current state based on the collected biometric information, means for generating optimal music according to the user's evaluated state, means for playing the generated music, means for monitoring the user's reaction during playback, means for optimizing a music generation algorithm based on the monitored user's reaction, means for analyzing the user's facial expression data to evaluate the user's emotions, means for adjusting the volume and tempo based on the user's emotions using an emotion engine, and means for the terminal to link with external data in real time and instantly provide the generated music. This makes it possible to provide music optimized for the user's mental and physical state by integrating the user's biometric information and emotional data, and to continuously improve accuracy through real-time reaction monitoring and feedback.

[0775] "User's biometric information" refers to data relating to the user's body, such as body temperature, pulse rate, and facial expression.

[0776] "Means for collection" refers to devices and methods for acquiring biometric information of users using various sensors and devices.

[0777] "Means of evaluation" refers to the process or device that analyzes and judges the user's current condition based on the collected biometric information.

[0778] "Generative means" refers to the process or technology that produces optimal music based on the analyzed state.

[0779] "Means for playing" refers to a device or method for letting a user listen to the generated music.

[0780] A "monitoring means" is a process or device that continuously monitors the user's reaction during playback.

[0781] "Optimization methods" are processes or techniques that improve music generation algorithms based on monitored user response data.

[0782] "Facial expression data" is information relating to the facial expression of the user.

[0783] The "analysis means" is a process or device that analyzes the collected data to evaluate the user's emotions and state.

[0784] The "emotion engine" is a technology that recognizes the user's emotions from facial expression data and other data, and reflects this in the music generation.

[0785] A "terminal" is a device that acquires, sends, and receives data, such as a smartphone or smartwatch.

[0786] "External data" is additional or contextual information obtained from the internet or other services.

[0787] "Means of collaboration" refers to mechanisms and technologies that allow terminals to share information with external data in real time.

[0788] "Relaxing music" is music created with the aim of reducing tension and stress in the user and making them feel relaxed.

[0789] This invention is a system that collects biometric information from a user in real time and generates optimal music using an emotion engine and a generative AI model. Specific embodiments of this system are described below.

[0790] First, the system is equipped with a means to collect biometric information such as the user's body temperature, pulse rate, and facial expression. Specific examples of devices include smartwatches and smartphones. The smartwatch uses a temperature sensor and a pulse sensor to measure body temperature and pulse rate. The smartphone uses a camera to capture the user's facial expression data, which is then analyzed by the emotion engine.

[0791] The collected biometric information and facial expression data are sent to a server in real time. The server integrates this information and uses an emotion engine to recognize the user's emotions. For example, the server analyzes the user's facial features from camera footage, and the emotion engine identifies the user's emotions from those expressions (e.g., joy, anger, sadness, or happiness). Furthermore, an AI model is used to evaluate the user's current state (e.g., stress level, fatigue level). Specifically, if the heart rate is higher than normal or if the emotion engine recognizes it as "stress," the AI ​​will determine that the user is feeling stressed.

[0792] The server then uses an AI composition model to generate optimal music based on the emotions and biometric information recognized by the emotion engine. For example, if it determines that the user is feeling stressed, it will generate music with a relaxing effect. The generated music is sent from the server to the device and played on the device. At this time, the volume and tempo of the music are also adjusted according to the emotions recognized by the emotion engine.

[0793] While music is playing, the device again monitors the user's biometric information and transmits the playback data to the server in real time. The server compares the biometric and emotional data before and during playback to evaluate the user's reaction. For example, if the user's pulse rate stabilizes after music playback and the emotion engine recognizes a "relaxed" feeling from the user's facial expression, the system will determine that the music was appropriate. Based on this feedback, the AI ​​composition model continues to learn and optimizes the music generation algorithm.

[0794] As a specific example, if a user has an important meeting in the morning, the smartwatch detects a high pulse rate, and the smartphone's emotion engine recognizes the user's tension. The server receives this data and evaluates the user as feeling stressed. The AI ​​composition model then generates relaxing music and plays it on the device. If the user's body temperature and pulse rate return to normal during playback and the emotion engine recognizes a relaxed facial expression, the system will determine that the music was appropriate and can reflect this in future playbacks.

[0795] The following is an example of a prompt sentence:

[0796] Generate optimal relaxing music for the user based on the following biometric and emotional data:

[0797] Body temperature: 37.5°C

[0798] Pulse rate: 85 bpm

[0799] Emotion: Tension (facial expression data)

[0800] This invention allows users to always receive the most appropriate music for their situation, thereby improving their quality of life. The system provides users with a highly personalized music experience by integrating emotion recognition and biometric information.

[0801] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0802] Step 1:

[0803] The devices (smartwatch and smartphone) collect the user's biometric information and facial expression data. Specifically, the smartwatch's temperature sensor and pulse sensor are used to measure body temperature and pulse, and the smartphone's camera is used to capture facial expression data. The input is sensor data and camera images, and the output is the collected biometric information and facial expression data.

[0804] Step 2:

[0805] The device transmits the collected biometric information and facial expression data to the server in real time. The input is the data collected in step 1, and the output is the data transmitted to the server.

[0806] Step 3:

[0807] The server integrates the received biometric information and facial expression data and uses an emotion engine to recognize the user's emotions. Specifically, it analyzes facial features from camera footage and identifies emotions based on the facial expressions. The input is the biometric information and facial expression data sent from the device, and the output is the user's emotional data.

[0808] Step 4:

[0809] The server uses an AI composition model to generate optimal music based on the user's emotional data and biometric information. For example, if the user is feeling stressed, it will generate music with a relaxing effect. The input is the user's emotional data and biometric information, and the output is the generated music data.

[0810] Step 5:

[0811] The server sends the generated music data to the terminal. The input is the generated music data, and the output is the music data sent to the terminal.

[0812] Step 6:

[0813] The device plays the received music data. Specifically, the smartphone plays the music and adjusts the volume and tempo according to the emotion recognized by the emotion engine. The input is the music data sent from the server, and the output is the music being played.

[0814] Step 7:

[0815] The device again monitors the user's biometric information while music is playing and transmits it to the server in real time. The input is the biometric information collected during playback, and the output is the data transmitted to the server.

[0816] Step 8:

[0817] The server compares the biometric and emotional data before and during playback to evaluate the user's reaction. The input is the data before and during playback, and the output is the evaluated reaction data.

[0818] Step 9:

[0819] The server optimizes the music generation algorithm based on the evaluated reaction data. Specifically, if the user's pulse stabilizes after playback and the emotion engine recognizes "relaxation," it trains the AI ​​composition model and improves the algorithm. The input is the evaluated reaction data, and the output is an optimized music generation algorithm.

[0820] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0821] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0822] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0823] [Third embodiment]

[0824] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0825] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0826] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0827] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0828] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0829] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0830] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0831] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0832] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0833] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0834] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0835] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0836] This invention is a system that collects a user's biometric information in real time, evaluates the user's condition based on that information, and then improvises and provides optimal music. Furthermore, it monitors the user's reactions while the music is playing, and optimizes the music generation algorithm based on that data.

[0837] The device (smartwatch or smartphone) collects biometric information such as the user's body temperature, pulse rate, and facial expression. For example, a smartwatch uses a temperature sensor and a pulse sensor to measure body temperature and pulse rate. A smartphone uses a camera to capture the user's facial expression data. This data is sent to a server in real time.

[0838] The server integrates the received biometric information and uses an AI model to assess the user's current state. For example, if the pulse rate is higher than normal, the AI ​​will assess that the user is feeling stressed. Based on the assessment results, the server uses an AI composition model to generate music that is optimal for the user's state. For example, it will create relaxing music for a user who is feeling stressed.

[0839] The generated music is sent from the server to the device, which then plays it. The playback volume is also optimized according to the user's condition. While the music is playing, the device again monitors the user's biometric information. By comparing the data before and after playback, the server evaluates the user's reaction and provides feedback to the AI ​​model. This information is used to optimize the music generation algorithm.

[0840] In addition, the system collects information about the user's schedule and provides music that is optimal for specific situations. For example, calming music can be provided when the user wants to relax. Using facial expression recognition technology, the camera can analyze the user's facial expressions to more accurately assess the user's mood and emotions.

[0841] As a concrete example of this system, suppose a user has an important meeting in the morning and their smartwatch detects a high pulse rate. The server receives this data and determines that the user is feeling stressed. The AI ​​composition model generates relaxing music and plays it on the device. If the user's body temperature and pulse rate return to normal during playback, the system determines that the music was appropriate. This feedback is reflected in future music generation, providing a more personalized music experience.

[0842] This allows users to always receive the most appropriate music for their situation, improving their quality of life. This system is highly effective for daily stress management, relaxation, and performance improvement.

[0843] The processing flow will be explained below.

[0844] Step 1:

[0845] Devices use sensors to collect biometric information about users: for example, a smartwatch measures a user's temperature and pulse, while a smartphone uses a camera to capture facial expression data.

[0846] Step 2:

[0847] The device transmits the collected biometric information to a server in real time, where body temperature, pulse rate, and facial expression data are integrated and uploaded to the server as a user profile.

[0848] Step 3:

[0849] The server uses an AI model to evaluate the user's current state based on the received biometric information. For example, if the heart rate is higher than normal, the AI ​​will determine that the user is feeling stressed.

[0850] Step 4:

[0851] The server uses an AI composition model to generate optimal music based on the user's assessed state: if the user is feeling stressed, music with a relaxing effect will be generated.

[0852] Step 5:

[0853] The server sends the generated music data to the terminal, which then imports the received music data and prepares for playback.

[0854] Step 6:

[0855] The device will play music at an optimal volume, and the volume settings will also be adjusted based on the user's state.

[0856] Step 7:

[0857] The device continues to monitor the user's biometric information while music is playing, and transmits the playback data to the server in real time.

[0858] Step 8:

[0859] The server compares the biometric data before and during playback to evaluate the user's reaction, for example, checking whether the pulse rate stabilizes after music playback.

[0860] Step 9:

[0861] The server will then provide feedback on the evaluation results and update the AI ​​model, which will optimize future music generation.

[0862] Step 10:

[0863] The device collects the user's schedule information and provides music tailored to specific situations, such as calming music during times when relaxation is needed.

[0864] Step 11:

[0865] The device uses the camera to analyze the user's facial expression data to assess their mood and emotions, which can then provide a more personalized music experience.

[0866] Through this series of steps, the system can provide the user with music that best suits their condition, improving their quality of life.

[0867] Example 1

[0868] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0869] Conventional music generation systems were unable to properly reflect the user's biological state or mood, making it difficult to provide optimal music for each individual user. Furthermore, they lacked the ability to monitor the user's reactions in real time during playback and adjust the system accordingly, making it difficult to maximize the effect of the music. Another issue was the inability to provide music based on the user's schedule information or specific situations.

[0870] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0871] In this invention, the server includes a means for transmitting a user's biometric information to the server in real time, a means for integrating the transmitted biometric information to evaluate the user's current state, and a means for generating optimal music based on the evaluation results. This makes it possible to accurately evaluate the user's state and provide optimal music based on that evaluation. Furthermore, by optimizing the music generation algorithm based on the monitored user's reactions, the effectiveness of the music can be maximized. Furthermore, music can be provided based on the user's schedule information or specific situations, allowing for a personalized music experience for each user.

[0872] "User's biometric information" is data that indicates the user's physical condition, such as body temperature, pulse rate, and facial expression.

[0873] "Means of collection" refers to devices or technologies such as sensors or cameras used to acquire biometric information.

[0874] "Transmitting means" refers to the communication technology or device used to transmit the collected data to the server.

[0875] "Means of integration" refers to techniques or methods for processing multiple pieces of data into one.

[0876] The "assessment means" refers to algorithms or models that analyze the integrated data to determine the user's condition.

[0877] The "generative means" refers to an AI composition model or software that creates music based on the evaluation results.

[0878] "Means for transmitting and playing" refers to the technology and devices that send the generated music data to a terminal and play it back as sound.

[0879] "Monitoring means" refers to technology or devices that monitor and record the user's biometric information again while music is being played.

[0880] "Optimizing means" refers to methods for improving the music generation algorithm based on the monitored data to provide better results.

[0881] "Schedule information" is data related to the user's plans and schedules.

[0882] A "situation" is a specific situation or environment, or a specific time or event that a user faces.

[0883] "Mood and emotions" refers to information that indicates the user's psychological state and feelings.

[0884] "Means of analysis" refers to the technology and algorithms used to analyze facial expression data and determine the user's mood and emotions.

[0885] This invention is a system that collects a user's biometric information in real time, evaluates the user's condition based on that information, and then improvises and provides optimal music. Furthermore, it monitors the user's reactions while the music is playing, and optimizes the music generation algorithm based on that data.

[0886] Collection and transmission methods

[0887] Devices (smartwatches and smartphones)

[0888] The device collects biometric information such as the user's body temperature, pulse rate, and facial expression. Specific hardware used includes a temperature sensor and pulse rate sensor in the smartwatch and a smartphone camera. This data is sent to a server in real time. Bluetooth and Wi-Fi are used as communication methods.

[0889] Examples:

[0890] The smartwatch measures body temperature with a temperature sensor and heart rate with a pulse sensor. This data is sent to a smartphone via Bluetooth, and then to a server via Wi-Fi. At the same time, the smartphone's camera captures facial expression data using biometric authentication technology, which is also sent to the server.

[0891] Evaluation methods

[0892] server

[0893] The server integrates the received biometric information and uses an AI model to assess the user's current state. Specifically, it analyzes the integrated data and assesses that the user is feeling stressed based on data such as "body temperature: 37.2°C, pulse rate: 110 BPM, facial expression: stressed facial expression."

[0894] Examples:

[0895] The server receives the data "Temperature: 37.2°C, Pulse: 110 BPM, Facial Expression: Stressed Facial Expression" and uses the AI ​​model to evaluate that "the user is feeling stressed." Based on this evaluation, the prompt sentence for the music to be generated is determined as follows: "User's biological information: Pulse 110 BPM, Body temperature 37.2°C, Facial expression: Stressed. Purpose of the music to be generated: Relaxation."

[0896] A means of generating and playing music

[0897] server

[0898] Based on the evaluation results, an AI composition model is used to generate music that is optimal for the user's state, such as music with a relaxing effect aimed at reducing stress.

[0899] Terminal

[0900] The generated music is sent from the server to the device, where it is played back, with the volume and tone optimized according to the user's state.

[0901] Examples:

[0902] The server generates slow-tempo music as "stress-reducing music" and sends it to the smartphone, where it plays the music and automatically adjusts the volume.

[0903] Monitoring and optimization measures

[0904] Terminal

[0905] While the music is playing, the device again monitors the user's body temperature, pulse rate, and facial expressions, and by comparing the data before and after playback, the effect of the music can be evaluated.

[0906] server

[0907] The server compares the data before and after playback and optimizes the music generation algorithm based on the monitored user reactions. This feedback updates the AI ​​model and is reflected in the next music generation.

[0908] Examples:

[0909] If the data before playback was "body temperature: 37.2°C, pulse rate: 110 BPM, facial expression: stressed expression," and the data after playback changed to "body temperature: 36.8°C, pulse rate: 75 BPM, facial expression: relaxed," the server would evaluate the music as effective and update the AI ​​model.

[0910] Collection and utilization of schedule information

[0911] Terminal

[0912] The system also collects information about the user's schedule and provides music that is optimal for a specific situation. For example, if the user has an important meeting in the morning, the system will provide relaxing music based on that information.

[0913] Examples:

[0914] If a user has entered in their schedule that they have an important meeting coming up in the morning, the server will receive that information and send relaxing music to the device to play to help ease tension.

[0915] This step-by-step approach allows us to provide a music experience that is optimized for each user's condition, improving their quality of life.

[0916] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0917] Step 1:

[0918] Biometric data collection

[0919] The device (smartwatch or smartphone) uses sensors and cameras to collect biometric information such as the user's body temperature, pulse rate, and facial expression. Specifically, the smartwatch measures body temperature with a temperature sensor and heart rate with a pulse sensor. The smartphone camera also takes a picture of the user's face and captures facial expression data.

[0920] Input: User's body temperature, pulse rate, facial expression

[0921] Output: Collected biometric information (body temperature, pulse, facial expression)

[0922] Step 2:

[0923] Sending data to the server

[0924] The device transmits the collected biometric information to a server in real time using Bluetooth or Wi-Fi. For example, data from a smartwatch is transmitted to a smartphone via Bluetooth, and then to the server via Wi-Fi.

[0925] Input: Collected biometric information (body temperature, pulse, facial expression)

[0926] Output: Biometric information sent to the server

[0927] Step 3:

[0928] Data integration and condition assessment

[0929] The server integrates the received biometric information and uses an AI model to evaluate the user's current condition. For example, if data such as "Temperature: 37.2°C, Pulse: 110 BPM, Facial Expression: Stressed Facial Expression" is received, the AI ​​model will analyze this data and conclude that "the user is feeling stressed."

[0930] Input: Biometric information sent to the server

[0931] Output: User's current status evaluation result

[0932] Step 4:

[0933] Music Generation

[0934] Based on the evaluation results, the server uses an AI composition model to generate music that best suits the user's state of mind. For example, it generates music with a relaxing effect for a user who is feeling stressed.

[0935] Input: User's current status evaluation result

[0936] Output: Generated music files

[0937] Step 5:

[0938] Sending and playing music

[0939] The server sends the generated music to the device, which then plays it. The volume and tone of the music are automatically adjusted according to the user's state. Specifically, the server sends music data to the smartphone, which then plays it through its speaker.

[0940] Input: Generated music files

[0941] Output: Music being played

[0942] Step 6:

[0943] Monitoring while music is playing

[0944] While music is playing, the device again monitors the user's body temperature, pulse rate, and facial expressions. This allows data to be collected before and after playback and records any changes. For example, while music is playing, the smartwatch again measures the user's body temperature and pulse rate, and the smartphone captures facial expressions.

[0945] Input: Monitoring results of biological information while music is playing

[0946] Output: Monitored vital signs

[0947] Step 7:

[0948] Reaction evaluation and algorithm optimization

[0949] The server compares the data before and after playback and evaluates the user's reaction. Based on the evaluation results, it optimizes the music generation algorithm and updates the AI ​​model. This feedback is used for the next music generation.

[0950] Input: Monitored biometric data (before and after playback)

[0951] Output: Optimized music generation algorithm

[0952] (Application example 1)

[0953] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0954] Currently, stress management and relaxation while in a vehicle primarily depend on manually selected music and entertainment information. However, manual selection makes it difficult to achieve an appropriate relaxation effect that matches the user's real-time state. Furthermore, conventional music playback systems do not optimize the system to reflect the user's real-time biometric information, resulting in a lack of immediate effects on stress reduction and mood improvement. The present invention aims to solve these problems and provide a system that highly automates relaxation and stress management in a vehicle.

[0955] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0956] In this invention, the server includes means for collecting biometric information of a user, means for evaluating the user's current state based on the collected biometric information, means for generating optimal music according to the evaluated state of the user, means for playing the generated music, means for monitoring the user's reaction during playback, means for optimizing a music generation algorithm based on the monitored user's reaction, and means for linking with the on-board system of the autonomous vehicle, thereby enabling the provision of optimal relaxation music in real time and stress management for the user.

[0957] "User's biometric information" refers to data related to the user's health and emotional state that can be obtained in real time, such as body temperature, pulse rate, and facial expression.

[0958] "User's current state" refers to the user's mental or physical state, such as stress level or degree of relaxation, assessed based on collected biometric information.

[0959] "Optimal music" is music created based on the user's biometric information and current condition to maximize the desired effect, such as relaxation or improved concentration.

[0960] "Generated music" is music that is improvised using artificial intelligence models or the like, depending on the user's current assessed state.

[0961] "Means for playing" refers to a device or system that outputs the generated music so that the user can listen to it.

[0962] "Monitoring means" refers to devices or software that recollect biometric information such as the user's body temperature, pulse rate, and facial expressions while music is being played, and track changes.

[0963] A "music generation algorithm" is a computer program or procedure for generating optimal music based on a user's biometric information and condition.

[0964] An "autonomous vehicle" is a vehicle that operates automatically and can travel safely without user intervention.

[0965] "In-vehicle systems" is a general term for information processing devices, communication devices, entertainment systems, etc. installed in autonomous vehicles.

[0966] This invention is a system that collects a user's biometric information in real time and generates and provides music based on that information. Its purpose is to automate relaxation and stress management for users in self-driving vehicles. Specific embodiments of the invention are described below.

[0967] composition

[0968] The system mainly consists of the following components:

[0969] 1. Biometric information collection device: Collects biometric information such as the user's body temperature, pulse rate, and facial expression. Specifically, sensors installed in smartwatches and smartphones are used.

[0970] 2. Biometric information processing server (hereinafter referred to as the server): Integrates collected biometric information and evaluates the user's current condition. An AI model is used for the evaluation.

[0971] 3. Music Generation Server: Generates optimal music based on the user's state evaluation results. AI composition model is used.

[0972] 4. In-vehicle system for autonomous vehicles: Plays generated music and continuously monitors the user's biometric information.

[0973] Processing flow

[0974] First, a smartwatch or smartphone collects the user's biometric information. The collected data is sent to a server in real time. The server then integrates this data and uses an AI model to evaluate the user's current state. For example, if the user's pulse rate is high, it is determined that the user is feeling stressed.

[0975] Based on the evaluation results, the music generation server generates appropriate music—for example, classical or ambient music for a user who is feeling stressed—and the generated music track is sent to the autonomous vehicle's in-vehicle system for playback inside the vehicle.

[0976] While music is playing, the smartwatch or smartphone continues to collect biometric information and transmits it to the server. By comparing the biometric information before and after playback, the server evaluates the user's reaction and uses this information as feedback to optimize the music generation algorithm.

[0977] Explanation and examples

[0978] As a concrete example, consider a situation where a user experiences a high pulse rate during a long drive. The server evaluates this as a stressful state and generates a relaxing classical music track. This track is then played on the in-car system. If the user's pulse rate returns to normal after playback, the system considers the music track appropriate and reflects this in future music generation.

[0979] Example prompt for a generative AI model:

[0980] "Generate optimal music based on the user's current biometric information. Evaluate their condition based on the information below and select a music track of an appropriate genre.

[0981] Heart rate: 110, temperature: 37.5, facial expression: neutral."

[0982] This makes it possible to provide optimal relaxation music in real time and manage the user's stress, improving the user's comfort in autonomous vehicles and providing a stress-free travel experience.

[0983] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0984] Step 1:

[0985] Smartwatches and smartphones collect users' biometric information.

[0986] Specifically, the smartwatch's pulse and temperature sensors and the smartphone's camera are used to measure the user's body temperature, pulse, and facial expression.

[0987] Input: Real-time biometric information of the user (body temperature, pulse rate, facial expression).

[0988] Output: Collected biometric data.

[0989] Step 2:

[0990] The terminal transmits the collected biometric information to the server.

[0991] The collected data is transferred in real time to a server via the Internet.

[0992] Input: Collected biometric data.

[0993] Output: Biometric data sent to the server.

[0994] Step 3:

[0995] The server integrates the received biometric information to assess the user's current condition.

[0996] The server uses an AI model to assess, for example, a high pulse rate as a state of stress, and a low pulse rate as a state of relaxation.

[0997] Input: Biometric data sent to the server.

[0998] Output: User's current state assessment (e.g., stressed, relaxed, neutral).

[0999] Step 4:

[1000] The server generates optimal music based on the evaluation results.

[1001] Using an AI composition model, it improvises relaxing music based on the user's condition.

[1002] Input: The user's current condition assessment result.

[1003] Output: The generated music track data.

[1004] Step 5:

[1005] The server transmits the generated music track data to the on-board system of the autonomous vehicle.

[1006] The in-car system receives this music data and prepares it for playback.

[1007] Input: The generated music track data.

[1008] Output: Music track data sent to the in-car system.

[1009] Step 6:

[1010] The in-car system plays music.

[1011] Users can enjoy a relaxing effect by listening to the music that is played.

[1012] Input: Music track data sent to the in-car system.

[1013] Output: Music played in the car.

[1014] Step 7:

[1015] While music is playing, the smartwatch or smartphone continues to collect biometric information.

[1016] The biometric information is sent to the server again.

[1017] Input: Real-time biometric information of the user (data while music is playing).

[1018] Output: Continuous biometric data sent to the server.

[1019] Step 8:

[1020] The server compares biometric information before and after music playback to evaluate the user's reaction.

[1021] Based on this evaluation, feedback is provided to the AI ​​model to optimize the music generation algorithm.

[1022] Input: Biometric data before and after playback.

[1023] Output: User response evaluation results and optimized music generation algorithm.

[1024] The above is the processing flow of the system of this invention, which makes it possible to provide optimal relaxation music in real time and manage the user's stress.

[1025] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1026] This invention combines an emotion engine with a system that collects a user's biometric information in real time, evaluates the user's condition based on that information, and improvises and provides optimal music. Furthermore, by monitoring the user's reactions during music playback and optimizing the music generation algorithm based on that data, a more precise and personalized music experience is provided.

[1027] The device (smartwatch or smartphone) collects biometric information such as the user's body temperature, pulse rate, and facial expression. For example, a smartwatch uses a temperature sensor and a pulse sensor to measure body temperature and pulse rate. A smartphone uses a camera to capture the user's facial expression data and analyzes it using an emotion engine. This data is sent to a server in real time.

[1028] The server integrates the received biometric information and facial expression data and uses an emotion engine to recognize the user's emotions. For example, it analyzes the user's facial features from camera footage, and the emotion engine identifies the user's emotions from those expressions (e.g., joy, anger, sadness, and happiness). It then uses an AI model to evaluate the user's current state (e.g., stress level, fatigue level). For example, if the heart rate is higher than normal or if the emotion engine recognizes it as "stressed," the AI ​​will determine that the user is feeling stressed.

[1029] Based on the emotions and biometric information recognized by the emotion engine, the server uses an AI composition model to generate optimal music. For example, if you are feeling stressed, music with a relaxing effect will be generated. The generated music is sent from the server to the device, which then plays the music. At this time, the volume and tempo of the music are also adjusted according to the emotions recognized by the emotion engine.

[1030] While music is playing, the device again monitors the user's biometric information and transmits the playback data to the server in real time. The server compares the biometric information and emotional data before and during playback to evaluate the user's reaction. For example, if the pulse rate stabilizes after music playback and the emotion engine recognizes a "relaxed" feeling from the user's facial expression, the system will determine that the music was appropriate. Based on this feedback, the AI ​​model continues to learn and optimizes the music generation algorithm.

[1031] Furthermore, the device can obtain the user's schedule information and provide music tailored to specific situations, such as calming music during times when relaxation is needed. Furthermore, by using facial expression recognition technology and an emotion engine, the camera can analyze the user's facial expressions and more accurately assess the user's mood and emotions.

[1032] This system can provide users with an advanced music experience to improve their quality of life, including daily stress management, relaxation, and performance improvement. As a specific example, if a user has an important meeting in the morning, the smartwatch detects a high pulse rate and the emotion engine recognizes that the user's face is tense. The server receives this data and evaluates the user as feeling stressed. The AI ​​composition model generates music with a relaxing effect and plays it on the device. If the user's body temperature and pulse rate return to normal during playback and the emotion engine recognizes a relaxed facial expression, the system will evaluate the music as appropriate and reflect this in future music generation.

[1033] This allows users to always receive the most appropriate music for their situation, improving their quality of life. By integrating emotion recognition and biometric information, this system achieves a highly personalized music experience that goes beyond conventional music playback systems.

[1034] The processing flow will be explained below.

[1035] Step 1:

[1036] Devices (smartwatches and smartphones) collect biometric information from users. For example, smartwatches measure body temperature and pulse, and smartphones use cameras to capture facial expression data.

[1037] Step 2:

[1038] The device transmits the collected biometric information and facial expression data to a server in real time. Body temperature, pulse rate, and facial expression data are integrated and uploaded to the server as a user profile.

[1039] Step 3:

[1040] The server uses an emotion engine to recognize the user's emotions based on the received biometric information and facial expression data. For example, it analyzes the user's facial features from camera footage and identifies emotions from their expressions (e.g., joy, anger, sadness, or happiness).

[1041] Step 4:

[1042] The server uses an AI model based on the emotions and biometric information recognized through the emotion engine to evaluate the user's current state (e.g., stress level, fatigue level). For example, if the heart rate is higher than normal and the emotion engine recognizes this as "stress," it will determine that the user is feeling stressed.

[1043] Step 5:

[1044] The server uses an AI composition model to generate optimal music based on the user's assessed state. For example, it generates relaxing music for a user who is feeling stressed.

[1045] Step 6:

[1046] The server sends the generated music data to the terminal, which then imports the received music data and prepares to play the music.

[1047] Step 7:

[1048] The device plays music at the optimal volume. The volume settings are also adjusted based on the user's state. The emotion engine automatically adjusts the tempo and volume of the music based on the emotions it recognizes.

[1049] Step 8:

[1050] The device continues to monitor the user's biometric information while playing music, and the data is sent to the server in real time.

[1051] Step 9:

[1052] The server compares the user's biometric and emotional data before and during playback to evaluate their reaction. For example, if the user's pulse rate stabilizes after music playback and the emotion engine recognizes a relaxed facial expression, the system will determine that the music was appropriate.

[1053] Step 10:

[1054] The server will then provide feedback on the evaluation results and update the AI ​​model, which will optimize the music generation process from the next time onwards.

[1055] Step 11:

[1056] The device collects the user's schedule information and provides music tailored to specific situations, such as calming music during times when relaxation is needed.

[1057] Step 12:

[1058] The device uses the camera to analyze the user's facial expression data to assess their mood and emotions, providing a more personalized music experience.

[1059] Through this series of processes, the system can provide music that is best suited to the user's condition, improving the user's quality of life.

[1060] Example 2

[1061] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1062] Conventional music playback systems have had difficulty collecting a user's emotions and biometric information in real time and providing optimal music based on that information. In particular, there is a need to accurately assess a user's current state and emotions, and generate and provide personalized music based on that assessment. Furthermore, there is a need to provide a more precise and personalized music experience by monitoring the user's reactions during music playback in real time and incorporating the results into the music generation algorithm.

[1063] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1064] In this invention, the server includes means for collecting biometric information of a user, means for evaluating the user's current state based on the collected biometric information, means for generating optimal music according to the user's evaluated state, means for playing the generated music, means for monitoring the user's reaction during playback, means for optimizing a music generation algorithm based on the monitored user's reaction, means for integrating the biometric information and facial expression data and recognizing the user's emotion using an emotion engine, and means for transmitting the biometric information during music playback to the server in real time. This makes it possible to generate optimal personalized music based on the user's biometric information and emotion data and provide it in real time.

[1065] "Biometric information" refers to data related to individual vital activities, such as a user's body temperature, pulse rate, and respiratory rate.

[1066] "Facial expression data" is visual information based on a user's facial features and muscle movements, and is data used for emotion recognition.

[1067] An "emotion engine" is an algorithm or model that analyzes input facial expressions and biometric information to recognize the user's emotions and psychological state.

[1068] An "AI composition model" is an artificial intelligence algorithm used to generate personalized music based on a user's emotions and state.

[1069] "Monitoring" is the act of continuously observing the state or changes of an object and collecting data.

[1070] "Optimization" refers to adjusting parameters and algorithms to maximize the performance of a system or process according to specific objectives and conditions.

[1071] "Condition assessment" refers to analyzing and judging the user's current health and psychological state based on collected biometric information and facial expression data.

[1072] "Music generation" is the process of using specific algorithms and models to create music that responds to the user's state and emotions.

[1073] "Real-time" means that processing and response are nearly instantaneous, with almost no delay.

[1074] A "server" is a computer system that processes and manages data within a network and provides services in response to requests from terminals.

[1075] This invention is an advanced system that collects biometric and facial expression data from users, evaluates their emotions and state based on that data, and generates and delivers optimal music. Furthermore, it monitors users' reactions during music playback and optimizes the music generation algorithm based on the results, providing a precise and personalized music experience.

[1076] 1. Collection of biometric information

[1077] The device (smartwatch or smartphone) collects biometric information such as the user's body temperature, pulse rate, and facial expression. The smartwatch uses a temperature sensor and pulse rate sensor to measure the user's body temperature and pulse rate. The smartphone uses a camera to capture the user's facial expression data and analyzes it using an emotion engine.

[1078] Examples:

[1079] The smartwatch measures body temperature and pulse every second and transmits the data to a smartphone in real time.

[1080] The smartphone camera captures facial expressions, and the emotion engine analyzes the facial data to recognize the emotions of joy, anger, sadness, and happiness.

[1081] 2. Data submission and analysis

[1082] The biometric information collected by the device and analyzed facial expression data are sent to a server in real time. The server integrates this data and uses an emotion engine to recognize emotions. It also uses an AI model to assess the user's current state (e.g., stress level, fatigue level).

[1083] Specific server behavior:

[1084] The server stores the received biometric information and facial expression data in a database and passes the data to the analysis module.

[1085] The emotion engine checks the facial expression analysis results and identifies states such as "stressed" or "relaxed."

[1086] 3. Music Generation and Playback

[1087] The emotion engine recognizes the user's emotions, and the AI ​​composition model generates optimal music based on biometric information. The generated music is sent from the server to the device, where it is played in real time.

[1088] Examples:

[1089] If the user is assessed as feeling stressed, the server's AI composition model will generate relaxing music.

[1090] The generated music file is sent to the device, which then plays the music through its speakers.

[1091] 4. Monitoring and optimization during music playback

[1092] While music is playing, the device again monitors the user's biometric information and transmits it to the server in real time. The server compares the biometric information before and after playback and evaluates the user's reaction. Based on this feedback data, the AI ​​model continues to learn and optimizes the music generation algorithm.

[1093] Specific server behavior:

[1094] Biometric information before and during playback is compared to evaluate whether the user's pulse rate and facial expression are relaxed.

[1095] The AI ​​model is updated based on the feedback data, optimizing the next music generation parameters.

[1096] The system analyzes users' biometric and emotional data in real time and provides personalized music experiences, improving the quality of their lives.

[1097] Example prompt sentence:

[1098] "We want to design a system that collects biometric information from users and evaluates their emotions and state based on that data. We will then use an emotion engine and an AI composition model to generate optimal music and deliver it to them in real time."

[1099] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1100] Step 1:

[1101] Biometric data collection

[1102] The device (smartwatch) measures the user's body temperature and pulse rate.

[1103] Input: User's body temperature and pulse information

[1104] How it works: The smartwatch's sensors detect body temperature and pulse every second and collect this data.

[1105] Output: Temperature and pulse data set

[1106] Step 2:

[1107] Capturing facial expression data

[1108] The device (smartphone) uses a camera to capture the user's face and send it to the emotion engine.

[1109] Input: Video of the user's face

[1110] Specific operation: The smartphone camera captures facial expressions every second and passes the image data to the emotion engine.

[1111] Output: Analyzed facial expression data

[1112] Step 3:

[1113] Transmission of biometric and facial expression data

[1114] The device sends the collected biometric information and analyzed facial expression data to the server.

[1115] Input: Body temperature, pulse rate dataset and facial expression data

[1116] Specific operation: The terminal divides this data into packets and sends them to a server via the Internet.

[1117] Output: Biometric information and facial expression data received by the server

[1118] Step 4:

[1119] Data integration and analysis

[1120] The server integrates the received biometric information and facial expression data and analyzes them using an emotion engine.

[1121] Input: Biometric information and facial expression data received by the server

[1122] Specific operation: The server stores these data in a database and passes them to the analysis module.

[1123] Data calculation: The emotion engine on the server checks the facial expression analysis results and identifies the emotion.

[1124] Output: Identified emotions and integrated biometric information

[1125] Step 5:

[1126] Condition assessment

[1127] The server uses an AI model to assess the user's condition based on the identified emotions and biometric information.

[1128] Input: identified emotions and integrated biometric information

[1129] Specific operation: The AI ​​model analyzes biometric information and evaluates the user's stress level and fatigue level.

[1130] Data calculation: AI model calculates stress and fatigue levels.

[1131] Output: Evaluated user state (e.g., stress level, fatigue level)

[1132] Step 6:

[1133] Music Generation

[1134] The server uses an AI composition model to generate optimal music based on the user's evaluated state.

[1135] Input: Evaluated user status

[1136] How it works: The AI ​​composition model analyzes the input data and determines musical parameters (e.g., tempo, melody).

[1137] Data calculation: AI composition model generates relaxing music.

[1138] Output: Generated music data

[1139] Step 7:

[1140] Sending music data

[1141] The server transmits the generated music data to the terminal.

[1142] Input: Generated music data

[1143] Specific operation: The server divides the music data into packets and sends them to the terminal.

[1144] Output: Music data arriving on the device

[1145] Step 8:

[1146] Playing music

[1147] The terminal plays the received music data.

[1148] Input: Music data arriving on the device

[1149] What it does: The device decodes the music and plays it through the speaker, adjusting the volume and tempo in real time as needed.

[1150] Output: Music played to the user

[1151] Step 9:

[1152] Monitoring biological information during regeneration

[1153] The device monitors the user's biometric information even while music is playing.

[1154] Input: Biometric information during music playback

[1155] How it works: The smartwatch continuously measures your body temperature and pulse rate while music is playing.

[1156] Output: Body temperature and pulse data during playback

[1157] Step 10:

[1158] Sending monitoring results

[1159] The device transmits the biometric information being played back to the server in real time.

[1160] Input: Body temperature and pulse data during playback

[1161] Specific operation: The device divides the biometric information into packets and sends them to the server.

[1162] Output: Biometric information received by the server during playback

[1163] Step 11:

[1164] Feedback and Optimization

[1165] The server compares biometric information before and after music playback and optimizes the music generation algorithm based on an AI model.

[1166] Input: Biometric information before and during playback

[1167] What it does: The server analyzes the feedback data and adds new patterns and trends to the AI ​​model.

[1168] Data computation: AI models continue to learn and optimize their algorithms.

[1169] Output: Optimized music generation algorithm

[1170] (Application example 2)

[1171] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1172] Modern society demands methods to optimize users' mental and physical states in real time. However, conventional music generation systems have been unable to fully utilize users' biometric and emotional data, making it difficult to provide personalized music experiences. Furthermore, these systems lack the ability to utilize feedback to optimize music generation algorithms, making it difficult to provide a sustained, high-quality music experience. Furthermore, they lack the ability to provide music according to users' schedules and monitor their reactions in real time, preventing personalized music experiences that would improve users' quality of life.

[1173] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting biometric information of a user, means for evaluating the user's current state based on the collected biometric information, means for generating optimal music according to the user's evaluated state, means for playing the generated music, means for monitoring the user's reaction during playback, means for optimizing a music generation algorithm based on the monitored user's reaction, means for analyzing the user's facial expression data to evaluate the user's emotions, means for adjusting the volume and tempo based on the user's emotions using an emotion engine, and means for the terminal to link with external data in real time and instantly provide the generated music. This makes it possible to provide music optimized for the user's mental and physical state by integrating the user's biometric information and emotional data, and to continuously improve accuracy through real-time reaction monitoring and feedback.

[1174] "User's biometric information" refers to data relating to the user's body, such as body temperature, pulse rate, and facial expression.

[1175] "Means for collection" refers to devices and methods for acquiring biometric information of users using various sensors and devices.

[1176] "Means of evaluation" refers to the process or device that analyzes and judges the user's current condition based on the collected biometric information.

[1177] "Generative means" refers to the process or technology that produces optimal music based on the analyzed state.

[1178] "Means for playing" refers to a device or method for letting a user listen to the generated music.

[1179] A "monitoring means" is a process or device that continuously monitors the user's reaction during playback.

[1180] "Optimization methods" are processes or techniques that improve music generation algorithms based on monitored user response data.

[1181] "Facial expression data" is information relating to the facial expression of the user.

[1182] The "analysis means" is a process or device that analyzes the collected data to evaluate the user's emotions and state.

[1183] The "emotion engine" is a technology that recognizes the user's emotions from facial expression data and other data, and reflects this in the music generation.

[1184] A "terminal" is a device that acquires, sends, and receives data, such as a smartphone or smartwatch.

[1185] "External data" is additional or contextual information obtained from the internet or other services.

[1186] "Means of collaboration" refers to mechanisms and technologies that allow terminals to share information with external data in real time.

[1187] "Relaxing music" is music created with the aim of reducing tension and stress in the user and making them feel relaxed.

[1188] This invention is a system that collects biometric information from a user in real time and generates optimal music using an emotion engine and a generative AI model. Specific embodiments of this system are described below.

[1189] First, the system is equipped with a means to collect biometric information such as the user's body temperature, pulse rate, and facial expression. Specific examples of devices include smartwatches and smartphones. The smartwatch uses a temperature sensor and a pulse sensor to measure body temperature and pulse rate. The smartphone uses a camera to capture the user's facial expression data, which is then analyzed by the emotion engine.

[1190] The collected biometric information and facial expression data are sent to a server in real time. The server integrates this information and uses an emotion engine to recognize the user's emotions. For example, the server analyzes the user's facial features from camera footage, and the emotion engine identifies the user's emotions from those expressions (e.g., joy, anger, sadness, or happiness). Furthermore, an AI model is used to evaluate the user's current state (e.g., stress level, fatigue level). Specifically, if the heart rate is higher than normal or if the emotion engine recognizes it as "stress," the AI ​​will determine that the user is feeling stressed.

[1191] The server then uses an AI composition model to generate optimal music based on the emotions and biometric information recognized by the emotion engine. For example, if it determines that the user is feeling stressed, it will generate music with a relaxing effect. The generated music is sent from the server to the device and played on the device. At this time, the volume and tempo of the music are also adjusted according to the emotions recognized by the emotion engine.

[1192] While music is playing, the device again monitors the user's biometric information and transmits the playback data to the server in real time. The server compares the biometric and emotional data before and during playback to evaluate the user's reaction. For example, if the user's pulse rate stabilizes after music playback and the emotion engine recognizes a "relaxed" feeling from the user's facial expression, the system will determine that the music was appropriate. Based on this feedback, the AI ​​composition model continues to learn and optimizes the music generation algorithm.

[1193] As a specific example, if a user has an important meeting in the morning, the smartwatch detects a high pulse rate, and the smartphone's emotion engine recognizes the user's tension. The server receives this data and evaluates the user as feeling stressed. The AI ​​composition model then generates relaxing music and plays it on the device. If the user's body temperature and pulse rate return to normal during playback and the emotion engine recognizes a relaxed facial expression, the system will determine that the music was appropriate and can reflect this in future playbacks.

[1194] The following is an example of a prompt sentence:

[1195] Generate optimal relaxing music for the user based on the following biometric and emotional data:

[1196] Body temperature: 37.5°C

[1197] Pulse rate: 85 bpm

[1198] Emotion: Tension (facial expression data)

[1199] This invention allows users to always receive the most appropriate music for their situation, thereby improving their quality of life. The system provides users with a highly personalized music experience by integrating emotion recognition and biometric information.

[1200] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1201] Step 1:

[1202] The devices (smartwatch and smartphone) collect the user's biometric information and facial expression data. Specifically, the smartwatch's temperature sensor and pulse sensor are used to measure body temperature and pulse, and the smartphone's camera is used to capture facial expression data. The input is sensor data and camera images, and the output is the collected biometric information and facial expression data.

[1203] Step 2:

[1204] The device transmits the collected biometric information and facial expression data to the server in real time. The input is the data collected in step 1, and the output is the data transmitted to the server.

[1205] Step 3:

[1206] The server integrates the received biometric information and facial expression data and uses an emotion engine to recognize the user's emotions. Specifically, it analyzes facial features from camera footage and identifies emotions based on the facial expressions. The input is the biometric information and facial expression data sent from the device, and the output is the user's emotional data.

[1207] Step 4:

[1208] The server uses an AI composition model to generate optimal music based on the user's emotional data and biometric information. For example, if the user is feeling stressed, it will generate music with a relaxing effect. The input is the user's emotional data and biometric information, and the output is the generated music data.

[1209] Step 5:

[1210] The server sends the generated music data to the terminal. The input is the generated music data, and the output is the music data sent to the terminal.

[1211] Step 6:

[1212] The device plays the received music data. Specifically, the smartphone plays the music and adjusts the volume and tempo according to the emotion recognized by the emotion engine. The input is the music data sent from the server, and the output is the music being played.

[1213] Step 7:

[1214] The device again monitors the user's biometric information while music is playing and transmits it to the server in real time. The input is the biometric information collected during playback, and the output is the data transmitted to the server.

[1215] Step 8:

[1216] The server compares the biometric and emotional data before and during playback to evaluate the user's reaction. The input is the data before and during playback, and the output is the evaluated reaction data.

[1217] Step 9:

[1218] The server optimizes the music generation algorithm based on the evaluated reaction data. Specifically, if the user's pulse stabilizes after playback and the emotion engine recognizes "relaxation," it trains the AI ​​composition model and improves the algorithm. The input is the evaluated reaction data, and the output is an optimized music generation algorithm.

[1219] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1220] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1221] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1222] [Fourth embodiment]

[1223] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1224] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1225] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1226] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1227] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1228] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1229] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1230] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1231] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1232] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1233] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1234] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1235] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1236] This invention is a system that collects a user's biometric information in real time, evaluates the user's condition based on that information, and then improvises and provides optimal music. Furthermore, it monitors the user's reactions while the music is playing, and optimizes the music generation algorithm based on that data.

[1237] The device (smartwatch or smartphone) collects biometric information such as the user's body temperature, pulse rate, and facial expression. For example, a smartwatch uses a temperature sensor and a pulse sensor to measure body temperature and pulse rate. A smartphone uses a camera to capture the user's facial expression data. This data is sent to a server in real time.

[1238] The server integrates the received biometric information and uses an AI model to assess the user's current state. For example, if the pulse rate is higher than normal, the AI ​​will assess that the user is feeling stressed. Based on the assessment results, the server uses an AI composition model to generate music that is optimal for the user's state. For example, it will create relaxing music for a user who is feeling stressed.

[1239] The generated music is sent from the server to the device, which then plays it. The playback volume is also optimized according to the user's condition. While the music is playing, the device again monitors the user's biometric information. By comparing the data before and after playback, the server evaluates the user's reaction and provides feedback to the AI ​​model. This information is used to optimize the music generation algorithm.

[1240] In addition, the system collects information about the user's schedule and provides music that is optimal for specific situations. For example, calming music can be provided when the user wants to relax. Using facial expression recognition technology, the camera can analyze the user's facial expressions to more accurately assess the user's mood and emotions.

[1241] As a concrete example of this system, suppose a user has an important meeting in the morning and their smartwatch detects a high pulse rate. The server receives this data and determines that the user is feeling stressed. The AI ​​composition model generates relaxing music and plays it on the device. If the user's body temperature and pulse rate return to normal during playback, the system determines that the music was appropriate. This feedback is reflected in future music generation, providing a more personalized music experience.

[1242] This allows users to always receive the most appropriate music for their situation, improving their quality of life. This system is highly effective for daily stress management, relaxation, and performance improvement.

[1243] The processing flow will be explained below.

[1244] Step 1:

[1245] Devices use sensors to collect biometric information about users: for example, a smartwatch measures a user's temperature and pulse, while a smartphone uses a camera to capture facial expression data.

[1246] Step 2:

[1247] The device transmits the collected biometric information to a server in real time, where body temperature, pulse rate, and facial expression data are integrated and uploaded to the server as a user profile.

[1248] Step 3:

[1249] The server uses an AI model to evaluate the user's current state based on the received biometric information. For example, if the heart rate is higher than normal, the AI ​​will determine that the user is feeling stressed.

[1250] Step 4:

[1251] The server uses an AI composition model to generate optimal music based on the user's assessed state: if the user is feeling stressed, music with a relaxing effect will be generated.

[1252] Step 5:

[1253] The server sends the generated music data to the terminal, which then imports the received music data and prepares for playback.

[1254] Step 6:

[1255] The device will play music at an optimal volume, and the volume settings will also be adjusted based on the user's state.

[1256] Step 7:

[1257] The device continues to monitor the user's biometric information while music is playing, and transmits the playback data to the server in real time.

[1258] Step 8:

[1259] The server compares the biometric data before and during playback to evaluate the user's reaction, for example, checking whether the pulse rate stabilizes after music playback.

[1260] Step 9:

[1261] The server will then provide feedback on the evaluation results and update the AI ​​model, which will optimize future music generation.

[1262] Step 10:

[1263] The device collects the user's schedule information and provides music tailored to specific situations, such as calming music during times when relaxation is needed.

[1264] Step 11:

[1265] The device uses the camera to analyze the user's facial expression data to assess their mood and emotions, which can then provide a more personalized music experience.

[1266] Through this series of steps, the system can provide the user with music that best suits their condition, improving their quality of life.

[1267] Example 1

[1268] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1269] Conventional music generation systems were unable to properly reflect the user's biological state or mood, making it difficult to provide optimal music for each individual user. Furthermore, they lacked the ability to monitor the user's reactions in real time during playback and adjust the system accordingly, making it difficult to maximize the effect of the music. Another issue was the inability to provide music based on the user's schedule information or specific situations.

[1270] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1271] In this invention, the server includes a means for transmitting a user's biometric information to the server in real time, a means for integrating the transmitted biometric information to evaluate the user's current state, and a means for generating optimal music based on the evaluation results. This makes it possible to accurately evaluate the user's state and provide optimal music based on that evaluation. Furthermore, by optimizing the music generation algorithm based on the monitored user's reactions, the effectiveness of the music can be maximized. Furthermore, music can be provided based on the user's schedule information or specific situations, allowing for a personalized music experience for each user.

[1272] "User's biometric information" is data that indicates the user's physical condition, such as body temperature, pulse rate, and facial expression.

[1273] "Means of collection" refers to devices or technologies such as sensors or cameras used to acquire biometric information.

[1274] "Transmitting means" refers to the communication technology or device used to transmit the collected data to the server.

[1275] "Means of integration" refers to techniques or methods for processing multiple pieces of data into one.

[1276] The "assessment means" refers to algorithms or models that analyze the integrated data to determine the user's condition.

[1277] The "generative means" refers to an AI composition model or software that creates music based on the evaluation results.

[1278] "Means for transmitting and playing" refers to the technology and devices that send the generated music data to a terminal and play it back as sound.

[1279] "Monitoring means" refers to technology or devices that monitor and record the user's biometric information again while music is being played.

[1280] "Optimizing means" refers to methods for improving the music generation algorithm based on the monitored data to provide better results.

[1281] "Schedule information" is data related to the user's plans and schedules.

[1282] A "situation" is a specific situation or environment, or a specific time or event that a user faces.

[1283] "Mood and emotions" refers to information that indicates the user's psychological state and feelings.

[1284] "Means of analysis" refers to the technology and algorithms used to analyze facial expression data and determine the user's mood and emotions.

[1285] This invention is a system that collects a user's biometric information in real time, evaluates the user's condition based on that information, and then improvises and provides optimal music. Furthermore, it monitors the user's reactions while the music is playing, and optimizes the music generation algorithm based on that data.

[1286] Collection and transmission methods

[1287] Devices (smartwatches and smartphones)

[1288] The device collects biometric information such as the user's body temperature, pulse rate, and facial expression. Specific hardware used includes a temperature sensor and pulse rate sensor in the smartwatch and a smartphone camera. This data is sent to a server in real time. Bluetooth and Wi-Fi are used as communication methods.

[1289] Examples:

[1290] The smartwatch measures body temperature with a temperature sensor and heart rate with a pulse sensor. This data is sent to a smartphone via Bluetooth, and then to a server via Wi-Fi. At the same time, the smartphone's camera captures facial expression data using biometric authentication technology, which is also sent to the server.

[1291] Evaluation methods

[1292] server

[1293] The server integrates the received biometric information and uses an AI model to assess the user's current state. Specifically, it analyzes the integrated data and assesses that the user is feeling stressed based on data such as "body temperature: 37.2°C, pulse rate: 110 BPM, facial expression: stressed facial expression."

[1294] Examples:

[1295] The server receives the data "Temperature: 37.2°C, Pulse: 110 BPM, Facial Expression: Stressed Facial Expression" and uses the AI ​​model to evaluate that "the user is feeling stressed." Based on this evaluation, the prompt sentence for the music to be generated is determined as follows: "User's biological information: Pulse 110 BPM, Body temperature 37.2°C, Facial expression: Stressed. Purpose of the music to be generated: Relaxation."

[1296] A means of generating and playing music

[1297] server

[1298] Based on the evaluation results, an AI composition model is used to generate music that is optimal for the user's state, such as music with a relaxing effect aimed at reducing stress.

[1299] Terminal

[1300] The generated music is sent from the server to the device, where it is played back, with the volume and tone optimized according to the user's state.

[1301] Examples:

[1302] The server generates slow-tempo music as "stress-reducing music" and sends it to the smartphone, where it plays the music and automatically adjusts the volume.

[1303] Monitoring and optimization measures

[1304] Terminal

[1305] While the music is playing, the device again monitors the user's body temperature, pulse rate, and facial expressions, and by comparing the data before and after playback, the effect of the music can be evaluated.

[1306] server

[1307] The server compares the data before and after playback and optimizes the music generation algorithm based on the monitored user reactions. This feedback updates the AI ​​model and is reflected in the next music generation.

[1308] Examples:

[1309] If the data before playback was "body temperature: 37.2°C, pulse rate: 110 BPM, facial expression: stressed expression," and the data after playback changed to "body temperature: 36.8°C, pulse rate: 75 BPM, facial expression: relaxed," the server would evaluate the music as effective and update the AI ​​model.

[1310] Collection and utilization of schedule information

[1311] Terminal

[1312] The system also collects information about the user's schedule and provides music that is optimal for a specific situation. For example, if the user has an important meeting in the morning, the system will provide relaxing music based on that information.

[1313] Examples:

[1314] If a user has entered in their schedule that they have an important meeting coming up in the morning, the server will receive that information and send relaxing music to the device to play to help ease tension.

[1315] This step-by-step approach allows us to provide a music experience that is optimized for each user's condition, improving their quality of life.

[1316] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1317] Step 1:

[1318] Biometric data collection

[1319] The device (smartwatch or smartphone) uses sensors and cameras to collect biometric information such as the user's body temperature, pulse rate, and facial expression. Specifically, the smartwatch measures body temperature with a temperature sensor and heart rate with a pulse sensor. The smartphone camera also takes a picture of the user's face and captures facial expression data.

[1320] Input: User's body temperature, pulse rate, facial expression

[1321] Output: Collected biometric information (body temperature, pulse, facial expression)

[1322] Step 2:

[1323] Sending data to the server

[1324] The device transmits the collected biometric information to a server in real time using Bluetooth or Wi-Fi. For example, data from a smartwatch is transmitted to a smartphone via Bluetooth, and then to the server via Wi-Fi.

[1325] Input: Collected biometric information (body temperature, pulse, facial expression)

[1326] Output: Biometric information sent to the server

[1327] Step 3:

[1328] Data integration and condition assessment

[1329] The server integrates the received biometric information and uses an AI model to evaluate the user's current condition. For example, if data such as "Temperature: 37.2°C, Pulse: 110 BPM, Facial Expression: Stressed Facial Expression" is received, the AI ​​model will analyze this data and conclude that "the user is feeling stressed."

[1330] Input: Biometric information sent to the server

[1331] Output: User's current status evaluation result

[1332] Step 4:

[1333] Music Generation

[1334] Based on the evaluation results, the server uses an AI composition model to generate music that best suits the user's state of mind. For example, it generates music with a relaxing effect for a user who is feeling stressed.

[1335] Input: User's current status evaluation result

[1336] Output: Generated music files

[1337] Step 5:

[1338] Sending and playing music

[1339] The server sends the generated music to the device, which then plays it. The volume and tone of the music are automatically adjusted according to the user's state. Specifically, the server sends music data to the smartphone, which then plays it through its speaker.

[1340] Input: Generated music files

[1341] Output: Music being played

[1342] Step 6:

[1343] Monitoring while music is playing

[1344] While music is playing, the device again monitors the user's body temperature, pulse rate, and facial expressions. This allows data to be collected before and after playback and records any changes. For example, while music is playing, the smartwatch again measures the user's body temperature and pulse rate, and the smartphone captures facial expressions.

[1345] Input: Monitoring results of biological information while music is playing

[1346] Output: Monitored vital signs

[1347] Step 7:

[1348] Reaction evaluation and algorithm optimization

[1349] The server compares the data before and after playback and evaluates the user's reaction. Based on the evaluation results, it optimizes the music generation algorithm and updates the AI ​​model. This feedback is used for the next music generation.

[1350] Input: Monitored biometric data (before and after playback)

[1351] Output: Optimized music generation algorithm

[1352] (Application example 1)

[1353] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1354] Currently, stress management and relaxation while in a vehicle primarily depend on manually selected music and entertainment information. However, manual selection makes it difficult to achieve an appropriate relaxation effect that matches the user's real-time state. Furthermore, conventional music playback systems do not optimize the system to reflect the user's real-time biometric information, resulting in a lack of immediate effects on stress reduction and mood improvement. The present invention aims to solve these problems and provide a system that highly automates relaxation and stress management in a vehicle.

[1355] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1356] In this invention, the server includes means for collecting biometric information of a user, means for evaluating the user's current state based on the collected biometric information, means for generating optimal music according to the evaluated state of the user, means for playing the generated music, means for monitoring the user's reaction during playback, means for optimizing a music generation algorithm based on the monitored user's reaction, and means for linking with the on-board system of the autonomous vehicle, thereby enabling the provision of optimal relaxation music in real time and stress management for the user.

[1357] "User's biometric information" refers to data related to the user's health and emotional state that can be obtained in real time, such as body temperature, pulse rate, and facial expression.

[1358] "User's current state" refers to the user's mental or physical state, such as stress level or degree of relaxation, assessed based on collected biometric information.

[1359] "Optimal music" is music created based on the user's biometric information and current condition to maximize the desired effect, such as relaxation or improved concentration.

[1360] "Generated music" is music that is improvised using artificial intelligence models or the like, depending on the user's current assessed state.

[1361] "Means for playing" refers to a device or system that outputs the generated music so that the user can listen to it.

[1362] "Monitoring means" refers to devices or software that recollect biometric information such as the user's body temperature, pulse rate, and facial expressions while music is being played, and track changes.

[1363] A "music generation algorithm" is a computer program or procedure for generating optimal music based on a user's biometric information and condition.

[1364] An "autonomous vehicle" is a vehicle that operates automatically and can travel safely without user intervention.

[1365] "In-vehicle systems" is a general term for information processing devices, communication devices, entertainment systems, etc. installed in autonomous vehicles.

[1366] This invention is a system that collects a user's biometric information in real time and generates and provides music based on that information. Its purpose is to automate relaxation and stress management for users in self-driving vehicles. Specific embodiments of the invention are described below.

[1367] composition

[1368] The system mainly consists of the following components:

[1369] 1. Biometric information collection device: Collects biometric information such as the user's body temperature, pulse rate, and facial expression. Specifically, sensors installed in smartwatches and smartphones are used.

[1370] 2. Biometric information processing server (hereinafter referred to as the server): Integrates collected biometric information and evaluates the user's current condition. An AI model is used for the evaluation.

[1371] 3. Music Generation Server: Generates optimal music based on the user's state evaluation results. AI composition model is used.

[1372] 4. In-vehicle system for autonomous vehicles: Plays generated music and continuously monitors the user's biometric information.

[1373] Processing flow

[1374] First, a smartwatch or smartphone collects the user's biometric information. The collected data is sent to a server in real time. The server then integrates this data and uses an AI model to evaluate the user's current state. For example, if the user's pulse rate is high, it is determined that the user is feeling stressed.

[1375] Based on the evaluation results, the music generation server generates appropriate music—for example, classical or ambient music for a user who is feeling stressed—and the generated music track is sent to the autonomous vehicle's in-vehicle system for playback inside the vehicle.

[1376] While music is playing, the smartwatch or smartphone continues to collect biometric information and transmits it to the server. By comparing the biometric information before and after playback, the server evaluates the user's reaction and uses this information as feedback to optimize the music generation algorithm.

[1377] Explanation and examples

[1378] As a concrete example, consider a situation where a user experiences a high pulse rate during a long drive. The server evaluates this as a stressful state and generates a relaxing classical music track. This track is then played on the in-car system. If the user's pulse rate returns to normal after playback, the system considers the music track appropriate and reflects this in future music generation.

[1379] Example prompt for a generative AI model:

[1380] "Generate optimal music based on the user's current biometric information. Evaluate their condition based on the information below and select a music track of an appropriate genre.

[1381] Heart rate: 110, temperature: 37.5, facial expression: neutral."

[1382] This makes it possible to provide optimal relaxation music in real time and manage the user's stress, improving the user's comfort in autonomous vehicles and providing a stress-free travel experience.

[1383] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1384] Step 1:

[1385] Smartwatches and smartphones collect users' biometric information.

[1386] Specifically, the smartwatch's pulse and temperature sensors and the smartphone's camera are used to measure the user's body temperature, pulse, and facial expression.

[1387] Input: Real-time biometric information of the user (body temperature, pulse rate, facial expression).

[1388] Output: Collected biometric data.

[1389] Step 2:

[1390] The terminal transmits the collected biometric information to the server.

[1391] The collected data is transferred in real time to a server via the Internet.

[1392] Input: Collected biometric data.

[1393] Output: Biometric data sent to the server.

[1394] Step 3:

[1395] The server integrates the received biometric information to assess the user's current condition.

[1396] The server uses an AI model to assess, for example, a high pulse rate as a state of stress, and a low pulse rate as a state of relaxation.

[1397] Input: Biometric data sent to the server.

[1398] Output: User's current state assessment (e.g., stressed, relaxed, neutral).

[1399] Step 4:

[1400] The server generates optimal music based on the evaluation results.

[1401] Using an AI composition model, it improvises relaxing music based on the user's condition.

[1402] Input: The user's current condition assessment result.

[1403] Output: The generated music track data.

[1404] Step 5:

[1405] The server transmits the generated music track data to the on-board system of the autonomous vehicle.

[1406] The in-car system receives this music data and prepares it for playback.

[1407] Input: The generated music track data.

[1408] Output: Music track data sent to the in-car system.

[1409] Step 6:

[1410] The in-car system plays music.

[1411] Users can enjoy a relaxing effect by listening to the music that is played.

[1412] Input: Music track data sent to the in-car system.

[1413] Output: Music played in the car.

[1414] Step 7:

[1415] While music is playing, the smartwatch or smartphone continues to collect biometric information.

[1416] The biometric information is sent to the server again.

[1417] Input: Real-time biometric information of the user (data while music is playing).

[1418] Output: Continuous biometric data sent to the server.

[1419] Step 8:

[1420] The server compares biometric information before and after music playback to evaluate the user's reaction.

[1421] Based on this evaluation, feedback is provided to the AI ​​model to optimize the music generation algorithm.

[1422] Input: Biometric data before and after playback.

[1423] Output: User response evaluation results and optimized music generation algorithm.

[1424] The above is the processing flow of the system of this invention, which makes it possible to provide optimal relaxation music in real time and manage the user's stress.

[1425] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1426] This invention combines an emotion engine with a system that collects a user's biometric information in real time, evaluates the user's condition based on that information, and improvises and provides optimal music. Furthermore, by monitoring the user's reactions during music playback and optimizing the music generation algorithm based on that data, a more precise and personalized music experience is provided.

[1427] The device (smartwatch or smartphone) collects biometric information such as the user's body temperature, pulse rate, and facial expression. For example, a smartwatch uses a temperature sensor and a pulse sensor to measure body temperature and pulse rate. A smartphone uses a camera to capture the user's facial expression data and analyzes it using an emotion engine. This data is sent to a server in real time.

[1428] The server integrates the received biometric information and facial expression data and uses an emotion engine to recognize the user's emotions. For example, it analyzes the user's facial features from camera footage, and the emotion engine identifies the user's emotions from those expressions (e.g., joy, anger, sadness, and happiness). It then uses an AI model to evaluate the user's current state (e.g., stress level, fatigue level). For example, if the heart rate is higher than normal or if the emotion engine recognizes it as "stressed," the AI ​​will determine that the user is feeling stressed.

[1429] Based on the emotions and biometric information recognized by the emotion engine, the server uses an AI composition model to generate optimal music. For example, if you are feeling stressed, music with a relaxing effect will be generated. The generated music is sent from the server to the device, which then plays the music. At this time, the volume and tempo of the music are also adjusted according to the emotions recognized by the emotion engine.

[1430] While music is playing, the device again monitors the user's biometric information and transmits the playback data to the server in real time. The server compares the biometric information and emotional data before and during playback to evaluate the user's reaction. For example, if the pulse rate stabilizes after music playback and the emotion engine recognizes a "relaxed" feeling from the user's facial expression, the system will determine that the music was appropriate. Based on this feedback, the AI ​​model continues to learn and optimizes the music generation algorithm.

[1431] Furthermore, the device can obtain the user's schedule information and provide music tailored to specific situations, such as calming music during times when relaxation is needed. Furthermore, by using facial expression recognition technology and an emotion engine, the camera can analyze the user's facial expressions and more accurately assess the user's mood and emotions.

[1432] This system can provide users with an advanced music experience to improve their quality of life, including daily stress management, relaxation, and performance improvement. As a specific example, if a user has an important meeting in the morning, the smartwatch detects a high pulse rate and the emotion engine recognizes that the user's face is tense. The server receives this data and evaluates the user as feeling stressed. The AI ​​composition model generates music with a relaxing effect and plays it on the device. If the user's body temperature and pulse rate return to normal during playback and the emotion engine recognizes a relaxed facial expression, the system will evaluate the music as appropriate and reflect this in future music generation.

[1433] This allows users to always receive the most appropriate music for their situation, improving their quality of life. By integrating emotion recognition and biometric information, this system achieves a highly personalized music experience that goes beyond conventional music playback systems.

[1434] The processing flow will be explained below.

[1435] Step 1:

[1436] Devices (smartwatches and smartphones) collect biometric information from users. For example, smartwatches measure body temperature and pulse, and smartphones use cameras to capture facial expression data.

[1437] Step 2:

[1438] The device transmits the collected biometric information and facial expression data to a server in real time. Body temperature, pulse rate, and facial expression data are integrated and uploaded to the server as a user profile.

[1439] Step 3:

[1440] The server uses an emotion engine to recognize the user's emotions based on the received biometric information and facial expression data. For example, it analyzes the user's facial features from camera footage and identifies emotions from their expressions (e.g., joy, anger, sadness, or happiness).

[1441] Step 4:

[1442] The server uses an AI model based on the emotions and biometric information recognized through the emotion engine to evaluate the user's current state (e.g., stress level, fatigue level). For example, if the heart rate is higher than normal and the emotion engine recognizes this as "stress," it will determine that the user is feeling stressed.

[1443] Step 5:

[1444] The server uses an AI composition model to generate optimal music based on the user's assessed state. For example, it generates relaxing music for a user who is feeling stressed.

[1445] Step 6:

[1446] The server sends the generated music data to the terminal, which then imports the received music data and prepares to play the music.

[1447] Step 7:

[1448] The device plays music at the optimal volume. The volume settings are also adjusted based on the user's state. The emotion engine automatically adjusts the tempo and volume of the music based on the emotions it recognizes.

[1449] Step 8:

[1450] The device continues to monitor the user's biometric information while playing music, and the data is sent to the server in real time.

[1451] Step 9:

[1452] The server compares the user's biometric and emotional data before and during playback to evaluate their reaction. For example, if the user's pulse rate stabilizes after music playback and the emotion engine recognizes a relaxed facial expression, the system will determine that the music was appropriate.

[1453] Step 10:

[1454] The server will then provide feedback on the evaluation results and update the AI ​​model, which will optimize the music generation process from the next time onwards.

[1455] Step 11:

[1456] The device collects the user's schedule information and provides music tailored to specific situations, such as calming music during times when relaxation is needed.

[1457] Step 12:

[1458] The device uses the camera to analyze the user's facial expression data to assess their mood and emotions, providing a more personalized music experience.

[1459] Through this series of processes, the system can provide music that is best suited to the user's condition, improving the user's quality of life.

[1460] Example 2

[1461] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1462] Conventional music playback systems have had difficulty collecting a user's emotions and biometric information in real time and providing optimal music based on that information. In particular, there is a need to accurately assess a user's current state and emotions, and generate and provide personalized music based on that assessment. Furthermore, there is a need to provide a more precise and personalized music experience by monitoring the user's reactions during music playback in real time and incorporating the results into the music generation algorithm.

[1463] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1464] In this invention, the server includes means for collecting biometric information of a user, means for evaluating the user's current state based on the collected biometric information, means for generating optimal music according to the user's evaluated state, means for playing the generated music, means for monitoring the user's reaction during playback, means for optimizing a music generation algorithm based on the monitored user's reaction, means for integrating the biometric information and facial expression data and recognizing the user's emotion using an emotion engine, and means for transmitting the biometric information during music playback to the server in real time. This makes it possible to generate optimal personalized music based on the user's biometric information and emotion data and provide it in real time.

[1465] "Biometric information" refers to data related to individual vital activities, such as a user's body temperature, pulse rate, and respiratory rate.

[1466] "Facial expression data" is visual information based on a user's facial features and muscle movements, and is data used for emotion recognition.

[1467] An "emotion engine" is an algorithm or model that analyzes input facial expressions and biometric information to recognize the user's emotions and psychological state.

[1468] An "AI composition model" is an artificial intelligence algorithm used to generate personalized music based on a user's emotions and state.

[1469] "Monitoring" is the act of continuously observing the state or changes of an object and collecting data.

[1470] "Optimization" refers to adjusting parameters and algorithms to maximize the performance of a system or process according to specific objectives and conditions.

[1471] "Condition assessment" refers to analyzing and judging the user's current health and psychological state based on collected biometric information and facial expression data.

[1472] "Music generation" is the process of using specific algorithms and models to create music that responds to the user's state and emotions.

[1473] "Real-time" means that processing and response are nearly instantaneous, with almost no delay.

[1474] A "server" is a computer system that processes and manages data within a network and provides services in response to requests from terminals.

[1475] This invention is an advanced system that collects biometric and facial expression data from users, evaluates their emotions and state based on that data, and generates and delivers optimal music. Furthermore, it monitors users' reactions during music playback and optimizes the music generation algorithm based on the results, providing a precise and personalized music experience.

[1476] 1. Collection of biometric information

[1477] The device (smartwatch or smartphone) collects biometric information such as the user's body temperature, pulse rate, and facial expression. The smartwatch uses a temperature sensor and pulse rate sensor to measure the user's body temperature and pulse rate. The smartphone uses a camera to capture the user's facial expression data and analyzes it using an emotion engine.

[1478] Examples:

[1479] The smartwatch measures body temperature and pulse every second and transmits the data to a smartphone in real time.

[1480] The smartphone camera captures facial expressions, and the emotion engine analyzes the facial data to recognize the emotions of joy, anger, sadness, and happiness.

[1481] 2. Data submission and analysis

[1482] The biometric information collected by the device and analyzed facial expression data are sent to a server in real time. The server integrates this data and uses an emotion engine to recognize emotions. It also uses an AI model to assess the user's current state (e.g., stress level, fatigue level).

[1483] Specific server behavior:

[1484] The server stores the received biometric information and facial expression data in a database and passes the data to the analysis module.

[1485] The emotion engine checks the facial expression analysis results and identifies states such as "stressed" or "relaxed."

[1486] 3. Music Generation and Playback

[1487] The emotion engine recognizes the user's emotions, and the AI ​​composition model generates optimal music based on biometric information. The generated music is sent from the server to the device, where it is played in real time.

[1488] Examples:

[1489] If the user is assessed as feeling stressed, the server's AI composition model will generate relaxing music.

[1490] The generated music file is sent to the device, which then plays the music through its speakers.

[1491] 4. Monitoring and optimization during music playback

[1492] While music is playing, the device again monitors the user's biometric information and transmits it to the server in real time. The server compares the biometric information before and after playback and evaluates the user's reaction. Based on this feedback data, the AI ​​model continues to learn and optimizes the music generation algorithm.

[1493] Specific server behavior:

[1494] Biometric information before and during playback is compared to evaluate whether the user's pulse rate and facial expression are relaxed.

[1495] The AI ​​model is updated based on the feedback data, optimizing the next music generation parameters.

[1496] The system analyzes users' biometric and emotional data in real time and provides personalized music experiences, improving the quality of their lives.

[1497] Example prompt sentence:

[1498] "We want to design a system that collects biometric information from users and evaluates their emotions and state based on that data. We will then use an emotion engine and an AI composition model to generate optimal music and deliver it to them in real time."

[1499] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1500] Step 1:

[1501] Biometric data collection

[1502] The device (smartwatch) measures the user's body temperature and pulse rate.

[1503] Input: User's body temperature and pulse information

[1504] How it works: The smartwatch's sensors detect body temperature and pulse every second and collect this data.

[1505] Output: Temperature and pulse data set

[1506] Step 2:

[1507] Capturing facial expression data

[1508] The device (smartphone) uses a camera to capture the user's face and send it to the emotion engine.

[1509] Input: Video of the user's face

[1510] Specific operation: The smartphone camera captures facial expressions every second and passes the image data to the emotion engine.

[1511] Output: Analyzed facial expression data

[1512] Step 3:

[1513] Transmission of biometric and facial expression data

[1514] The device sends the collected biometric information and analyzed facial expression data to the server.

[1515] Input: Body temperature, pulse rate dataset and facial expression data

[1516] Specific operation: The terminal divides this data into packets and sends them to a server via the Internet.

[1517] Output: Biometric information and facial expression data received by the server

[1518] Step 4:

[1519] Data integration and analysis

[1520] The server integrates the received biometric information and facial expression data and analyzes them using an emotion engine.

[1521] Input: Biometric information and facial expression data received by the server

[1522] Specific operation: The server stores these data in a database and passes them to the analysis module.

[1523] Data calculation: The emotion engine on the server checks the facial expression analysis results and identifies the emotion.

[1524] Output: Identified emotions and integrated biometric information

[1525] Step 5:

[1526] Condition assessment

[1527] The server uses an AI model to assess the user's condition based on the identified emotions and biometric information.

[1528] Input: identified emotions and integrated biometric information

[1529] Specific operation: The AI ​​model analyzes biometric information and evaluates the user's stress level and fatigue level.

[1530] Data calculation: AI model calculates stress and fatigue levels.

[1531] Output: Evaluated user state (e.g., stress level, fatigue level)

[1532] Step 6:

[1533] Music Generation

[1534] The server uses an AI composition model to generate optimal music based on the user's evaluated state.

[1535] Input: Evaluated user status

[1536] How it works: The AI ​​composition model analyzes the input data and determines musical parameters (e.g., tempo, melody).

[1537] Data calculation: AI composition model generates relaxing music.

[1538] Output: Generated music data

[1539] Step 7:

[1540] Sending music data

[1541] The server transmits the generated music data to the terminal.

[1542] Input: Generated music data

[1543] Specific operation: The server divides the music data into packets and sends them to the terminal.

[1544] Output: Music data arriving on the device

[1545] Step 8:

[1546] Playing music

[1547] The terminal plays the received music data.

[1548] Input: Music data arriving on the device

[1549] What it does: The device decodes the music and plays it through the speaker, adjusting the volume and tempo in real time as needed.

[1550] Output: Music played to the user

[1551] Step 9:

[1552] Monitoring biological information during regeneration

[1553] The device monitors the user's biometric information even while music is playing.

[1554] Input: Biometric information during music playback

[1555] How it works: The smartwatch continuously measures your body temperature and pulse rate while music is playing.

[1556] Output: Body temperature and pulse data during playback

[1557] Step 10:

[1558] Sending monitoring results

[1559] The device transmits the biometric information being played back to the server in real time.

[1560] Input: Body temperature and pulse data during playback

[1561] Specific operation: The device divides the biometric information into packets and sends them to the server.

[1562] Output: Biometric information received by the server during playback

[1563] Step 11:

[1564] Feedback and Optimization

[1565] The server compares biometric information before and after music playback and optimizes the music generation algorithm based on an AI model.

[1566] Input: Biometric information before and during playback

[1567] What it does: The server analyzes the feedback data and adds new patterns and trends to the AI ​​model.

[1568] Data computation: AI models continue to learn and optimize their algorithms.

[1569] Output: Optimized music generation algorithm

[1570] (Application example 2)

[1571] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1572] Modern society demands methods to optimize users' mental and physical states in real time. However, conventional music generation systems have been unable to fully utilize users' biometric and emotional data, making it difficult to provide personalized music experiences. Furthermore, these systems lack the ability to utilize feedback to optimize music generation algorithms, making it difficult to provide a sustained, high-quality music experience. Furthermore, they lack the ability to provide music according to users' schedules and monitor their reactions in real time, preventing personalized music experiences that would improve users' quality of life.

[1573] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting biometric information of a user, means for evaluating the user's current state based on the collected biometric information, means for generating optimal music according to the user's evaluated state, means for playing the generated music, means for monitoring the user's reaction during playback, means for optimizing a music generation algorithm based on the monitored user's reaction, means for analyzing the user's facial expression data to evaluate the user's emotions, means for adjusting the volume and tempo based on the user's emotions using an emotion engine, and means for the terminal to link with external data in real time and instantly provide the generated music. This makes it possible to provide music optimized for the user's mental and physical state by integrating the user's biometric information and emotional data, and to continuously improve accuracy through real-time reaction monitoring and feedback.

[1574] "User's biometric information" refers to data relating to the user's body, such as body temperature, pulse rate, and facial expression.

[1575] "Means for collection" refers to devices and methods for acquiring biometric information of users using various sensors and devices.

[1576] "Means of evaluation" refers to the process or device that analyzes and judges the user's current condition based on the collected biometric information.

[1577] "Generative means" refers to the process or technology that produces optimal music based on the analyzed state.

[1578] "Means for playing" refers to a device or method for letting a user listen to the generated music.

[1579] A "monitoring means" is a process or device that continuously monitors the user's reaction during playback.

[1580] "Optimization methods" are processes or techniques that improve music generation algorithms based on monitored user response data.

[1581] "Facial expression data" is information relating to the facial expression of the user.

[1582] The "analysis means" is a process or device that analyzes the collected data to evaluate the user's emotions and state.

[1583] The "emotion engine" is a technology that recognizes the user's emotions from facial expression data and other data, and reflects this in the music generation.

[1584] A "terminal" is a device that acquires, sends, and receives data, such as a smartphone or smartwatch.

[1585] "External data" is additional or contextual information obtained from the internet or other services.

[1586] "Means of collaboration" refers to mechanisms and technologies that allow terminals to share information with external data in real time.

[1587] "Relaxing music" is music created with the aim of reducing tension and stress in the user and making them feel relaxed.

[1588] This invention is a system that collects biometric information from a user in real time and generates optimal music using an emotion engine and a generative AI model. Specific embodiments of this system are described below.

[1589] First, the system is equipped with a means to collect biometric information such as the user's body temperature, pulse rate, and facial expression. Specific examples of devices include smartwatches and smartphones. The smartwatch uses a temperature sensor and a pulse sensor to measure body temperature and pulse rate. The smartphone uses a camera to capture the user's facial expression data, which is then analyzed by the emotion engine.

[1590] The collected biometric information and facial expression data are sent to a server in real time. The server integrates this information and uses an emotion engine to recognize the user's emotions. For example, the server analyzes the user's facial features from camera footage, and the emotion engine identifies the user's emotions from those expressions (e.g., joy, anger, sadness, or happiness). Furthermore, an AI model is used to evaluate the user's current state (e.g., stress level, fatigue level). Specifically, if the heart rate is higher than normal or if the emotion engine recognizes it as "stress," the AI ​​will determine that the user is feeling stressed.

[1591] The server then uses an AI composition model to generate optimal music based on the emotions and biometric information recognized by the emotion engine. For example, if it determines that the user is feeling stressed, it will generate music with a relaxing effect. The generated music is sent from the server to the device and played on the device. At this time, the volume and tempo of the music are also adjusted according to the emotions recognized by the emotion engine.

[1592] While music is playing, the device again monitors the user's biometric information and transmits the playback data to the server in real time. The server compares the biometric and emotional data before and during playback to evaluate the user's reaction. For example, if the user's pulse rate stabilizes after music playback and the emotion engine recognizes a "relaxed" feeling from the user's facial expression, the system will determine that the music was appropriate. Based on this feedback, the AI ​​composition model continues to learn and optimizes the music generation algorithm.

[1593] As a specific example, if a user has an important meeting in the morning, the smartwatch detects a high pulse rate, and the smartphone's emotion engine recognizes the user's tension. The server receives this data and evaluates the user as feeling stressed. The AI ​​composition model then generates relaxing music and plays it on the device. If the user's body temperature and pulse rate return to normal during playback and the emotion engine recognizes a relaxed facial expression, the system will determine that the music was appropriate and can reflect this in future playbacks.

[1594] The following is an example of a prompt sentence:

[1595] Generate optimal relaxing music for the user based on the following biometric and emotional data:

[1596] Body temperature: 37.5°C

[1597] Pulse rate: 85 bpm

[1598] Emotion: Tension (facial expression data)

[1599] This invention allows users to always receive the most appropriate music for their situation, thereby improving their quality of life. The system provides users with a highly personalized music experience by integrating emotion recognition and biometric information.

[1600] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1601] Step 1:

[1602] The devices (smartwatch and smartphone) collect the user's biometric information and facial expression data. Specifically, the smartwatch's temperature sensor and pulse sensor are used to measure body temperature and pulse, and the smartphone's camera is used to capture facial expression data. The input is sensor data and camera images, and the output is the collected biometric information and facial expression data.

[1603] Step 2:

[1604] The device transmits the collected biometric information and facial expression data to the server in real time. The input is the data collected in step 1, and the output is the data transmitted to the server.

[1605] Step 3:

[1606] The server integrates the received biometric information and facial expression data and uses an emotion engine to recognize the user's emotions. Specifically, it analyzes facial features from camera footage and identifies emotions based on the facial expressions. The input is the biometric information and facial expression data sent from the device, and the output is the user's emotional data.

[1607] Step 4:

[1608] The server uses an AI composition model to generate optimal music based on the user's emotional data and biometric information. For example, if the user is feeling stressed, it will generate music with a relaxing effect. The input is the user's emotional data and biometric information, and the output is the generated music data.

[1609] Step 5:

[1610] The server sends the generated music data to the terminal. The input is the generated music data, and the output is the music data sent to the terminal.

[1611] Step 6:

[1612] The device plays the received music data. Specifically, the smartphone plays the music and adjusts the volume and tempo according to the emotion recognized by the emotion engine. The input is the music data sent from the server, and the output is the music being played.

[1613] Step 7:

[1614] The device again monitors the user's biometric information while music is playing and transmits it to the server in real time. The input is the biometric information collected during playback, and the output is the data transmitted to the server.

[1615] Step 8:

[1616] The server compares the biometric and emotional data before and during playback to evaluate the user's reaction. The input is the data before and during playback, and the output is the evaluated reaction data.

[1617] Step 9:

[1618] The server optimizes the music generation algorithm based on the evaluated reaction data. Specifically, if the user's pulse stabilizes after playback and the emotion engine recognizes "relaxation," it trains the AI ​​composition model and improves the algorithm. The input is the evaluated reaction data, and the output is an optimized music generation algorithm.

[1619] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1620] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1621] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1622] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1623] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1624] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1625] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1626] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1627] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1628] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1629] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1630] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1631] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1632] 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.

[1633] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1634] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1635] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1636] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1637] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1638] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1639] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1640] The following is further disclosed regarding the above embodiment.

[1641] Understood. Below are proposed patent claims for the described system.

[1642] (Claim 1)

[1643] a means for collecting biometric information of a user;

[1644] means for assessing a user's current state based on the collected biometric information;

[1645] means for generating optimal music according to the user's evaluated state;

[1646] means for playing the generated music;

[1647] a means for monitoring user reactions during playback;

[1648] means for optimizing a music generation algorithm based on monitored user responses;

[1649] A system including:

[1650] (Claim 2)

[1651] A means for acquiring schedule information of a user;

[1652] 10. The system of claim 1, further comprising means for providing music best suited to a particular situation based on the obtained schedule information.

[1653] (Claim 3)

[1654] 10. The system of claim 1, further comprising means for analyzing the user's facial expression data to assess the user's mood or emotion.

[1655] "Example 1"

[1656] (Claim 1)

[1657] a means for collecting biometric information of a user;

[1658] means for transmitting the collected biometric information to a server in real time;

[1659] means for integrating the transmitted biometric information to assess the user's current state;

[1660] a means for generating optimal music based on the evaluation results;

[1661] means for transmitting the generated music to a user's terminal and playing it;

[1662] a means for re-monitoring the user's biometric information while the music is being played;

[1663] means for optimizing a music generation algorithm based on monitored user responses;

[1664] A system including:

[1665] (Claim 2)

[1666] A means for acquiring schedule information of a user;

[1667] 10. The system of claim 1, further comprising means for providing optimal music for a particular situation based on the obtained schedule information.

[1668] (Claim 3)

[1669] 10. The system of claim 1, further comprising means for analyzing the user's facial expression data to assess the user's mood or emotion.

[1670] "Application Example 1"

[1671] (Claim 1)

[1672] a means for collecting biometric information of a user;

[1673] means for assessing a user's current state based on the collected biometric information;

[1674] means for generating optimal music according to the user's evaluated state;

[1675] means for playing the generated music;

[1676] a means for monitoring user reactions during playback;

[1677] means for optimizing a music generation algorithm based on monitored user responses;

[1678] The system further includes means for interfacing with an on-board system of the autonomous vehicle.

[1679] (Claim 2)

[1680] A means for acquiring schedule information of a user;

[1681] 10. The system of claim 1, further comprising means for providing music best suited to a particular situation based on the obtained schedule information.

[1682] (Claim 3)

[1683] 10. The system of claim 1, further comprising means for analyzing the user's facial expression data to assess the user's mood or emotion.

[1684] "Example 2: Combining Emotion Engines"

[1685] (Claim 1)

[1686] a means for collecting biometric information of a user;

[1687] means for assessing a user's current state based on the collected biometric information;

[1688] means for generating optimal music according to the user's evaluated state;

[1689] means for playing the generated music;

[1690] a means for monitoring user reactions during playback;

[1691] means for optimizing a music generation algorithm based on monitored user responses;

[1692] a means for integrating biometric information and facial expression data and recognizing a user's emotions using an emotion engine;

[1693] a means for transmitting biometric information to a server in real time during music playback;

[1694] A system including:

[1695] (Claim 2)

[1696] A means for acquiring schedule information of a user;

[1697] 10. The system of claim 1, further comprising means for providing music best suited to a particular situation based on the obtained schedule information.

[1698] (Claim 3)

[1699] 10. The system of claim 1, further comprising means for analyzing the user's facial expression data to assess the user's mood or emotion.

[1700] "Application example 2 when combining emotion engines"

[1701] (Claim 1)

[1702] a means for collecting biometric information of a user;

[1703] means for assessing a user's current state based on the collected biometric information;

[1704] means for generating optimal music according to the user's evaluated state;

[1705] means for playing the generated music;

[1706] a means for monitoring user reactions during playback;

[1707] means for optimizing a music generation algorithm based on monitored user responses;

[1708] means for analyzing the user's facial expression data to evaluate the user's emotions;

[1709] a means for adjusting volume and tempo based on a user's emotions using an emotion engine;

[1710] A means for the device to connect with external data in real time and instantly provide the generated music;

[1711] A system including:

[1712] (Claim 2)

[1713] A means for acquiring schedule information of a user;

[1714] 10. The system of claim 1, further comprising means for providing music best suited to a particular situation based on the obtained schedule information.

[1715] (Claim 3)

[1716] 10. The system of claim 1, further comprising means for feeding back the effect of the generated music and for learning and updating the music generation algorithm. [Explanation of symbols]

[1717] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for collecting biometric information of a user; means for assessing a user's current state based on the collected biometric information; means for generating optimal music according to the user's evaluated state; means for playing the generated music; a means for monitoring user reactions during playback; means for optimizing a music generation algorithm based on monitored user responses; A system including:

2. A means for acquiring schedule information of a user; 10. The system of claim 1, further comprising means for providing music best suited to a particular situation based on the obtained schedule information.

3. The system of claim 1 , further comprising means for analyzing the user's facial expression data to assess the user's mood or emotion.

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