system

Abstract

We provide the system. [Solution] Image capture means for acquiring user facial expression information and posture information, An estimation means for estimating the user's emotional state and fatigue level based on the acquired facial expression information and posture information, A generation means for generating music data based on estimated emotional state and fatigue level, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In daily life, since the technology for automatically providing music according to the user's emotional state and fatigue level is not fully established, many people have trouble finding music suitable for that moment. Since such problems may affect work efficiency and relaxation, it is necessary to solve this problem.

Means for Solving the Problems

[0005] This invention includes an image capture means for acquiring emotional information from the user's face and body posture, and an estimation means for analyzing the obtained information to estimate the user's emotional state and fatigue level. Furthermore, by using a means for generating optimal music data based on the estimated information, the system realizes a system that provides music tailored to the user's emotions and physical condition. In addition, if the user hums, the system includes means for generating music based on that sound, and means for incorporating biometric information and reflecting it in music generation, enabling more personalized music provision.

[0006] "Image capture means" refers to a device or method for acquiring information on a user's facial expressions and posture.

[0007] "Estimation means" refers to a process or algorithm for analyzing and estimating the user's emotional state and fatigue level based on acquired facial and posture information.

[0008] "Generation means" refers to technology or software for generating optimal music data according to estimated emotional state and fatigue level.

[0009] "Means for acquiring humming audio" refers to a device or method for recording a user's humming and acquiring the resulting audio data.

[0010] "Means for generating music data" refers to an algorithm or system that creates new music based on user status information and acquired data.

[0011] "Means for acquiring biometric information" refers to devices or technologies for collecting physiological data such as a user's blood pressure and heart rate. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a tagged processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, a tagged RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a tagged storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, a tagged communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention relates to a system that provides optimal music according to the user's emotional state and fatigue level. In this system, the terminal uses a camera mounted on a PC or smartphone to capture the user's facial expressions and posture information, and acquires this data in real time. Furthermore, the terminal also acquires biometric information such as blood pressure and heart rate from a smartwatch worn by the user via communication means such as Bluetooth.

[0034] Next, the device sends all acquired data to a server for analysis. The server uses collaborative filtering and emotion estimation algorithms to estimate the user's emotional state and fatigue level. This includes techniques that capture and analyze subtle changes in facial expressions and posture. The estimated emotional state and fatigue level serve as criteria for selecting the music most suitable for the user.

[0035] The server generates music data using an AI music generation model based on estimated emotional data, combined with the user's past background music playback history. This model has the ability to create music that matches the user's individual preferences and the mood at that time. If the user hums, the voice is recorded in advance on the device and sent to the server as melody information. The server can also use this melody information to generate individual music based on the humming.

[0036] The generated music is sent to the device, which then suggests the song to the user. If the user accepts the suggestion, the music plays on the device. For example, when the user wants to relax, calming piano music is provided, and when they want to feel energized, an upbeat and cheerful song is generated. In this way, the system can customize the music experience in real time according to the user's emotions and state.

[0037] This allows users to regulate their mental state and enrich their daily lives through music optimized for their mood and emotions at any given moment.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The device uses the camera on a PC or smartphone to capture the user's facial expressions and posture in real time and acquire data. If the user is wearing a smartwatch, it also acquires biometric information such as blood pressure and heart rate using Bluetooth communication.

[0041] Step 2:

[0042] The device transmits acquired facial expressions, posture, and biometric information to the server. The data is converted to an appropriate format and configured to minimize latency to enable real-time analysis.

[0043] Step 3:

[0044] The server analyzes the received data. First, it uses collaborative filtering and emotion estimation algorithms to analyze the user's facial expressions and posture, and estimate their emotional state and fatigue level. This is achieved by identifying characteristic patterns within the data.

[0045] Step 4:

[0046] The server considers estimated emotional state, fatigue level, and past background music playback history to generate optimal music data using an AI music generation model. This model selects songs that best match the user's preferences and emotions at that moment. If humming is detected, the system also generates music incorporating that melody information.

[0047] Step 5:

[0048] The generated music data is sent to the device. The device then suggests the newly generated music to the user in the form of a notification or pop-up message.

[0049] Step 6:

[0050] If the user approves the suggested music, the device will play the music data. During playback, feedback data regarding the user's impressions and satisfaction can be collected and used to improve the music generation process in the future.

[0051] (Example 1)

[0052] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0053] With the rapid advancement of information technology, there is a growing demand for services that are tailored to users' emotions and physical states. However, many existing systems have been limited in their ability to accurately understand the user's state and provide appropriate music information. In particular, real-time estimation of emotional states and automatic generation of music that matches individual preferences remain challenges.

[0054] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0055] In this invention, the server includes image acquisition means for acquiring user facial expression data and posture data, communication means for acquiring user physical information, analysis means for estimating the user's emotional state and fatigue level based on the acquired facial expression data, posture data, and physical information, generation means for generating music information by adding past playback history to the estimated emotional state and fatigue level, and provision means for providing the generated music information to the user. This makes it possible to generate and provide music information that is tailored to the user's emotions and physical state.

[0056] "Image acquisition means" refers to devices and methods for acquiring user facial expression data and posture data. These devices and methods make it possible to capture information such as changes in the user's facial expression and body tilt in real time.

[0057] "Communication methods" refer to data communication technologies used to acquire a user's physical information. This includes protocols for wirelessly acquiring data from smart devices and communication technologies between devices.

[0058] "Analysis means" refers to methods and devices for analyzing acquired facial expression data, posture data, and physical information to estimate the user's emotional state and fatigue level. This analysis allows for an effective evaluation of the user's psychological and physiological state.

[0059] "Generation method" refers to the process or technology for generating music information by taking into account estimated emotional state and fatigue level, along with past playback history. This generation function selects or generates music that is optimal for the user's current state.

[0060] "Means of delivery" refers to methods and devices for conveying generated music information to the user. This allows the user to receive and listen to the generated music.

[0061] This invention is a system that generates and provides appropriate music information in real time based on the user's emotional state and fatigue level. The embodiments thereof are described in detail below.

[0062] The device uses a camera to capture the user's facial expressions and posture in real time. This camera is built into the PC or smartphone and uses facial recognition technology to analyze subtle changes in facial expressions and posture. Furthermore, the device uses communication technologies such as Bluetooth to collect physiological data such as heart rate and blood pressure from smart devices.

[0063] Once data is collected, the device sends it to a server. The server processes this data using collaborative filtering and sentiment analysis algorithms to estimate the user's current emotional state and fatigue level.

[0064] Based on the estimation results, the server uses a generation AI model to create music information best suited to the user. The user's past playback history is also considered during the generation process, resulting in individually customized music. For example, the prompt might read, "Currently, the user is relaxed, and their heart rate is stable. Please generate calming music."

[0065] The generated music is returned to the device, which then suggests the song to the user. If the user accepts the suggestion, the music is played through the device, creating a real-time musical experience tailored to the user's emotional state.

[0066] This system allows users to experience psychological relaxation and increased vitality by listening to music tailored to their mood and emotions at the moment. By utilizing the aforementioned hardware and software technologies, the system can function efficiently and effectively.

[0067] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0068] Step 1:

[0069] The device activates the camera built into the PC or smartphone and captures the user's facial expression and posture data in real time. It applies a facial recognition algorithm to capture subtle changes in facial expression and body tilt. The input is video data from the camera, and the output is analyzed facial expression and posture data.

[0070] Step 2:

[0071] The terminal acquires physiological data such as heart rate and blood pressure from a smart device via Bluetooth communication. This allows the terminal to collect data about the user's physical condition. The input is physiological signals from the smart device, and the output is analyzed physiological information.

[0072] Step 3:

[0073] The terminal collects facial expression data, posture data, and physiological information, packages them into packets, and sends them to the server. The data is encrypted before transmission to ensure security. The input consists of the analyzed data, and the output consists of encrypted data packets.

[0074] Step 4:

[0075] The server processes the received data through an analysis algorithm to estimate the user's emotional state and fatigue level. Using collaborative filtering technology, it compares this data with past data to accurately understand the user's psychological state. The input is data packets from the terminal, and the output is the estimated emotional state and fatigue level.

[0076] Step 5:

[0077] The server uses a generation AI model to generate music information, taking into account the estimated emotional state, fatigue level, and the user's past playback history. The input is the emotional state, fatigue level, and playback history, and the prompt is "Current emotion is relaxed, heart rate is stable. Please generate calming music." The output is the generated music file.

[0078] Step 6:

[0079] The server sends the generated music file to the terminal. The terminal suggests the music to the user, and if the user accepts, playback begins. The input is the music file received from the server, and the output is the music experience provided to the user. Here, the terminal briefly describes the content of the music and encourages the user to listen.

[0080] (Application Example 1)

[0081] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0082] Modern consumers are seeking a more comfortable and personalized experience while ordering food and waiting for delivery. However, typical food delivery services lack features that adjust background music according to the user's emotional state and personal preferences, making it difficult to alleviate stress and frustration during waiting times.

[0083] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0084] In this invention, the server includes image capture means for acquiring the user's facial expression and posture information, means for suggesting music suitable for the dish based on the user's order history, and means for generating music data based on the estimated emotional state and fatigue level. This makes it possible to provide music that corresponds to the user's emotional state and order content, enabling a more comfortable and personalized delivery experience.

[0085] A "user" is a consumer who uses the system to place orders or receive music generation services.

[0086] "Facial expression information" refers to data about the user's facial expressions, which is acquired using a camera.

[0087] "Posture information" refers to data about the user's body movements and posture, and, like facial expression information, is acquired by a camera.

[0088] "Image capture means" refers to a shooting device or technology for acquiring user facial expression information and posture information.

[0089] "Emotional state" refers to the user's current state of mind and emotions, and is estimated by the system.

[0090] "Fatigue level" is an indicator that shows the degree of fatigue a user is experiencing, and it is estimated by the system along with their emotional state.

[0091] "Estimation means" refers to technologies and algorithms used to analyze and determine a user's emotional state and fatigue level based on acquired data.

[0092] "Music data" refers to music information provided to the user, and is created by various generation methods.

[0093] "Generation means" refers to a technology or system for creating music data based on estimated emotional states and fatigue levels.

[0094] "Order history" is a record of food and beverage orders that a user has placed in the past.

[0095] "Methods for suggesting music suitable for a dish" refer to technologies and systems that select and present music that is appropriate for a dish based on the user's order history.

[0096] The system that realizes this invention first acquires the user's facial expression information, posture information, and biometric information using a camera and smartwatch on a terminal. A smartphone or tablet camera is used as the image capture means, and biometric information such as heart rate and blood pressure is collected from the smartwatch via Bluetooth communication. This data is transmitted to a server in real time.

[0097] The server uses collaborative filtering and emotion estimation algorithms based on the collected data to estimate the user's emotional state and fatigue level. From these estimation results, a generative AI model is used to generate music data tailored to each individual user. Furthermore, it can analyze the user's order history and suggest music that matches their order.

[0098] For example, when a user orders a pizza, cheerful and fun music can be played to make the waiting time for the food more enjoyable. This allows for a more personalized delivery experience for the user.

[0099] The hardware used includes smartphones, tablets, smartwatches, and servers such as Amazon Web Services (AWS®). The software utilizes facial recognition algorithms, emotion estimation algorithms, and OpenAI® generative AI models for music generation.

[0100] For example, if a user's current emotional state requires relaxation, and they have ordered Asian food, the system might use the prompt "Generate relaxing Asian music" to generate music. This allows for music suggestions based on specific order details.

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] The device uses the smartphone's camera to capture the user's facial expressions and posture. In addition, it acquires biometric information such as heart rate and blood pressure through a smartwatch. This data is collected in real time and converted into a digital format. In this process, the input is data of the user's actual facial expressions and biometric state, and the output is the corresponding digital information.

[0104] Step 2:

[0105] The terminal transmits the collected digital information to the server. The server performs collaborative filtering and emotion estimation algorithms to analyze the received data. The inputs are transmitted facial expression information, posture information, and biometric information, and the outputs are estimated data of emotional state and fatigue level.

[0106] Step 3:

[0107] The server references the user's past order history based on estimated emotional state and fatigue levels. It then generates prompt messages to suggest music related to specific food orders. At this stage, the inputs are the emotional state estimation data and order history, while the output is the specific prompt message for music generation.

[0108] Step 4:

[0109] The server uses a generative AI model to generate music data based on the created prompt sentences. In this step, the input is the music generation prompt sentences, and the output is the generated customized music data.

[0110] Step 5:

[0111] The server sends the generated music data to the terminal, and the terminal suggests music to the user. If the user accepts the suggestion, the music is played. Here, the input is the music data sent from the server, and the output is the provision of a music experience through the terminal.

[0112] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0113] This invention relates to a system that analyzes a user's emotional state using an emotion engine based on the user's facial expression and posture information, and generates music data based on that analysis.

[0114] First, the device uses the camera on a PC or smartphone to acquire the user's facial expressions and posture information in real time. This allows for the rapid and accurate capture of specific information related to the user's emotions. The device also acquires biometric information from the smartwatch the user is wearing, which is used for detailed emotional analysis.

[0115] This acquired information is sent to a server, which uses its built-in emotion engine to recognize and analyze the user's emotional state. The emotion engine analyzes various emotional indicators to determine the user's emotional state. For example, frequent smiles might indicate "happiness," while frequent frowns might indicate "stress."

[0116] Next, the server generates music data using an AI music generation model based on the analyzed emotional data. This model generates music that is most suitable for the user's emotional state. For example, if it is determined that the user needs to relax, calm piano music will be generated.

[0117] Furthermore, if a user provides a hummed tune, the audio data is acquired by the device, and the melody information is sent to the server. Combined with the analysis results of the emotion engine, it is possible to generate music based on this melody information.

[0118] The generated music data is sent to the device, which then suggests songs to the user. If the user accepts the suggestion, the device plays the song. For example, if the user has had a stressful day, the emotion engine can analyze the user's mood and provide relaxing ambient music.

[0119] Thus, the present invention provides music optimized for the user's emotions, offering a musical experience that corresponds to their emotional state at that moment. This contributes to maintaining and improving the user's mental health and enriches their daily life.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] The device captures the user's facial expressions and posture information in real time using the camera of a PC or smartphone, and acquires this data. Furthermore, the device acquires the user's biometric information via a smartwatch to understand the overall picture.

[0123] Step 2:

[0124] The device centralizes the acquired facial expression, posture, and biometric information and sends it to the server as a data packet. This ensures that the data necessary for analysis reaches the server immediately.

[0125] Step 3:

[0126] The server inputs the received data into the emotion engine. The emotion engine analyzes specific emotional states from facial expressions and posture, and estimates states such as "happiness," "stress," and "fatigue." This analysis includes a process of detecting features in the data using deep learning algorithms.

[0127] Step 4:

[0128] The server generates music data using an AI music generation model based on the emotional state estimated by the emotion engine. This model selects and generates music that is best suited to the user's current mood; for example, it creates music with a calming melody when relaxation is needed.

[0129] Step 5:

[0130] When a user hums, the device records the sound and sends it to the server as melody information. The server then uses this melody information to either generate a new song or compose it as existing music.

[0131] Step 6:

[0132] The generated music data is sent to the device. The device then suggests the newly generated music to the user and displays the suggestions via screen notifications or within the app.

[0133] Step 7:

[0134] The user reviews the music suggestions from the device and starts playback upon acceptance. During music playback, the device may request user feedback, which is sent to the server to improve future suggestions.

[0135] (Example 2)

[0136] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0137] In modern times, providing music tailored to a user's emotional state contributes to improved mental health and stress reduction. However, existing systems have limitations in utilizing information from facial expressions and posture, and they are unable to fully utilize users' biometric data or personal humming in music generation. The objective of this invention is to develop a system that provides a more accurate and customized musical experience by using diverse user information.

[0138] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0139] In this invention, the server includes information acquisition means for acquiring user facial expression information and posture information, analysis means for analyzing the user's emotional state based on the facial expression information and posture information collected by the information acquisition means, and music generation means for generating music data using a generation AI model based on the analyzed emotional state. This makes it possible to provide a music experience optimized for the user's emotional state in real time.

[0140] "Information acquisition means" refers to devices and methods for collecting information about a user's facial expressions and posture using sensors and cameras, and acquiring it as data.

[0141] "Analysis means" refers to software or algorithms that process data to identify the user's emotional state based on acquired facial expression and posture information.

[0142] A "generative AI model" is a system that utilizes artificial intelligence technology to automatically generate musical patterns and elements that match the user's emotional state.

[0143] "Music generation means" refers to technologies and methods for automatically creating appropriate music based on the results of an analysis of emotional states.

[0144] A "humming processing method" is a technology that has the function of recording a hummed tune by a user and generating a song based on that audio data.

[0145] "Methods for acquiring biometric information" refer to methods for acquiring physiological data such as a user's heart rate and body temperature using smartwatches or other biosensors.

[0146] "Biometric information analysis means" refers to technologies and algorithms that process and associate acquired biological information in order to utilize it for music data generation.

[0147] This invention is a system that generates music data according to the user's emotional state. A specific embodiment of this system is described below.

[0148] The device uses cameras and sensors on a PC or smartphone to acquire the user's facial expressions and posture information. By using facial recognition software, it can accurately monitor changes in the user's facial expressions in real time. In addition, the device uses wearable devices such as smartwatches to acquire biometric information such as the user's heart rate and body temperature. This allows for the collection of data to analyze the user's emotional state in more detail.

[0149] The information acquired by the device is transmitted to the server via the network. The server is equipped with an emotion engine to analyze this information, estimating the user's emotional state by analyzing facial expressions, posture information, and biometric information. Because the emotion engine utilizes neural network technology, it can distinguish multiple emotional indicators with high accuracy.

[0150] The server uses a generative AI model based on the analyzed emotional state. This generative AI model generates music data that matches the emotional state, and its style and atmosphere can be altered. An example of a prompt would be, "Generate music suitable for a relaxing evening." By entering such prompts, the server automatically generates music appropriate for the user.

[0151] Furthermore, if a user provides a hummed tune, the device acquires the audio data and sends it to the server. The server can then combine the results of the emotion engine's analysis with the melody information of this hummed tune to perform its own music generation.

[0152] The generated music data is sent back from the server to the terminal, which then suggests music to the user. If the user approves the suggestion, the terminal plays the music. For example, on a day when the user is feeling stressed, relaxing music may be provided, contributing to the improvement of the user's mental health.

[0153] This system will enable a more personalized music experience that responds to the user's real-time emotional state.

[0154] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0155] Step 1:

[0156] The device uses the camera of a PC or smartphone to acquire the user's facial expressions and posture information in real time. Using facial recognition software, it detects characteristic points of facial expressions and changes in posture, and records this as digital data. It receives camera video data as input and generates analyzable facial and posture data as output.

[0157] Step 2:

[0158] The device acquires biometric information such as heart rate and body temperature from the smartwatch. A dedicated health monitoring application retrieves this information and records it as digital data representing the user's physical state. It receives biometric measurement data from the smartwatch as input and generates analyzable biometric data as output.

[0159] Step 3:

[0160] The device encrypts the data acquired in Step 1 and Step 2 and sends it to the server over the network. This ensures that data is transferred securely while maintaining privacy. It receives facial expressions, posture, and biometric data as input and outputs them as data to be sent to the server.

[0161] Step 4:

[0162] The server feeds the received data into the emotion engine and begins analysis. The emotion engine uses neural network technology to estimate the user's emotional state. It receives data on facial expressions, posture, and biometrics as input and generates evaluation data regarding the emotional state as output.

[0163] Step 5:

[0164] The server, based on the analysis results of the emotion engine, inputs appropriate prompts into the generative AI model and generates music data. The generative AI model creates music best suited to the user based on prompts such as "Generate music with a relaxing atmosphere." It receives emotional state evaluation data as input and generates music data as output.

[0165] Step 6:

[0166] The terminal receives music data from the server and presents it to the user through an interface that suggests music. If the user approves the suggested music, the terminal plays the song. It receives generated music data as input and plays music as output.

[0167] (Application Example 2)

[0168] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0169] In modern society, providing entertainment that responds immediately to changes in an individual's emotional state is a crucial element in improving the quality of life. However, conventional music playback systems lack the means to analyze a user's emotions and physiological state in real time and provide music that suits them. As a result, users cannot easily obtain music that matches their emotions and physiological state at any given time, and there is a lack of effective support to alleviate stress and anxiety.

[0170] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0171] In this invention, the server includes image acquisition means for acquiring user facial expression data and posture data, estimation means for estimating the user's emotional state based on the acquired facial expression data and posture data, generation means for generating music data based on the estimated emotional state, and means for incorporating pre-acquired physiological data of the user into the music data generation. This enables the user to instantly receive music that suits their emotional state, thereby enhancing daily emotional support.

[0172] "User facial expression data" is digital information obtained by analyzing the features of a user's face, and it serves as basic data for estimating their emotional state.

[0173] "Posture data" refers to information that indicates the position and angle of the user's body, and is used to understand the user's psychological state.

[0174] "Image acquisition means" refers to a device that uses a camera or other imaging device to capture the user's actions and appearance, and converts them into analyzable digital data.

[0175] "Estimation method" is a term that refers to the methods and processes used to identify a user's internal state by performing analysis based on acquired data.

[0176] "Generation means" refers to a method or process for creating an intended output based on predetermined input information, and in this case, it refers to generating music data.

[0177] "Physiological data" refers to digital information that indicates a user's physical function and state, such as heart rate and activity level. This data is an important element in estimating emotional states.

[0178] A "mobile communication terminal" refers to electronic devices such as mobile phones and smartphones that can communicate without being tied to a specific location.

[0179] "Voice-based melodic information" refers to information obtained by acquiring a melody spoken by a user as audio data and converting it into an analyzable format.

[0180] This invention provides a system for generating and playing music based on a user's emotional state. This system includes a server for acquiring and analyzing facial expression data, posture data, and physiological data, centered on the user's mobile communication terminal, and an interface for providing the generated music to the user.

[0181] The server analyzes facial expression data obtained through the user's mobile communication terminal's camera. Specifically, it uses facial expression recognition software (e.g., OpenCV) with the facial images acquired from the camera to understand the user's emotional state. Furthermore, the terminal acquires physiological data such as heart rate from wearable devices such as smartwatches.

[0182] The analyzed data is sent to an emotion estimation engine, which identifies the emotional state. For example, if the user is smiling, it is determined to be in a happy mood. This emotional state serves as the basis for creating music data using a generative AI model. The generative AI model (e.g., OpenAI's music generation model) generates the most suitable song according to this emotional information.

[0183] The generated music is sent to the user's device and presented to the user by a music playback application. If the user accepts it, the device plays the music and works to guide the user's emotional state in a positive direction.

[0184] As a concrete example, let's say a user uses the app on a Friday afternoon, wanting to relax. The app analyzes the user's calm facial expression and obtains data indicating a calm heart rate. The system determines that the user is in a "relaxed state" and prompts the AI ​​model with the message, "You need to relax, generate calming music," which then generates a suitable piano piece. In this way, the system can provide the user with an optimal musical experience in real time.

[0185] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0186] Step 1:

[0187] The device uses a camera to acquire the user's facial expression data in real time. Based on the acquired image data, an expression recognition algorithm analyzes features associated with specific emotions, such as smiles and frown lines. The input is image data from the camera, and the output is data indicating the emotional state.

[0188] Step 2:

[0189] The device acquires physiological data such as heart rate and activity level from wearable devices such as smartwatches. This allows the user's physiological state to be understood. The input is physiological data from sensors, and the output is data indicating the physiological state.

[0190] Step 3:

[0191] The device sends acquired facial expression data and physiological data to the server. The server receives this data and uses an emotion estimation engine to estimate the user's emotional state. The input is facial expression data and physiological data, and the output is the emotion estimation result.

[0192] Step 4:

[0193] The server inputs prompt text for music generation into the generative AI model based on the emotion estimation results. The generative AI model receives this prompt text and generates music data appropriate to the user's emotions. The input is the prompt text based on the emotion estimation results, and the output is the generated music data.

[0194] Step 5:

[0195] The server sends the generated music data to the terminal. The terminal receives this music data and suggests to the user that they play the music. If the user approves playback, the terminal plays the music and lets the user listen. The input is the generated music data, and the output is the music that is played.

[0196] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0197] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0198] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0199] [Second Embodiment]

[0200] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0201] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0202] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0203] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0204] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0205] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0206] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0207] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0208] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0209] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0210] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0211] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0212] This invention relates to a system that provides optimal music according to the user's emotional state and fatigue level. In this system, the terminal uses a camera mounted on a PC or smartphone to capture the user's facial expressions and posture information, and acquires this data in real time. Furthermore, the terminal also acquires biometric information such as blood pressure and heart rate from a smartwatch worn by the user via communication means such as Bluetooth.

[0213] Next, the device sends all acquired data to a server for analysis. The server uses collaborative filtering and emotion estimation algorithms to estimate the user's emotional state and fatigue level. This includes techniques that capture and analyze subtle changes in facial expressions and posture. The estimated emotional state and fatigue level serve as criteria for selecting the music most suitable for the user.

[0214] The server generates music data using an AI music generation model based on estimated emotional data, combined with the user's past background music playback history. This model has the ability to create music that matches the user's individual preferences and the mood at that time. If the user hums, the voice is recorded in advance on the device and sent to the server as melody information. The server can also use this melody information to generate individual music based on the humming.

[0215] The generated music is sent to the device, which then suggests the song to the user. If the user accepts the suggestion, the music plays on the device. For example, when the user wants to relax, calming piano music is provided, and when they want to feel energized, an upbeat and cheerful song is generated. In this way, the system can customize the music experience in real time according to the user's emotions and state.

[0216] This allows users to regulate their mental state and enrich their daily lives through music optimized for their mood and emotions at any given moment.

[0217] The following describes the processing flow.

[0218] Step 1:

[0219] The device uses the camera on a PC or smartphone to capture the user's facial expressions and posture in real time and acquire data. If the user is wearing a smartwatch, it also acquires biometric information such as blood pressure and heart rate using Bluetooth communication.

[0220] Step 2:

[0221] The device transmits acquired facial expressions, posture, and biometric information to the server. The data is converted to an appropriate format and configured to minimize latency to enable real-time analysis.

[0222] Step 3:

[0223] The server analyzes the received data. First, it uses collaborative filtering and emotion estimation algorithms to analyze the user's facial expressions and posture, and estimate their emotional state and fatigue level. This is achieved by identifying characteristic patterns within the data.

[0224] Step 4:

[0225] The server considers estimated emotional state, fatigue level, and past background music playback history to generate optimal music data using an AI music generation model. This model selects songs that best match the user's preferences and emotions at that moment. If humming is detected, the system also generates music incorporating that melody information.

[0226] Step 5:

[0227] The generated music data is sent to the device. The device then suggests the newly generated music to the user in the form of a notification or pop-up message.

[0228] Step 6:

[0229] If the user approves the suggested music, the device will play the music data. During playback, feedback data regarding the user's impressions and satisfaction can be collected and used to improve the music generation process in the future.

[0230] (Example 1)

[0231] Next, we will describe Example 1. 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."

[0232] With the rapid advancement of information technology, there is a growing demand for services that are tailored to users' emotions and physical states. However, many existing systems have been limited in their ability to accurately understand the user's state and provide appropriate music information. In particular, real-time estimation of emotional states and automatic generation of music that matches individual preferences remain challenges.

[0233] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0234] In this invention, the server includes image acquisition means for acquiring user facial expression data and posture data, communication means for acquiring user physical information, analysis means for estimating the user's emotional state and fatigue level based on the acquired facial expression data, posture data, and physical information, generation means for generating music information by adding past playback history to the estimated emotional state and fatigue level, and provision means for providing the generated music information to the user. This makes it possible to generate and provide music information that is tailored to the user's emotions and physical state.

[0235] "Image acquisition means" refers to devices and methods for acquiring user facial expression data and posture data. These devices and methods make it possible to capture information such as changes in the user's facial expression and body tilt in real time.

[0236] "Communication methods" refer to data communication technologies used to acquire a user's physical information. This includes protocols for wirelessly acquiring data from smart devices and communication technologies between devices.

[0237] "Analysis means" refers to methods and devices for analyzing acquired facial expression data, posture data, and physical information to estimate the user's emotional state and fatigue level. This analysis allows for an effective evaluation of the user's psychological and physiological state.

[0238] "Generation method" refers to the process or technology for generating music information by taking into account estimated emotional state and fatigue level, along with past playback history. This generation function selects or generates music that is optimal for the user's current state.

[0239] "Means of delivery" refers to methods and devices for conveying generated music information to the user. This allows the user to receive and listen to the generated music.

[0240] This invention is a system that generates and provides appropriate music information in real time based on the user's emotional state and fatigue level. The embodiments thereof are described in detail below.

[0241] The device uses a camera to capture the user's facial expressions and posture in real time. This camera is built into the PC or smartphone and uses facial recognition technology to analyze subtle changes in facial expressions and posture. Furthermore, the device uses communication technologies such as Bluetooth to collect physiological data such as heart rate and blood pressure from smart devices.

[0242] Once data is collected, the device sends it to a server. The server processes this data using collaborative filtering and sentiment analysis algorithms to estimate the user's current emotional state and fatigue level.

[0243] Based on the estimation results, the server uses a generation AI model to create music information best suited to the user. The user's past playback history is also considered during the generation process, resulting in individually customized music. For example, the prompt might read, "Currently, the user is relaxed, and their heart rate is stable. Please generate calming music."

[0244] The generated music is returned to the device, which then suggests the song to the user. If the user accepts the suggestion, the music is played through the device, creating a real-time musical experience tailored to the user's emotional state.

[0245] This system allows users to experience psychological relaxation and increased vitality by listening to music tailored to their mood and emotions at the moment. By utilizing the aforementioned hardware and software technologies, the system can function efficiently and effectively.

[0246] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0247] Step 1:

[0248] The device activates the camera built into the PC or smartphone and captures the user's facial expression and posture data in real time. It applies a facial recognition algorithm to capture subtle changes in facial expression and body tilt. The input is video data from the camera, and the output is analyzed facial expression and posture data.

[0249] Step 2:

[0250] The terminal acquires physiological data such as heart rate and blood pressure from a smart device via Bluetooth communication. This allows the terminal to collect data about the user's physical condition. The input is physiological signals from the smart device, and the output is analyzed physiological information.

[0251] Step 3:

[0252] The terminal collects facial expression data, posture data, and physiological information, packages them into packets, and sends them to the server. The data is encrypted before transmission to ensure security. The input consists of the analyzed data, and the output consists of encrypted data packets.

[0253] Step 4:

[0254] The server processes the received data through an analysis algorithm to estimate the user's emotional state and fatigue level. Using collaborative filtering technology, it compares this data with past data to accurately understand the user's psychological state. The input is data packets from the terminal, and the output is the estimated emotional state and fatigue level.

[0255] Step 5:

[0256] The server uses a generation AI model to generate music information, taking into account the estimated emotional state, fatigue level, and the user's past playback history. The input is the emotional state, fatigue level, and playback history, and the prompt is "Current emotion is relaxed, heart rate is stable. Please generate calming music." The output is the generated music file.

[0257] Step 6:

[0258] The server sends the generated music file to the terminal. The terminal suggests the music to the user, and if the user accepts, playback begins. The input is the music file received from the server, and the output is the music experience provided to the user. Here, the terminal briefly describes the content of the music and encourages the user to listen.

[0259] (Application Example 1)

[0260] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0261] Modern consumers are seeking a more comfortable and personalized experience while ordering food and waiting for delivery. However, typical food delivery services lack features that adjust background music according to the user's emotional state and personal preferences, making it difficult to alleviate stress and frustration during waiting times.

[0262] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0263] In this invention, the server includes image capture means for acquiring the user's facial expression and posture information, means for suggesting music suitable for the dish based on the user's order history, and means for generating music data based on the estimated emotional state and fatigue level. This makes it possible to provide music that corresponds to the user's emotional state and order content, enabling a more comfortable and personalized delivery experience.

[0264] A "user" is a consumer who uses the system to place orders or receive music generation services.

[0265] "Facial expression information" refers to data about the user's facial expressions, which is acquired using a camera.

[0266] "Posture information" refers to data about the user's body movements and posture, and, like facial expression information, is acquired by a camera.

[0267] "Image capture means" refers to a shooting device or technology for acquiring user facial expression information and posture information.

[0268] "Emotional state" refers to the user's current state of mind and emotions, and is estimated by the system.

[0269] "Fatigue level" is an indicator that shows the degree of fatigue a user is experiencing, and it is estimated by the system along with their emotional state.

[0270] "Estimation means" refers to technologies and algorithms used to analyze and determine a user's emotional state and fatigue level based on acquired data.

[0271] "Music data" refers to music information provided to the user, and is created by various generation methods.

[0272] "Generation means" refers to a technology or system for creating music data based on estimated emotional states and fatigue levels.

[0273] "Order history" is a record of food and beverage orders that a user has placed in the past.

[0274] "Methods for suggesting music suitable for a dish" refers to technologies and systems that select and present music that is appropriate for a dish based on the user's order history.

[0275] The system that realizes this invention first acquires the user's facial expression information, posture information, and biometric information using a camera and smartwatch on a terminal. A smartphone or tablet camera is used as the image capture means, and biometric information such as heart rate and blood pressure is collected from the smartwatch via Bluetooth communication. This data is transmitted to a server in real time.

[0276] The server uses collaborative filtering and emotion estimation algorithms based on the collected data to estimate the user's emotional state and fatigue level. From these estimation results, a generative AI model is used to generate music data tailored to each individual user. Furthermore, it can analyze the user's order history and suggest music that matches their order.

[0277] For example, when a user orders a pizza, cheerful and fun music can be played to make the waiting time for the food more enjoyable. This allows for a more personalized delivery experience for the user.

[0278] The hardware to be used includes smartphones, tablets, smartwatches, and servers such as Amazon Web Services (AWS). For software, facial recognition algorithms, emotion estimation algorithms, and for the music generation part, the generative AI model of OpenAI is used.

[0279] As a specific example, when the user's current emotional state requires relaxation and the user is ordering Asian cuisine, music is generated using the prompt sentence "Please generate Asian-style relaxing music". This enables music recommendations based on specific order contents.

[0280] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0281] Step 1:

[0282] The terminal uses the camera of the smartphone to capture the user's facial expression information and posture information. In addition, biometric information such as heart rate and blood pressure is obtained through the smartwatch. These data are collected in real time and converted into digital format. In this process, the input is the data of the user's actual facial expressions and biometric states, and the output is the corresponding digital information.

[0283] Step 2:

[0284] The terminal sends the collected digital information to the server. The server executes collaborative filtering and emotion estimation algorithms to analyze the received data. The input is the transferred facial expression information, posture information, and biometric information, and the output is the estimated data of the emotional state and fatigue level.

[0285] Step 3:

[0286] Based on the estimated results of the emotional state and fatigue level, the server refers to the order history of the user's past orders. Then, in order to make music recommendations related to specific food orders, it generates prompt sentences. The input at this stage is the emotional state estimation data and the order history, and the output is the specific prompt sentences for music generation.

[0287] Step 4:

[0288] The server uses the generation AI model to generate music data based on the created prompt sentences. In this step, the input is the music generation prompt sentences, and the output is the generated customized music data.

[0289] Step 5:

[0290] The server transmits the generated music data to the terminal, and the terminal recommends the music to the user. When the user accepts the recommendation, the terminal plays the music. Here, the input is the music data sent from the server, and the output is the provision of the music experience through the terminal.

[0291] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion identification model 59 and perform specific processing using the user's emotions.

[0292] The present invention relates to a system that analyzes the user's emotional state using an emotion engine based on the user's facial expression information and posture information, and generates music data based on this.

[0293] First, the terminal uses the cameras of a PC or smartphone to acquire the user's facial expression and posture information in real time. As a result, specific information related to the user's emotions is captured quickly and accurately. In addition, the terminal acquires biometric information from the smartwatch worn by the user and uses it for a detailed analysis of emotions.

[0294] This acquired information is sent to a server, which uses its built-in emotion engine to recognize and analyze the user's emotional state. The emotion engine analyzes various emotional indicators to determine the user's emotional state. For example, frequent smiles might indicate "happiness," while frequent frowns might indicate "stress."

[0295] Next, the server generates music data using an AI music generation model based on the analyzed emotional data. This model generates music that is most suitable for the user's emotional state. For example, if it is determined that the user needs to relax, calm piano music will be generated.

[0296] Furthermore, if a user provides a hummed tune, the audio data is acquired by the device, and the melody information is sent to the server. Combined with the analysis results of the emotion engine, it is possible to generate music based on this melody information.

[0297] The generated music data is sent to the device, which then suggests songs to the user. If the user accepts the suggestion, the device plays the song. For example, if the user has had a stressful day, the emotion engine can analyze the user's mood and provide relaxing ambient music.

[0298] Thus, the present invention provides music optimized for the user's emotions, offering a musical experience that corresponds to their emotional state at that moment. This contributes to maintaining and improving the user's mental health and enriches their daily life.

[0299] The following describes the processing flow.

[0300] Step 1:

[0301] The device captures the user's facial expressions and posture information in real time using the camera of a PC or smartphone, and acquires this data. Furthermore, the device acquires the user's biometric information via a smartwatch to understand the overall picture.

[0302] Step 2:

[0303] The terminal unifies the acquired expression information, posture information, and biometric information and transmits them to the server as data packets. As a result, the data necessary for analysis immediately reaches the server.

[0304] Step 3:

[0305] The server inputs the received data into the emotion engine. The emotion engine analyzes the specific emotional state from the expression and posture and estimates states such as "happiness", "stress", "fatigue", etc. This analysis includes a process of detecting features in the data using deep learning algorithms and the like.

[0306] Step 4:

[0307] Based on the emotional state estimated by the emotion engine, the server uses the AI music generation model to generate music data. This model selects and generates a piece of music optimal for the user's current mood. For example, when relaxation is needed, it creates a piece of music with a gentle melody.

[0308] Step 5:

[0309] When the user hums, the terminal records the voice and transmits it to the server as melody information. The server generates its own music or composes it as existing music considering this melody information.

[0310] Step 6:

[0311] The generated music data is transmitted to the terminal. The terminal proposes the newly generated music to the user and displays the proposal content on the screen or within the app.

[0312] Step 7:

[0313] The user reviews the music suggestions from the device and starts playback upon acceptance. During music playback, the device may request user feedback, which is sent to the server to improve future suggestions.

[0314] (Example 2)

[0315] Next, we will describe Example 2. 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".

[0316] In modern times, providing music tailored to a user's emotional state contributes to improved mental health and stress reduction. However, existing systems have limitations in utilizing information from facial expressions and posture, and they are unable to fully utilize users' biometric data or personal humming in music generation. The objective of this invention is to develop a system that provides a more accurate and customized musical experience by using diverse user information.

[0317] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0318] In this invention, the server includes information acquisition means for acquiring user facial expression information and posture information, analysis means for analyzing the user's emotional state based on the facial expression information and posture information collected by the information acquisition means, and music generation means for generating music data using a generation AI model based on the analyzed emotional state. This makes it possible to provide a music experience optimized for the user's emotional state in real time.

[0319] "Information acquisition means" refers to devices and methods for collecting information about a user's facial expressions and posture using sensors and cameras, and acquiring it as data.

[0320] "Analysis means" refers to software or algorithms that process data to identify the user's emotional state based on acquired facial expression and posture information.

[0321] A "generative AI model" is a system that utilizes artificial intelligence technology to automatically generate musical patterns and elements that match the user's emotional state.

[0322] "Music generation means" refers to technologies and methods for automatically creating appropriate music based on the results of an analysis of emotional states.

[0323] A "humming processing method" is a technology that has the function of recording a hummed tune by a user and generating a song based on that audio data.

[0324] "Methods for acquiring biometric information" refer to methods for acquiring physiological data such as a user's heart rate and body temperature using smartwatches or other biosensors.

[0325] "Biometric information analysis means" refers to technologies and algorithms that process and associate acquired biological information in order to utilize it for music data generation.

[0326] This invention is a system that generates music data according to the user's emotional state. A specific embodiment of this system is described below.

[0327] The device uses cameras and sensors on a PC or smartphone to acquire the user's facial expressions and posture information. By using facial recognition software, it can accurately monitor changes in the user's facial expressions in real time. In addition, the device uses wearable devices such as smartwatches to acquire biometric information such as the user's heart rate and body temperature. This allows for the collection of data to analyze the user's emotional state in more detail.

[0328] The information acquired by the device is transmitted to the server via the network. The server is equipped with an emotion engine to analyze this information, estimating the user's emotional state by analyzing facial expressions, posture information, and biometric information. Because the emotion engine utilizes neural network technology, it can distinguish multiple emotional indicators with high accuracy.

[0329] The server uses a generative AI model based on the analyzed emotional state. This generative AI model generates music data that matches the emotional state, and its style and atmosphere can be altered. An example of a prompt would be, "Generate music suitable for a relaxing evening." By entering such prompts, the server automatically generates music appropriate for the user.

[0330] Furthermore, if a user provides a hummed tune, the device acquires the audio data and sends it to the server. The server can then combine the results of the emotion engine's analysis with the melody information of this hummed tune to perform its own music generation.

[0331] The generated music data is sent back from the server to the terminal, which then suggests music to the user. If the user approves the suggestion, the terminal plays the music. For example, on a day when the user is feeling stressed, relaxing music may be provided, contributing to the improvement of the user's mental health.

[0332] This system will enable a more personalized music experience that responds to the user's real-time emotional state.

[0333] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0334] Step 1:

[0335] The device uses the camera of a PC or smartphone to acquire the user's facial expressions and posture information in real time. Using facial recognition software, it detects characteristic points of facial expressions and changes in posture, and records this as digital data. It receives camera video data as input and generates analyzable facial and posture data as output.

[0336] Step 2:

[0337] The device acquires biometric information such as heart rate and body temperature from the smartwatch. A dedicated health monitoring application retrieves this information and records it as digital data representing the user's physical state. It receives biometric measurement data from the smartwatch as input and generates analyzable biometric data as output.

[0338] Step 3:

[0339] The device encrypts the data acquired in Step 1 and Step 2 and sends it to the server over the network. This ensures that data is transferred securely while maintaining privacy. It receives facial expressions, posture, and biometric data as input and outputs them as data to be sent to the server.

[0340] Step 4:

[0341] The server feeds the received data into the emotion engine and begins analysis. The emotion engine uses neural network technology to estimate the user's emotional state. It receives data on facial expressions, posture, and biometrics as input and generates evaluation data regarding the emotional state as output.

[0342] Step 5:

[0343] The server, based on the analysis results of the emotion engine, inputs appropriate prompts into the generative AI model and generates music data. The generative AI model creates music best suited to the user based on prompts such as "Generate music with a relaxing atmosphere." It receives emotional state evaluation data as input and generates music data as output.

[0344] Step 6:

[0345] The terminal receives music data from the server and presents it to the user through an interface that suggests music. If the user approves the suggested music, the terminal plays the song. It receives generated music data as input and plays music as output.

[0346] (Application Example 2)

[0347] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0348] In modern society, providing entertainment that responds immediately to changes in an individual's emotional state is a crucial element in improving the quality of life. However, conventional music playback systems lack the means to analyze a user's emotions and physiological state in real time and provide music that suits them. As a result, users cannot easily obtain music that matches their emotions and physiological state at any given time, and there is a lack of effective support to alleviate stress and anxiety.

[0349] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0350] In this invention, the server includes image acquisition means for acquiring user facial expression data and posture data, estimation means for estimating the user's emotional state based on the acquired facial expression data and posture data, generation means for generating music data based on the estimated emotional state, and means for incorporating pre-acquired physiological data of the user into the music data generation. This enables the user to instantly receive music that suits their emotional state, thereby enhancing daily emotional support.

[0351] "User facial expression data" is digital information obtained by analyzing the features of a user's face, and it serves as basic data for estimating their emotional state.

[0352] "Posture data" refers to information that indicates the position and angle of the user's body, and is used to understand the user's psychological state.

[0353] "Image acquisition means" refers to a device that uses a camera or other imaging device to capture the user's actions and appearance, and converts them into analyzable digital data.

[0354] "Estimation method" is a term that refers to the methods and processes used to identify a user's internal state by performing analysis based on acquired data.

[0355] "Generation means" refers to a method or process for creating an intended output based on predetermined input information, and in this case, it refers to generating music data.

[0356] "Physiological data" refers to digital information that indicates a user's physical function and state, such as heart rate and activity level. This data is an important element in estimating emotional states.

[0357] A "mobile communication terminal" refers to electronic devices such as mobile phones and smartphones that can communicate without being tied to a specific location.

[0358] "Voice-based melodic information" refers to information obtained by acquiring a melody spoken by a user as audio data and converting it into an analyzable format.

[0359] This invention provides a system for generating and playing music based on a user's emotional state. This system includes a server for acquiring and analyzing facial expression data, posture data, and physiological data, centered on the user's mobile communication terminal, and an interface for providing the generated music to the user.

[0360] The server analyzes facial expression data obtained through the user's mobile communication terminal's camera. Specifically, it uses facial expression recognition software (e.g., OpenCV) with the facial images acquired from the camera to understand the user's emotional state. Furthermore, the terminal acquires physiological data such as heart rate from wearable devices such as smartwatches.

[0361] The analyzed data is sent to an emotion estimation engine, which identifies the emotional state. For example, if the user is smiling, it is determined to be in a happy mood. This emotional state serves as the basis for creating music data using a generative AI model. The generative AI model (e.g., OpenAI's music generation model) generates the most suitable song according to this emotional information.

[0362] The generated music is sent to the user's device and presented to the user by a music playback application. If the user accepts it, the device plays the music and works to guide the user's emotional state in a positive direction.

[0363] As a concrete example, let's say a user uses the app on a Friday afternoon, wanting to relax. The app analyzes the user's calm facial expression and obtains data indicating a calm heart rate. The system determines that the user is in a "relaxed state" and prompts the AI ​​model with the message, "You need to relax, generate calming music," which then generates a suitable piano piece. In this way, the system can provide the user with an optimal musical experience in real time.

[0364] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0365] Step 1:

[0366] The device uses a camera to acquire the user's facial expression data in real time. Based on the acquired image data, an expression recognition algorithm analyzes features associated with specific emotions, such as smiles and frown lines. The input is image data from the camera, and the output is data indicating the emotional state.

[0367] Step 2:

[0368] The device acquires physiological data such as heart rate and activity level from wearable devices such as smartwatches. This allows the user's physiological state to be understood. The input is physiological data from sensors, and the output is data indicating the physiological state.

[0369] Step 3:

[0370] The device sends acquired facial expression data and physiological data to the server. The server receives this data and uses an emotion estimation engine to estimate the user's emotional state. The input is facial expression data and physiological data, and the output is the emotion estimation result.

[0371] Step 4:

[0372] The server inputs prompt text for music generation into the generative AI model based on the emotion estimation results. The generative AI model receives this prompt text and generates music data appropriate to the user's emotions. The input is the prompt text based on the emotion estimation results, and the output is the generated music data.

[0373] Step 5:

[0374] The server sends the generated music data to the terminal. The terminal receives this music data and suggests to the user that they play the music. If the user approves playback, the terminal plays the music and lets the user listen. The input is the generated music data, and the output is the music that is played.

[0375] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0376] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0377] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0378] [Third Embodiment]

[0379] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0380] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0381] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0382] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0383] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0384] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0385] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0386] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0387] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0388] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0389] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0390] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0391] This invention relates to a system that provides optimal music according to the user's emotional state and fatigue level. In this system, the terminal uses a camera mounted on a PC or smartphone to capture the user's facial expressions and posture information, and acquires this data in real time. Furthermore, the terminal also acquires biometric information such as blood pressure and heart rate from a smartwatch worn by the user via communication means such as Bluetooth.

[0392] Next, the device sends all acquired data to a server for analysis. The server uses collaborative filtering and emotion estimation algorithms to estimate the user's emotional state and fatigue level. This includes techniques that capture and analyze subtle changes in facial expressions and posture. The estimated emotional state and fatigue level serve as criteria for selecting the music most suitable for the user.

[0393] The server generates music data using an AI music generation model based on estimated emotional data, combined with the user's past background music playback history. This model has the ability to create music that matches the user's individual preferences and the mood at that time. If the user hums, the voice is recorded in advance on the device and sent to the server as melody information. The server can also use this melody information to generate individual music based on the humming.

[0394] The generated music is sent to the device, which then suggests the song to the user. If the user accepts the suggestion, the music plays on the device. For example, when the user wants to relax, calming piano music is provided, and when they want to feel energized, an upbeat and cheerful song is generated. In this way, the system can customize the music experience in real time according to the user's emotions and state.

[0395] This allows users to regulate their mental state and enrich their daily lives through music optimized for their mood and emotions at any given moment.

[0396] The following describes the processing flow.

[0397] Step 1:

[0398] The device uses the camera on a PC or smartphone to capture the user's facial expressions and posture in real time and acquire data. If the user is wearing a smartwatch, it also acquires biometric information such as blood pressure and heart rate using Bluetooth communication.

[0399] Step 2:

[0400] The device transmits acquired facial expressions, posture, and biometric information to the server. The data is converted to an appropriate format and configured to minimize latency to enable real-time analysis.

[0401] Step 3:

[0402] The server analyzes the received data. First, it uses collaborative filtering and emotion estimation algorithms to analyze the user's facial expressions and posture, and estimate their emotional state and fatigue level. This is achieved by identifying characteristic patterns within the data.

[0403] Step 4:

[0404] The server considers estimated emotional state, fatigue level, and past background music playback history to generate optimal music data using an AI music generation model. This model selects songs that best match the user's preferences and emotions at that moment. If humming is detected, the system also generates music incorporating that melody information.

[0405] Step 5:

[0406] The generated music data is sent to the device. The device then suggests the newly generated music to the user in the form of a notification or pop-up message.

[0407] Step 6:

[0408] If the user approves the suggested music, the device will play the music data. During playback, feedback data regarding the user's impressions and satisfaction can be collected and used to improve the music generation process in the future.

[0409] (Example 1)

[0410] Next, we will describe Example 1. 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."

[0411] With the rapid advancement of information technology, there is a growing demand for services that are tailored to users' emotions and physical states. However, many existing systems have been limited in their ability to accurately understand the user's state and provide appropriate music information. In particular, real-time estimation of emotional states and automatic generation of music that matches individual preferences remain challenges.

[0412] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0413] In this invention, the server includes image acquisition means for acquiring user facial expression data and posture data, communication means for acquiring user physical information, analysis means for estimating the user's emotional state and fatigue level based on the acquired facial expression data, posture data, and physical information, generation means for generating music information by adding past playback history to the estimated emotional state and fatigue level, and provision means for providing the generated music information to the user. This makes it possible to generate and provide music information that is tailored to the user's emotions and physical state.

[0414] "Image acquisition means" refers to devices and methods for acquiring user facial expression data and posture data. These devices and methods make it possible to capture information such as changes in the user's facial expression and body tilt in real time.

[0415] "Communication methods" refer to data communication technologies used to acquire a user's physical information. This includes protocols for wirelessly acquiring data from smart devices and communication technologies between devices.

[0416] "Analysis means" refers to methods and devices for analyzing acquired facial expression data, posture data, and physical information to estimate the user's emotional state and fatigue level. This analysis allows for an effective evaluation of the user's psychological and physiological state.

[0417] "Generation method" refers to the process or technology for generating music information by taking into account the estimated emotional state and fatigue level, along with past playback history. This generation function selects or generates music that is optimal for the user's current state.

[0418] "Means of delivery" refers to methods and devices for conveying generated music information to the user. This allows the user to receive and listen to the generated music.

[0419] This invention is a system that generates and provides appropriate music information in real time based on the user's emotional state and fatigue level. The embodiments thereof are described in detail below.

[0420] The device uses a camera to capture the user's facial expressions and posture in real time. This camera is built into the PC or smartphone and uses facial recognition technology to analyze subtle changes in facial expressions and posture. Furthermore, the device uses communication technologies such as Bluetooth to collect physiological data such as heart rate and blood pressure from smart devices.

[0421] Once data is collected, the device sends it to a server. The server processes this data using collaborative filtering and sentiment analysis algorithms to estimate the user's current emotional state and fatigue level.

[0422] Based on the estimation results, the server uses a generation AI model to create music information best suited to the user. The user's past playback history is also considered during the generation process, resulting in individually customized music. For example, the prompt might read, "Currently, the user is relaxed, and their heart rate is stable. Please generate calming music."

[0423] The generated music is returned to the device, which then suggests the song to the user. If the user accepts the suggestion, the music is played through the device, creating a real-time musical experience tailored to the user's emotional state.

[0424] This system allows users to experience psychological relaxation and increased vitality by listening to music tailored to their mood and emotions at the moment. By utilizing the aforementioned hardware and software technologies, the system can function efficiently and effectively.

[0425] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0426] Step 1:

[0427] The device activates the camera built into the PC or smartphone and captures the user's facial expression and posture data in real time. It applies a facial recognition algorithm to capture subtle changes in facial expression and body tilt. The input is video data from the camera, and the output is analyzed facial expression and posture data.

[0428] Step 2:

[0429] The terminal acquires physiological data such as heart rate and blood pressure from a smart device via Bluetooth communication. This allows the terminal to collect data about the user's physical condition. The input is physiological signals from the smart device, and the output is analyzed physiological information.

[0430] Step 3:

[0431] The terminal collects facial expression data, posture data, and physiological information, packages them into packets, and sends them to the server. The data is encrypted before transmission to ensure security. The input consists of the analyzed data, and the output consists of encrypted data packets.

[0432] Step 4:

[0433] The server processes the received data through an analysis algorithm to estimate the user's emotional state and fatigue level. Using collaborative filtering technology, it compares this data with past data to accurately understand the user's psychological state. The input is data packets from the terminal, and the output is the estimated emotional state and fatigue level.

[0434] Step 5:

[0435] The server uses a generation AI model to generate music information, taking into account the estimated emotional state, fatigue level, and the user's past playback history. The input is the emotional state, fatigue level, and playback history, and the prompt is "Current emotion is relaxed, heart rate is stable. Please generate calming music." The output is the generated music file.

[0436] Step 6:

[0437] The server sends the generated music file to the terminal. The terminal suggests the music to the user, and if the user accepts, playback begins. The input is the music file received from the server, and the output is the music experience provided to the user. Here, the terminal briefly describes the content of the music and encourages the user to listen.

[0438] (Application Example 1)

[0439] Next, we will explain Application Example 1. In the following explanation, 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."

[0440] Modern consumers are seeking a more comfortable and personalized experience while ordering food and waiting for delivery. However, typical food delivery services lack features that adjust background music according to the user's emotional state and personal preferences, making it difficult to alleviate stress and frustration during waiting times.

[0441] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0442] In this invention, the server includes image capture means for acquiring the user's facial expression and posture information, means for suggesting music suitable for the dish based on the user's order history, and means for generating music data based on the estimated emotional state and fatigue level. This makes it possible to provide music that corresponds to the user's emotional state and order content, enabling a more comfortable and personalized delivery experience.

[0443] A "user" is a consumer who uses the system to place orders or receive music generation services.

[0444] "Facial expression information" refers to data about the user's facial expressions, which is acquired using a camera.

[0445] "Posture information" refers to data about the user's body movements and posture, and, like facial expression information, is acquired by a camera.

[0446] "Image capture means" refers to a shooting device or technology for acquiring user facial expression information and posture information.

[0447] "Emotional state" refers to the user's current state of mind and emotions, and is estimated by the system.

[0448] "Fatigue level" is an indicator that shows the degree of fatigue a user is experiencing, and it is estimated by the system along with their emotional state.

[0449] "Estimation means" refers to technologies and algorithms used to analyze and determine a user's emotional state and fatigue level based on acquired data.

[0450] "Music data" refers to music information provided to the user, and is created by various generation methods.

[0451] "Generation means" refers to a technology or system for creating music data based on estimated emotional states and fatigue levels.

[0452] "Order history" is a record of food and beverage orders that a user has placed in the past.

[0453] "Methods for suggesting music suitable for a dish" refer to technologies and systems that select and present music that is appropriate for a dish based on the user's order history.

[0454] The system that realizes this invention first acquires the user's facial expression information, posture information, and biometric information using a camera and smartwatch on a terminal. A smartphone or tablet camera is used as the image capture means, and biometric information such as heart rate and blood pressure is collected from the smartwatch via Bluetooth communication. This data is transmitted to a server in real time.

[0455] The server uses collaborative filtering and emotion estimation algorithms based on the collected data to estimate the user's emotional state and fatigue level. From these estimation results, a generative AI model is used to generate music data tailored to each individual user. Furthermore, it can analyze the user's order history and suggest music that matches their order.

[0456] For example, when a user orders a pizza, cheerful and fun music can be played to make the waiting time for the food more enjoyable. This allows for a more personalized delivery experience for the user.

[0457] The hardware used includes smartphones, tablets, and smartwatches, while the servers utilize Amazon Web Services (AWS). The software employs facial recognition algorithms, emotion estimation algorithms, and OpenAI's generative AI models for music generation.

[0458] For example, if a user's current emotional state requires relaxation, and they have ordered Asian food, the system might use the prompt "Generate relaxing Asian music" to generate music. This allows for music suggestions based on specific order details.

[0459] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0460] Step 1:

[0461] The device uses the smartphone's camera to capture the user's facial expressions and posture. In addition, it acquires biometric information such as heart rate and blood pressure through a smartwatch. This data is collected in real time and converted into a digital format. In this process, the input is data of the user's actual facial expressions and biometric state, and the output is the corresponding digital information.

[0462] Step 2:

[0463] The terminal transmits the collected digital information to the server. The server performs collaborative filtering and emotion estimation algorithms to analyze the received data. The inputs are transmitted facial expression information, posture information, and biometric information, and the outputs are estimated data of emotional state and fatigue level.

[0464] Step 3:

[0465] The server references the user's past order history based on estimated emotional state and fatigue levels. It then generates prompt messages to suggest music related to specific food orders. At this stage, the inputs are the emotional state estimation data and order history, while the output is the specific prompt message for music generation.

[0466] Step 4:

[0467] The server uses a generative AI model to generate music data based on the created prompt sentences. In this step, the input is the music generation prompt sentences, and the output is the generated customized music data.

[0468] Step 5:

[0469] The server sends the generated music data to the terminal, and the terminal suggests music to the user. If the user accepts the suggestion, the music is played. Here, the input is the music data sent from the server, and the output is the provision of a music experience through the terminal.

[0470] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0471] This invention relates to a system that analyzes a user's emotional state using an emotion engine based on the user's facial expression and posture information, and generates music data based on that analysis.

[0472] First, the device uses the camera on a PC or smartphone to acquire the user's facial expressions and posture information in real time. This allows for the rapid and accurate capture of specific information related to the user's emotions. The device also acquires biometric information from the smartwatch the user is wearing, which is used for detailed emotional analysis.

[0473] This acquired information is sent to a server, which uses its built-in emotion engine to recognize and analyze the user's emotional state. The emotion engine analyzes various emotional indicators to determine the user's emotional state. For example, frequent smiles might indicate "happiness," while frequent frowns might indicate "stress."

[0474] Next, the server generates music data using an AI music generation model based on the analyzed emotional data. This model generates music that is most suitable for the user's emotional state. For example, if it is determined that the user needs to relax, calm piano music will be generated.

[0475] Furthermore, if a user provides a hummed tune, the audio data is acquired by the device, and the melody information is sent to the server. Combined with the analysis results of the emotion engine, it is possible to generate music based on this melody information.

[0476] The generated music data is sent to the device, which then suggests songs to the user. If the user accepts the suggestion, the device plays the song. For example, if the user has had a stressful day, the emotion engine can analyze the user's mood and provide relaxing ambient music.

[0477] Thus, the present invention provides music optimized for the user's emotions, offering a musical experience that corresponds to their emotional state at that moment. This contributes to maintaining and improving the user's mental health and enriches their daily life.

[0478] The following describes the processing flow.

[0479] Step 1:

[0480] The device captures the user's facial expressions and posture information in real time using the camera of a PC or smartphone, and acquires this data. Furthermore, the device acquires the user's biometric information via a smartwatch to understand the overall picture.

[0481] Step 2:

[0482] The device centralizes the acquired facial expression, posture, and biometric information and sends it to the server as a data packet. This ensures that the data necessary for analysis reaches the server immediately.

[0483] Step 3:

[0484] The server inputs the received data into the emotion engine. The emotion engine analyzes specific emotional states from facial expressions and posture, and estimates states such as "happiness," "stress," and "fatigue." This analysis includes a process of detecting features in the data using deep learning algorithms.

[0485] Step 4:

[0486] The server generates music data using an AI music generation model based on the emotional state estimated by the emotion engine. This model selects and generates music that is best suited to the user's current mood; for example, it creates music with a calming melody when relaxation is needed.

[0487] Step 5:

[0488] When a user hums, the device records the sound and sends it to the server as melody information. The server then uses this melody information to either generate a new song or compose it as existing music.

[0489] Step 6:

[0490] The generated music data is sent to the device. The device then suggests the newly generated music to the user and displays the suggestions via screen notifications or within the app.

[0491] Step 7:

[0492] The user reviews the music suggestions from the device and starts playback upon acceptance. During music playback, the device may request user feedback, which is sent to the server to improve future suggestions.

[0493] (Example 2)

[0494] Next, we will describe Example 2. 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."

[0495] In modern times, providing music tailored to a user's emotional state contributes to improved mental health and stress reduction. However, existing systems have limitations in utilizing information from facial expressions and posture, and they are unable to fully utilize users' biometric data or personal humming in music generation. The objective of this invention is to develop a system that provides a more accurate and customized musical experience by using diverse user information.

[0496] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0497] In this invention, the server includes information acquisition means for acquiring user facial expression information and posture information, analysis means for analyzing the user's emotional state based on the facial expression information and posture information collected by the information acquisition means, and music generation means for generating music data using a generation AI model based on the analyzed emotional state. This makes it possible to provide a music experience optimized for the user's emotional state in real time.

[0498] "Information acquisition means" refers to devices and methods for collecting information about a user's facial expressions and posture using sensors and cameras, and acquiring it as data.

[0499] "Analysis means" refers to software or algorithms that process data to identify the user's emotional state based on acquired facial expression and posture information.

[0500] A "generative AI model" is a system that utilizes artificial intelligence technology to automatically generate musical patterns and elements that match the user's emotional state.

[0501] "Music generation means" refers to technologies and methods for automatically creating appropriate music based on the results of an analysis of emotional states.

[0502] A "humming processing method" is a technology that has the function of recording a hummed tune by a user and generating a song based on that audio data.

[0503] "Methods for acquiring biometric information" refer to methods for acquiring physiological data such as a user's heart rate and body temperature using smartwatches or other biosensors.

[0504] "Biometric information analysis means" refers to technologies and algorithms that process and associate acquired biological information in order to utilize it for music data generation.

[0505] This invention is a system that generates music data according to the user's emotional state. A specific embodiment of this system is described below.

[0506] The device uses cameras and sensors on a PC or smartphone to acquire the user's facial expressions and posture information. By using facial recognition software, it can accurately monitor changes in the user's facial expressions in real time. In addition, the device uses wearable devices such as smartwatches to acquire biometric information such as the user's heart rate and body temperature. This allows for the collection of data to analyze the user's emotional state in more detail.

[0507] The information acquired by the device is transmitted to the server via the network. The server is equipped with an emotion engine to analyze this information, estimating the user's emotional state by analyzing facial expressions, posture information, and biometric information. Because the emotion engine utilizes neural network technology, it can distinguish multiple emotional indicators with high accuracy.

[0508] The server uses a generative AI model based on the analyzed emotional state. This generative AI model generates music data that matches the emotional state, and its style and atmosphere can be altered. An example of a prompt would be, "Generate music suitable for a relaxing evening." By entering such prompts, the server automatically generates music appropriate for the user.

[0509] Furthermore, if a user provides a hummed tune, the device acquires the audio data and sends it to the server. The server can then combine the results of the emotion engine's analysis with the melody information of this hummed tune to perform its own music generation.

[0510] The generated music data is sent back from the server to the terminal, which then suggests music to the user. If the user approves the suggestion, the terminal plays the music. For example, on a day when the user is feeling stressed, relaxing music may be provided, contributing to the improvement of the user's mental health.

[0511] This system will enable a more personalized music experience that responds to the user's real-time emotional state.

[0512] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0513] Step 1:

[0514] The device uses the camera of a PC or smartphone to acquire the user's facial expressions and posture information in real time. Using facial recognition software, it detects characteristic points of facial expressions and changes in posture, and records this as digital data. It receives camera video data as input and generates analyzable facial and posture data as output.

[0515] Step 2:

[0516] The device acquires biometric information such as heart rate and body temperature from the smartwatch. A dedicated health monitoring application retrieves this information and records it as digital data representing the user's physical state. It receives biometric measurement data from the smartwatch as input and generates analyzable biometric data as output.

[0517] Step 3:

[0518] The device encrypts the data acquired in Step 1 and Step 2 and sends it to the server over the network. This ensures that data is transferred securely while maintaining privacy. It receives facial expressions, posture, and biometric data as input and outputs them as data to be sent to the server.

[0519] Step 4:

[0520] The server feeds the received data into the emotion engine and begins analysis. The emotion engine uses neural network technology to estimate the user's emotional state. It receives data on facial expressions, posture, and biometrics as input and generates evaluation data regarding the emotional state as output.

[0521] Step 5:

[0522] The server, based on the analysis results of the emotion engine, inputs appropriate prompts into the generative AI model and generates music data. The generative AI model creates music best suited to the user based on prompts such as "Generate music with a relaxing atmosphere." It receives emotional state evaluation data as input and generates music data as output.

[0523] Step 6:

[0524] The terminal receives music data from the server and presents it to the user through an interface that suggests music. If the user approves the suggested music, the terminal plays the song. It receives generated music data as input and plays music as output.

[0525] (Application Example 2)

[0526] Next, we will explain application example 2. In the following explanation, 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."

[0527] In modern society, providing entertainment that responds immediately to changes in an individual's emotional state is a crucial element in improving the quality of life. However, conventional music playback systems lack the means to analyze a user's emotions and physiological state in real time and provide music that suits them. As a result, users cannot easily obtain music that matches their emotions and physiological state at any given time, and there is a lack of effective support to alleviate stress and anxiety.

[0528] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0529] In this invention, the server includes image acquisition means for acquiring user facial expression data and posture data, estimation means for estimating the user's emotional state based on the acquired facial expression data and posture data, generation means for generating music data based on the estimated emotional state, and means for incorporating pre-acquired physiological data of the user into the music data generation. This enables the user to instantly receive music that suits their emotional state, thereby enhancing daily emotional support.

[0530] "User facial expression data" is digital information obtained by analyzing the features of a user's face, and it serves as basic data for estimating their emotional state.

[0531] "Posture data" refers to information that indicates the position and angle of the user's body, and is used to understand the user's psychological state.

[0532] "Image acquisition means" refers to a device that uses a camera or other imaging device to capture the user's actions and appearance, and converts them into analyzable digital data.

[0533] "Estimation method" is a term that refers to the methods and processes used to identify a user's internal state by performing analysis based on acquired data.

[0534] "Generation means" refers to a method or process for creating an intended output based on predetermined input information, and in this case, it refers to generating music data.

[0535] "Physiological data" refers to digital information that indicates a user's physical function and state, such as heart rate and activity level. This data is an important element in estimating emotional states.

[0536] A "mobile communication terminal" refers to electronic devices such as mobile phones and smartphones that can communicate without being tied to a specific location.

[0537] "Voice-based melodic information" refers to information obtained by acquiring a melody spoken by a user as audio data and converting it into an analyzable format.

[0538] This invention provides a system for generating and playing music based on a user's emotional state. This system includes a server for acquiring and analyzing facial expression data, posture data, and physiological data, centered on the user's mobile communication terminal, and an interface for providing the generated music to the user.

[0539] The server analyzes facial expression data obtained through the user's mobile communication terminal's camera. Specifically, it uses facial expression recognition software (e.g., OpenCV) with the facial images acquired from the camera to understand the user's emotional state. Furthermore, the terminal acquires physiological data such as heart rate from wearable devices such as smartwatches.

[0540] The analyzed data is sent to an emotion estimation engine, which identifies the emotional state. For example, if the user is smiling, it is determined to be in a happy mood. This emotional state serves as the basis for creating music data using a generative AI model. The generative AI model (e.g., OpenAI's music generation model) generates the most suitable song according to this emotional information.

[0541] The generated music is sent to the user's device and presented to the user by a music playback application. If the user accepts it, the device plays the music and works to guide the user's emotional state in a positive direction.

[0542] As a concrete example, let's say a user uses the app on a Friday afternoon, wanting to relax. The app analyzes the user's calm facial expression and obtains data indicating a calm heart rate. The system determines that the user is in a "relaxed state" and prompts the AI ​​model with the message, "You need to relax, generate calming music," which then generates a suitable piano piece. In this way, the system can provide the user with an optimal musical experience in real time.

[0543] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0544] Step 1:

[0545] The device uses a camera to acquire real-time data on the user's facial expressions. Based on the acquired image data, an expression recognition algorithm analyzes features associated with specific emotions, such as smiles and frown lines. The input is image data from the camera, and the output is data indicating the emotional state.

[0546] Step 2:

[0547] The device acquires physiological data such as heart rate and activity level from wearable devices such as smartwatches. This allows the user's physiological state to be understood. The input is physiological data from sensors, and the output is data indicating the physiological state.

[0548] Step 3:

[0549] The device sends acquired facial expression data and physiological data to the server. The server receives this data and uses an emotion estimation engine to estimate the user's emotional state. The input is facial expression data and physiological data, and the output is the emotion estimation result.

[0550] Step 4:

[0551] The server inputs prompt text for music generation into the generative AI model based on the emotion estimation results. The generative AI model receives this prompt text and generates music data appropriate to the user's emotions. The input is the prompt text based on the emotion estimation results, and the output is the generated music data.

[0552] Step 5:

[0553] The server sends the generated music data to the terminal. The terminal receives this music data and suggests to the user that they play the music. If the user approves playback, the terminal plays the music and lets the user listen. The input is the generated music data, and the output is the music that is played.

[0554] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0555] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0556] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0557] [Fourth Embodiment]

[0558] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0559] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0560] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0561] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0562] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0563] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0564] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0565] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0566] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0567] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0568] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0569] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0570] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0571] This invention relates to a system that provides optimal music according to the user's emotional state and fatigue level. In this system, the terminal uses a camera mounted on a PC or smartphone to capture the user's facial expressions and posture information, and acquires this data in real time. Furthermore, the terminal also acquires biometric information such as blood pressure and heart rate from a smartwatch worn by the user via communication means such as Bluetooth.

[0572] Next, the device sends all acquired data to a server for analysis. The server uses collaborative filtering and emotion estimation algorithms to estimate the user's emotional state and fatigue level. This includes techniques that capture and analyze subtle changes in facial expressions and posture. The estimated emotional state and fatigue level serve as criteria for selecting the music most suitable for the user.

[0573] The server generates music data using an AI music generation model based on estimated emotional data, combined with the user's past background music playback history. This model has the ability to create music that matches the user's individual preferences and the mood at that time. If the user hums, the voice is recorded in advance on the device and sent to the server as melody information. The server can also use this melody information to generate individual music based on the humming.

[0574] The generated music is sent to the device, which then suggests the song to the user. If the user accepts the suggestion, the music plays on the device. For example, when the user wants to relax, calming piano music is provided, and when they want to feel energized, an upbeat and cheerful song is generated. In this way, the system can customize the music experience in real time according to the user's emotions and state.

[0575] This allows users to regulate their mental state and enrich their daily lives through music optimized for their mood and emotions at any given moment.

[0576] The following describes the processing flow.

[0577] Step 1:

[0578] The device uses the camera on a PC or smartphone to capture the user's facial expressions and posture in real time and acquire data. If the user is wearing a smartwatch, it also acquires biometric information such as blood pressure and heart rate using Bluetooth communication.

[0579] Step 2:

[0580] The device transmits acquired facial expressions, posture, and biometric information to the server. The data is converted to an appropriate format and configured to minimize latency to enable real-time analysis.

[0581] Step 3:

[0582] The server analyzes the received data. First, it uses collaborative filtering and emotion estimation algorithms to analyze the user's facial expressions and posture, and estimate their emotional state and fatigue level. This is achieved by identifying characteristic patterns within the data.

[0583] Step 4:

[0584] The server considers estimated emotional state, fatigue level, and past background music playback history to generate optimal music data using an AI music generation model. This model selects songs that best match the user's preferences and emotions at that moment. Furthermore, if humming is detected, the system also generates music incorporating that melody information.

[0585] Step 5:

[0586] The generated music data is sent to the device. The device then suggests the newly generated music to the user in the form of a notification or pop-up message.

[0587] Step 6:

[0588] If the user approves the suggested music, the device will play the music data. During playback, feedback data regarding the user's impressions and satisfaction can be collected and used to improve the music generation process in the future.

[0589] (Example 1)

[0590] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0591] With the rapid advancement of information technology, there is a growing demand for services that are tailored to users' emotions and physical states. However, many existing systems have been limited in their ability to accurately understand the user's state and provide appropriate music information. In particular, real-time estimation of emotional states and automatic generation of music that matches individual preferences remain challenges.

[0592] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0593] In this invention, the server includes image acquisition means for acquiring user facial expression data and posture data, communication means for acquiring user physical information, analysis means for estimating the user's emotional state and fatigue level based on the acquired facial expression data, posture data, and physical information, generation means for generating music information by adding past playback history to the estimated emotional state and fatigue level, and provision means for providing the generated music information to the user. This makes it possible to generate and provide music information that is tailored to the user's emotions and physical state.

[0594] "Image acquisition means" refers to devices and methods for acquiring user facial expression data and posture data. These devices and methods make it possible to capture information such as changes in the user's facial expression and body tilt in real time.

[0595] "Communication methods" refer to data communication technologies used to acquire a user's physical information. This includes protocols for wirelessly acquiring data from smart devices and communication technologies between devices.

[0596] "Analysis means" refers to methods and devices for analyzing acquired facial expression data, posture data, and physical information to estimate the user's emotional state and fatigue level. This analysis allows for an effective evaluation of the user's psychological and physiological state.

[0597] "Generation method" refers to the process or technology for generating music information by taking into account the estimated emotional state and fatigue level, along with past playback history. This generation function selects or generates music that is optimal for the user's current state.

[0598] "Means of delivery" refers to methods and devices for conveying generated music information to the user. This allows the user to receive and listen to the generated music.

[0599] This invention is a system that generates and provides appropriate music information in real time based on the user's emotional state and fatigue level. The embodiments thereof are described in detail below.

[0600] The device uses a camera to capture the user's facial expressions and posture in real time. This camera is built into the PC or smartphone and uses facial recognition technology to analyze subtle changes in facial expressions and posture. Furthermore, the device uses communication technologies such as Bluetooth to collect physiological data such as heart rate and blood pressure from smart devices.

[0601] Once data is collected, the device sends it to a server. The server processes this data using collaborative filtering and sentiment analysis algorithms to estimate the user's current emotional state and fatigue level.

[0602] Based on the estimation results, the server uses a generation AI model to create music information best suited to the user. The user's past playback history is also considered during the generation process, resulting in individually customized music. For example, the prompt might read, "Currently, the user is relaxed, and their heart rate is stable. Please generate calming music."

[0603] The generated music is returned to the device, which then suggests the song to the user. If the user accepts the suggestion, the music is played through the device, creating a real-time musical experience tailored to the user's emotional state.

[0604] This system allows users to experience psychological relaxation and increased vitality by listening to music tailored to their mood and emotions at the moment. By utilizing the aforementioned hardware and software technologies, the system can function efficiently and effectively.

[0605] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0606] Step 1:

[0607] The device activates the camera built into the PC or smartphone and captures the user's facial expression and posture data in real time. It applies a facial recognition algorithm to capture subtle changes in facial expression and body tilt. The input is video data from the camera, and the output is analyzed facial expression and posture data.

[0608] Step 2:

[0609] The terminal acquires physiological data such as heart rate and blood pressure from a smart device via Bluetooth communication. This allows the terminal to collect data about the user's physical condition. The input is physiological signals from the smart device, and the output is analyzed physiological information.

[0610] Step 3:

[0611] The terminal collects facial expression data, posture data, and physiological information, packages them into packets, and sends them to the server. The data is encrypted before transmission to ensure security. The input consists of the analyzed data, and the output consists of encrypted data packets.

[0612] Step 4:

[0613] The server processes the received data through an analysis algorithm to estimate the user's emotional state and fatigue level. Using collaborative filtering technology, it compares this data with past data to accurately understand the user's psychological state. The input is data packets from the terminal, and the output is the estimated emotional state and fatigue level.

[0614] Step 5:

[0615] The server uses a generation AI model to generate music information, taking into account the estimated emotional state, fatigue level, and the user's past playback history. The input is the emotional state, fatigue level, and playback history, and the prompt is "Current emotion is relaxed, heart rate is stable. Please generate calming music." The output is the generated music file.

[0616] Step 6:

[0617] The server sends the generated music file to the terminal. The terminal suggests the music to the user, and if the user accepts, playback begins. The input is the music file received from the server, and the output is the music experience provided to the user. Here, the terminal briefly describes the content of the music and encourages the user to listen.

[0618] (Application Example 1)

[0619] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0620] Modern consumers are seeking a more comfortable and personalized experience while ordering food and waiting for delivery. However, typical food delivery services lack features that adjust background music according to the user's emotional state and personal preferences, making it difficult to alleviate stress and frustration during waiting times.

[0621] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0622] In this invention, the server includes image capture means for acquiring the user's facial expression and posture information, means for suggesting music suitable for the dish based on the user's order history, and means for generating music data based on the estimated emotional state and fatigue level. This makes it possible to provide music that corresponds to the user's emotional state and order content, enabling a more comfortable and personalized delivery experience.

[0623] A "user" is a consumer who uses the system to place orders or receive music generation services.

[0624] "Facial expression information" refers to data about the user's facial expressions, which is acquired using a camera.

[0625] "Posture information" refers to data about the user's body movements and posture, and, like facial expression information, is acquired by a camera.

[0626] "Image capture means" refers to a shooting device or technology for acquiring user facial expression information and posture information.

[0627] "Emotional state" refers to the user's current state of mind and emotions, and is estimated by the system.

[0628] "Fatigue level" is an indicator that shows the degree of fatigue a user is experiencing, and it is estimated by the system along with their emotional state.

[0629] "Estimation means" refers to technologies and algorithms used to analyze and determine a user's emotional state and fatigue level based on acquired data.

[0630] "Music data" refers to music information provided to the user, and is created by various generation methods.

[0631] "Generation means" refers to a technology or system for creating music data based on estimated emotional states and fatigue levels.

[0632] "Order history" is a record of food and beverage orders that a user has placed in the past.

[0633] "Methods for suggesting music suitable for a dish" refers to technologies and systems that select and present music that is appropriate for a dish based on the user's order history.

[0634] The system that realizes this invention first acquires the user's facial expression information, posture information, and biometric information using a camera and smartwatch on a terminal. A smartphone or tablet camera is used as the image capture means, and biometric information such as heart rate and blood pressure is collected from the smartwatch via Bluetooth communication. This data is transmitted to a server in real time.

[0635] The server uses collaborative filtering and emotion estimation algorithms based on the collected data to estimate the user's emotional state and fatigue level. From these estimation results, a generative AI model is used to generate music data tailored to each individual user. Furthermore, it can analyze the user's order history and suggest music that matches their order.

[0636] For example, when a user orders a pizza, cheerful and fun music can be played to make the waiting time for the food more enjoyable. This allows for a more personalized delivery experience for the user.

[0637] The hardware used includes smartphones, tablets, and smartwatches, while the servers utilize Amazon Web Services (AWS). The software employs facial recognition algorithms, emotion estimation algorithms, and OpenAI's generative AI models for music generation.

[0638] For example, if a user's current emotional state requires relaxation, and they have ordered Asian food, the system might use the prompt "Generate relaxing Asian music" to generate music. This allows for music suggestions based on specific order details.

[0639] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0640] Step 1:

[0641] The device uses the smartphone's camera to capture the user's facial expressions and posture. In addition, it acquires biometric information such as heart rate and blood pressure through a smartwatch. This data is collected in real time and converted into a digital format. In this process, the input is data of the user's actual facial expressions and biometric state, and the output is the corresponding digital information.

[0642] Step 2:

[0643] The terminal transmits the collected digital information to the server. The server performs collaborative filtering and emotion estimation algorithms to analyze the received data. The inputs are transmitted facial expression information, posture information, and biometric information, and the outputs are estimated data of emotional state and fatigue level.

[0644] Step 3:

[0645] The server references the user's past order history based on estimated emotional state and fatigue levels. It then generates prompt messages to suggest music related to specific food orders. At this stage, the input is the emotional state estimation data and order history, and the output is the specific prompt message for music generation.

[0646] Step 4:

[0647] The server uses a generative AI model to generate music data based on the created prompt sentences. In this step, the input is the music generation prompt sentences, and the output is the generated customized music data.

[0648] Step 5:

[0649] The server sends the generated music data to the terminal, and the terminal suggests music to the user. If the user accepts the suggestion, the music is played. Here, the input is the music data sent from the server, and the output is the provision of a music experience through the terminal.

[0650] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0651] This invention relates to a system that analyzes a user's emotional state using an emotion engine based on the user's facial expression and posture information, and generates music data based on that analysis.

[0652] First, the device uses the camera on a PC or smartphone to acquire the user's facial expressions and posture information in real time. This allows for the rapid and accurate capture of specific information related to the user's emotions. The device also acquires biometric information from the smartwatch the user is wearing, which is used for detailed emotional analysis.

[0653] This acquired information is sent to a server, which uses its built-in emotion engine to recognize and analyze the user's emotional state. The emotion engine analyzes various emotional indicators to determine the user's emotional state. For example, frequent smiles might indicate "happiness," while frequent frowns might indicate "stress."

[0654] Next, the server generates music data using an AI music generation model based on the analyzed emotional data. This model generates music that is most suitable for the user's emotional state. For example, if it is determined that the user needs to relax, calm piano music will be generated.

[0655] Furthermore, if a user provides a hummed tune, the audio data is acquired by the device, and the melody information is sent to the server. Combined with the analysis results of the emotion engine, it is possible to generate music based on this melody information.

[0656] The generated music data is sent to the device, which then suggests songs to the user. If the user accepts the suggestion, the device plays the song. For example, if the user has had a stressful day, the emotion engine can analyze the user's mood and provide relaxing ambient music.

[0657] Thus, the present invention provides music optimized for the user's emotions, offering a musical experience that corresponds to their emotional state at that moment. This contributes to maintaining and improving the user's mental health and enriches their daily life.

[0658] The following describes the processing flow.

[0659] Step 1:

[0660] The device captures the user's facial expressions and posture information in real time using the camera of a PC or smartphone, and acquires this data. Furthermore, the device acquires the user's biometric information via a smartwatch to understand the overall picture.

[0661] Step 2:

[0662] The device centralizes the acquired facial expression, posture, and biometric information and sends it to the server as a data packet. This ensures that the data necessary for analysis reaches the server immediately.

[0663] Step 3:

[0664] The server inputs the received data into the emotion engine. The emotion engine analyzes specific emotional states from facial expressions and posture, and estimates states such as "happiness," "stress," and "fatigue." This analysis includes a process of detecting features in the data using deep learning algorithms.

[0665] Step 4:

[0666] The server generates music data using an AI music generation model based on the emotional state estimated by the emotion engine. This model selects and generates music that is best suited to the user's current mood; for example, it creates music with a calming melody when relaxation is needed.

[0667] Step 5:

[0668] When a user hums, the device records the sound and sends it to the server as melody information. The server then uses this melody information to either generate a new song or compose it as existing music.

[0669] Step 6:

[0670] The generated music data is sent to the device. The device then suggests the newly generated music to the user and displays the suggestions via screen notifications or within the app.

[0671] Step 7:

[0672] The user reviews the music suggestions from the device and starts playback upon acceptance. During music playback, the device may request user feedback, which is sent to the server to improve future suggestions.

[0673] (Example 2)

[0674] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0675] In modern times, providing music tailored to a user's emotional state contributes to improved mental health and stress reduction. However, existing systems have limitations in utilizing information from facial expressions and posture, and they are unable to fully utilize users' biometric data or personal humming in music generation. The objective of this invention is to develop a system that provides a more accurate and customized musical experience by using diverse user information.

[0676] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0677] In this invention, the server includes information acquisition means for acquiring user facial expression information and posture information, analysis means for analyzing the user's emotional state based on the facial expression information and posture information collected by the information acquisition means, and music generation means for generating music data using a generation AI model based on the analyzed emotional state. This makes it possible to provide a music experience optimized for the user's emotional state in real time.

[0678] "Information acquisition means" refers to devices and methods for collecting information about a user's facial expressions and posture using sensors and cameras, and acquiring it as data.

[0679] "Analysis means" refers to software or algorithms that process data to identify the user's emotional state based on acquired facial expression and posture information.

[0680] A "generative AI model" is a system that utilizes artificial intelligence technology to automatically generate musical patterns and elements that match the user's emotional state.

[0681] "Music generation means" refers to technologies and methods for automatically creating appropriate music based on the results of an analysis of emotional states.

[0682] A "humming processing method" is a technology that has the function of recording a hummed tune by a user and generating a song based on that audio data.

[0683] "Methods for acquiring biometric information" refer to methods for acquiring physiological data such as a user's heart rate and body temperature using smartwatches or other biosensors.

[0684] "Biometric information analysis means" refers to technologies and algorithms that process and associate acquired biological information in order to utilize it for music data generation.

[0685] This invention is a system that generates music data according to the user's emotional state. A specific embodiment of this system is described below.

[0686] The device uses cameras and sensors on a PC or smartphone to acquire the user's facial expressions and posture information. By using facial recognition software, it can accurately monitor changes in the user's facial expressions in real time. In addition, the device uses wearable devices such as smartwatches to acquire biometric information such as the user's heart rate and body temperature. This allows for the collection of data to analyze the user's emotional state in more detail.

[0687] The information acquired by the device is transmitted to the server via the network. The server is equipped with an emotion engine to analyze this information, estimating the user's emotional state by analyzing facial expressions, posture information, and biometric information. Because the emotion engine utilizes neural network technology, it can distinguish multiple emotional indicators with high accuracy.

[0688] The server uses a generative AI model based on the analyzed emotional state. This generative AI model generates music data that matches the emotional state, and its style and atmosphere can be altered. An example of a prompt would be, "Generate music suitable for a relaxing evening." By entering such prompts, the server automatically generates music appropriate for the user.

[0689] Furthermore, if a user provides a hummed tune, the device acquires the audio data and sends it to the server. The server can then combine the results of the emotion engine's analysis with the melody information of this hummed tune to perform its own music generation.

[0690] The generated music data is sent back from the server to the terminal, which then suggests music to the user. If the user approves the suggestion, the terminal plays the music. For example, on a day when the user is feeling stressed, relaxing music may be provided, contributing to the improvement of the user's mental health.

[0691] This system will enable a more personalized music experience that responds to the user's real-time emotional state.

[0692] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0693] Step 1:

[0694] The device uses the camera of a PC or smartphone to acquire the user's facial expressions and posture information in real time. Using facial recognition software, it detects characteristic points of facial expressions and changes in posture, and records this as digital data. It receives camera video data as input and generates analyzable facial and posture data as output.

[0695] Step 2:

[0696] The device acquires biometric information such as heart rate and body temperature from the smartwatch. A dedicated health monitoring application retrieves this information and records it as digital data representing the user's physical state. It receives biometric measurement data from the smartwatch as input and generates analyzable biometric data as output.

[0697] Step 3:

[0698] The device encrypts the data acquired in Step 1 and Step 2 and sends it to the server over the network. This ensures that data is transferred securely while maintaining privacy. It receives facial expressions, posture, and biometric data as input and outputs them as data to be sent to the server.

[0699] Step 4:

[0700] The server feeds the received data into the emotion engine and begins analysis. The emotion engine uses neural network technology to estimate the user's emotional state. It receives data on facial expressions, posture, and biometrics as input and generates evaluation data regarding the emotional state as output.

[0701] Step 5:

[0702] The server, based on the analysis results of the emotion engine, inputs appropriate prompts into the generative AI model and generates music data. The generative AI model creates music best suited to the user based on prompts such as "Generate music with a relaxing atmosphere." It receives emotional state evaluation data as input and generates music data as output.

[0703] Step 6:

[0704] The terminal receives music data from the server and presents it to the user through an interface that suggests music. If the user approves the suggested music, the terminal plays the song. It receives generated music data as input and plays music as output.

[0705] (Application Example 2)

[0706] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0707] In modern society, providing entertainment that responds immediately to changes in an individual's emotional state is a crucial element in improving the quality of life. However, conventional music playback systems lack the means to analyze a user's emotions and physiological state in real time and provide music that suits them. As a result, users cannot easily obtain music that matches their emotions and physiological state at any given time, and there is a lack of effective support to alleviate stress and anxiety.

[0708] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0709] In this invention, the server includes image acquisition means for acquiring user facial expression data and posture data, estimation means for estimating the user's emotional state based on the acquired facial expression data and posture data, generation means for generating music data based on the estimated emotional state, and means for incorporating pre-acquired physiological data of the user into the music data generation. This enables the user to instantly receive music that suits their emotional state, thereby enhancing daily emotional support.

[0710] "User facial expression data" is digital information obtained by analyzing the features of a user's face, and it serves as basic data for estimating their emotional state.

[0711] "Posture data" refers to information that indicates the position and angle of the user's body, and is used to understand the user's psychological state.

[0712] "Image acquisition means" refers to a device that uses a camera or other imaging device to capture the user's actions and appearance, and converts them into analyzable digital data.

[0713] "Estimation method" is a term that refers to the methods and processes used to identify a user's internal state by performing analysis based on acquired data.

[0714] "Generation means" refers to a method or process for creating an intended output based on predetermined input information, and in this case, it refers to generating music data.

[0715] "Physiological data" refers to digital information that indicates a user's physical function and state, such as heart rate and activity level. This data is an important element in estimating emotional states.

[0716] A "mobile communication terminal" refers to electronic devices such as mobile phones and smartphones that can communicate without being tied to a specific location.

[0717] "Voice-based melodic information" refers to information obtained by acquiring a melody spoken by a user as audio data and converting it into an analyzable format.

[0718] This invention provides a system for generating and playing music based on a user's emotional state. This system includes a server for acquiring and analyzing facial expression data, posture data, and physiological data, centered on the user's mobile communication terminal, and an interface for providing the generated music to the user.

[0719] The server analyzes facial expression data obtained through the user's mobile communication terminal's camera. Specifically, it uses facial expression recognition software (e.g., OpenCV) with the facial images acquired from the camera to understand the user's emotional state. Furthermore, the terminal acquires physiological data such as heart rate from wearable devices such as smartwatches.

[0720] The analyzed data is sent to an emotion estimation engine, which identifies the emotional state. For example, if the user is smiling, it is determined to be in a happy mood. This emotional state serves as the basis for creating music data using a generative AI model. The generative AI model (e.g., OpenAI's music generation model) generates the most suitable song according to this emotional information.

[0721] The generated music is sent to the user's device and presented to the user by a music playback application. If the user accepts it, the device plays the music and works to guide the user's emotional state in a positive direction.

[0722] As a concrete example, let's say a user uses the app on a Friday afternoon, wanting to relax. The app analyzes the user's calm facial expression and obtains data indicating a calm heart rate. The system determines that the user is in a "relaxed state" and prompts the AI ​​model with the message, "You need to relax, generate calming music," which then generates a suitable piano piece. In this way, the system can provide the user with an optimal musical experience in real time.

[0723] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0724] Step 1:

[0725] The device uses a camera to acquire real-time data on the user's facial expressions. Based on the acquired image data, an expression recognition algorithm analyzes features associated with specific emotions, such as smiles and frown lines. The input is image data from the camera, and the output is data indicating the emotional state.

[0726] Step 2:

[0727] The device acquires physiological data such as heart rate and activity level from wearable devices such as smartwatches. This allows the user's physiological state to be understood. The input is physiological data from sensors, and the output is data indicating the physiological state.

[0728] Step 3:

[0729] The device sends acquired facial expression data and physiological data to the server. The server receives this data and uses an emotion estimation engine to estimate the user's emotional state. The input is facial expression data and physiological data, and the output is the emotion estimation result.

[0730] Step 4:

[0731] The server inputs prompt text for music generation into the generative AI model based on the emotion estimation results. The generative AI model receives this prompt text and generates music data appropriate to the user's emotions. The input is the prompt text based on the emotion estimation results, and the output is the generated music data.

[0732] Step 5:

[0733] The server sends the generated music data to the terminal. The terminal receives this music data and suggests to the user that they play the music. If the user approves playback, the terminal plays the music and lets the user listen. The input is the generated music data, and the output is the music that is played.

[0734] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0735] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0736] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0737] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0738] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0739] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0740] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0741] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0742] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0743] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0744] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0745] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0746] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0747] 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.

[0748] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0749] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0750] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0751] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0752] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0753] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0754] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0755] The following is further disclosed regarding the embodiments described above.

[0756] (Claim 1)

[0757] Image capture means for acquiring user facial expression information and posture information,

[0758] An estimation means for estimating the user's emotional state and fatigue level based on the acquired facial expression information and posture information,

[0759] A generation means for generating music data based on estimated emotional state and fatigue level,

[0760] A system that includes this.

[0761] (Claim 2)

[0762] The system according to claim 1, comprising means for acquiring the user's humming voice and generating music data based on the humming voice.

[0763] (Claim 3)

[0764] The system according to claim 1, comprising means for acquiring a user's biometric information and incorporating said biometric information into music data generation.

[0765] "Example 1"

[0766] (Claim 1)

[0767] An image acquisition means for acquiring user facial expression data and posture data,

[0768] A communication method for acquiring the user's physical information,

[0769] An analysis means for estimating the user's emotional state and fatigue level based on the acquired facial expression data, posture data, and physical information,

[0770] A generation means that generates music information by adding past playback history to estimated emotional state and fatigue level,

[0771] A means of providing generated music information to the user,

[0772] A system that includes this.

[0773] (Claim 2)

[0774] The system according to claim 1, comprising means for acquiring a user's voice and generating music information based on the voice.

[0775] (Claim 3)

[0776] The system according to claim 1, comprising means for incorporating the user's physical information into the generation of music information.

[0777] "Application Example 1"

[0778] (Claim 1)

[0779] Image capture means for acquiring user facial expression information and posture information,

[0780] An estimation means for estimating the user's emotional state and fatigue level based on the acquired facial expression information and posture information,

[0781] A generation means for generating music data based on estimated emotional state and fatigue level,

[0782] A method for suggesting music suitable for a meal based on the user's order history,

[0783] A system that includes this.

[0784] (Claim 2)

[0785] The system according to claim 1, comprising means for acquiring the user's humming voice and generating music data based on the humming voice.

[0786] (Claim 3)

[0787] The system according to claim 1, comprising means for acquiring a user's biometric information and incorporating said biometric information into music data generation.

[0788] "Example 2 of combining an emotion engine"

[0789] (Claim 1)

[0790] Information acquisition means for obtaining user facial expression information and posture information,

[0791] An analysis means for analyzing the user's emotional state based on facial expression information and posture information collected by the aforementioned information acquisition means,

[0792] A music generation means that generates music data using a generative AI model based on the analyzed emotional state,

[0793] A humming processing means that acquires the user's humming and generates a song based on that audio information,

[0794] A means for acquiring biological information and incorporating said biological information into the analysis,

[0795] A system that includes this.

[0796] (Claim 2)

[0797] The system according to claim 1, which inputs prompts to a generative AI model based on the analyzed emotional state and generates optimal music data.

[0798] (Claim 3)

[0799] The system according to claim 1, further comprising means for influencing music data obtained from a generated AI model using analysis results based on biological information.

[0800] "Application example 2 when combining with an emotional engine"

[0801] (Claim 1)

[0802] An image acquisition means for acquiring user facial expression data and posture data,

[0803] An estimation means for estimating the user's emotional state based on the acquired facial expression data and posture data,

[0804] A generation means for generating music data based on an estimated emotional state,

[0805] A method for generating music data based on pre-acquired physiological data of the user,

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, comprising means for acquiring melody information from a user's voice and generating music data based on the melody information.

[0809] (Claim 3)

[0810] The system according to claim 1, which acquires user facial expression data using a camera on the user's mobile communication terminal. [Explanation of Symbols]

[0811] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Image capture means for acquiring user facial expression information and posture information, An estimation means for estimating the user's emotional state and fatigue level based on the acquired facial expression information and posture information, A generation means for generating music data based on estimated emotional state and fatigue level, A system that includes this.

2. The system according to claim 1, comprising means for acquiring the user's humming voice and generating music data based on the humming voice.

3. The system according to claim 1, comprising means for acquiring the user's biometric information and incorporating said biometric information into music data generation.

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