system

Abstract

We provide the system. [Solution] A means of receiving work information from users, A means for determining appropriate musical characteristics based on the aforementioned work information, means for generating music data based on the aforementioned musical characteristics, A means of monitoring user activity data in real time, means for adjusting the generated music data according to the activity data, A means for distributing the adjusted music data to the user, 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 as a 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 modern society, it is emphasized that individuals efficiently perform tasks in business and at home. However, many users find it difficult to find and maintain a rhythm suitable for tasks. In particular, in order to perform efficient work, a personalized music experience for each user is required. However, existing music applications lack means to provide music tailored to the specific tasks of users.

Means for Solving the Problems

[0005] This invention provides a system that has means for receiving work information from the user and determines music characteristics according to the work content to provide the user with optimal music. Specifically, it generates music data based on music characteristics, and can further monitor the user's activity data in real time and adjust the music data according to that activity data. With such a system, the user can perform tasks while listening to music that suits them, improving work efficiency and performance.

[0006] "Means for receiving work information from users" refers to an interface or process that allows users to input their current tasks and activities, and for the system to retrieve that information.

[0007] "Means for determining appropriate musical characteristics" refers to an algorithm or process for deriving the tempo, genre, and atmosphere of music based on user input.

[0008] "Means for generating music data" refers to generative models and technologies for automatically generating new music based on determined musical characteristics.

[0009] "Means of monitoring user activity data in real time" refers to a system or device for continuously monitoring a user's heart rate and movement information collected from smart devices, etc.

[0010] "Means for adjusting generated music data" refers to processes or algorithms that adaptively change the tempo, volume, atmosphere, etc., of already generated music in accordance with the user's activity data.

[0011] "Means for delivering adjusted music data to users" refers to a network or protocol for streaming or playing the adjusted music to the user's device. [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] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying 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 working 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, etc.

[0018] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

[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] To implement this invention, a system is constructed in which a user, a terminal, and a server work together. The user inputs daily task information through an application on the terminal and provides it to the system. For example, the user can input tasks such as "create documents" or "training." The terminal receives this input information and sends it to the server.

[0034] Based on the received task information, the server analyzes the task content using natural language processing techniques. Here, the server determines the optimal musical characteristics for the task, such as tempo and pitch. The user's past selection and activity history are considered in determining these musical characteristics. The server then uses a generation algorithm to generate music data based on the determined musical characteristics. This generated music data is optimized for the user's activities and helps the user efficiently complete the task.

[0035] The terminal collects activity data from the user's smart device. This activity data includes heart rate, movement, and location information, and is provided to the server in real time. The server dynamically adjusts the generated music data according to the activity data. If the user wants to improve their concentration, the rhythm can be changed or the tempo adjusted to improve work efficiency. By dynamically changing the music to suit the user in this way, an optimal state can always be maintained.

[0036] As a concrete example, consider the case where a user performs a "jogging" task. When the user inputs "jogging" as the task, the server determines high-tempo, energetic musical characteristics and generates music. If the user's heart rate becomes too high while jogging, the server adjusts the music to a slower tempo to maintain a calm state.

[0037] This system allows users to have a musical experience that helps them efficiently perform their daily tasks, maximizing their task performance.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user launches an application on their device and enters information about their current task, such as "housework" or "data entry." The device then accurately receives this input information.

[0041] Step 2:

[0042] The terminal sends task information received from the user to the server. The server receives this information and prepares to analyze the task details.

[0043] Step 3:

[0044] The server uses natural language processing technology to analyze task information and identify relevant keywords and work environment. Based on this, it derives the optimal musical characteristics for the task, namely tempo and genre.

[0045] Step 4:

[0046] Based on the determined musical characteristics, the server generates new music data using a generation algorithm. This music data also takes into account the user's past selection history.

[0047] Step 5:

[0048] The device collects activity data in real time from the user's smart device. This activity data includes heart rate, exercise level, and posture information. The device sends this data to the server.

[0049] Step 6:

[0050] The server analyzes the collected activity data and dynamically adjusts the music data to suit the user's situation. For example, if the user is seeking relaxation, the music tempo will be slowed down.

[0051] Step 7:

[0052] The terminal receives music data that has been adjusted from the server and delivers it to the user's device. This ensures that the user can always listen to music that is appropriate for their task.

[0053] Step 8:

[0054] Users perform tasks while listening to the provided music. The music adapts to the user's activity rhythm, improving work efficiency.

[0055] (Example 1)

[0056] 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."

[0057] In modern society, many people use music to carry out their activities efficiently, but selecting music that is suitable for individual activities and physiological states is not easy. In particular, there is a need to optimize activity performance by dynamically changing the characteristics of music according to the activity. Conventional music selection methods have the challenge of not being able to provide music that is quickly and appropriately suited to this purpose.

[0058] 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.

[0059] In this invention, the server includes means for receiving activity information provided by a human-operated device, means for determining optimal acoustic characteristics using the activity information, and means for generating acoustic data based on the acoustic characteristics. This makes it possible to dynamically provide music that is suitable for the user's activity and physiological state.

[0060] "Activity information" refers to information provided by a person when they perform a specific activity, such as exercise or work activities.

[0061] "Acoustic properties" refer to the characteristics of a sound, including elements such as tempo, rhythm, and tone.

[0062] "Acoustic data" refers to sound information generated based on acoustic characteristics, representing music or sounds tailored to specific activities or states.

[0063] "Activity status data" refers to information that records a person's physical or behavioral state in real time, including heart rate and movement information.

[0064] "Means of adjustment" refers to methods of processing acoustic data to modify or optimize it according to activity status data.

[0065] "Means of provision" refers to methods of transmitting and making available the generated audio data to people.

[0066] To implement this invention, a system needs to be built in cooperation with a user, a terminal, and a server. The user inputs activity information through a dedicated application on a digital device. The information that the user can input could include activity details such as exercise or work. For example, activity information such as "jogging" or "document creation" can be entered.

[0067] The terminal is responsible for receiving the entered activity information and transmitting it to the server via the network. This transmission process uses data communication based on the HTTP protocol.

[0068] The server uses natural language processing techniques to analyze the received activity information. This process uses Python's natural language processing libraries, NLTK and spaCy, to analyze text data and determine the acoustic characteristics required for the activity. In determining the acoustic characteristics, the server retrieves the user's past preference history from a database (e.g., MySQL®) and takes it into consideration.

[0069] After the acoustic characteristics are determined, the server generates acoustic data using a generative AI model. The generation of acoustic data is performed using a machine learning model specifically designed for music generation. Specifically, advanced music generation models such as OpenAI's MuseNet are used. An example of a prompt to the generative AI model would be, "Generate fast-paced, energetic music."

[0070] The device uses sensors from the smart device to collect user activity data. This activity data includes heart rate, movement, and location information, which is transmitted to the server in real time.

[0071] The server dynamically adjusts the generated audio data based on the received activity status data. When the user needs to concentrate, the rhythm and tempo of the music are changed accordingly to provide an audio experience tailored to the purpose. In this way, the system aims to improve the quality of activity by providing a comfortable audio environment that is in line with the user's situation and needs.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The user enters activity information using a terminal application. This information includes specific activities such as "jogging" or "studying." This information becomes the input data sent to the next processing step.

[0075] Step 2:

[0076] The terminal sends activity information received from the user to the server. The data is transferred to the server using the HTTP protocol, and the server receives it as input data for analysis. In this case, the data is generally sent in JSON format.

[0077] Step 3:

[0078] The server performs natural language processing based on the received activity information. Specifically, it analyzes the text data using Python's NLTK or spaCy to extract important keywords and context. Based on this processing, foundational data for determining acoustic characteristics is generated. This foundational data includes, for example, indicators related to appropriate tempo and rhythm.

[0079] Step 4:

[0080] The server retrieves and analyzes past user preference history from a database. This historical data is retrieved using SQL from relational databases such as MySQL, and current activity is compared with past patterns to further refine the acoustic characteristics. This process determines the detailed parameters necessary for generating the music.

[0081] Step 5:

[0082] The server generates acoustic data using a generative AI model based on acoustic characteristics. The AI ​​model used is specialized for music generation, and specific examples such as "Generate high-tempo, energetic music" are input to the model as prompts. Based on this, the model outputs music data. This data is sent to the terminal as a music file.

[0083] Step 6:

[0084] The device collects user activity data from the smart device's sensors. This data includes real-time heart rate, motion data, and location information, and is immediately transmitted to the server. The activity data provides the input data necessary for real-time music adjustments.

[0085] Step 7:

[0086] The server dynamically adjusts the generated audio data based on activity data. For example, if the user's heart rate increases, the tempo of the music is adjusted. The purpose of this process is to always maintain optimal user performance. The adjusted music data is then delivered to the user through the terminal, creating a feedback loop.

[0087] (Application Example 1)

[0088] 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."

[0089] There is a lack of means to provide music in real time that is optimal for the daily activities and tasks of diverse users, thereby promoting effects such as improved concentration and relaxation. Furthermore, the technology to achieve this using smart audio devices is not yet sufficiently developed, which is a challenge.

[0090] 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.

[0091] In this invention, the server includes means for receiving work information from the user, means for determining appropriate musical characteristics based on the work information, and means for generating acoustic data based on the musical characteristics. This makes it possible to provide optimal acoustic data in real time for a variety of user activities.

[0092] A "user" refers to an individual who interacts with this system and provides work information and activity data.

[0093] "Work information" refers to information about the tasks and activities that the user is trying to perform, and it is the data that forms the basis for determining the musical characteristics.

[0094] "Musical characteristics" refer to elements that describe the sonic features optimized for the user's work or activity, such as the rhythm, tempo, and atmosphere of the music.

[0095] "Audio data" refers to digital data that represents sound information generated based on musical characteristics and is provided to the user as music.

[0096] "Activity data" refers to data acquired in real time, such as the user's physiological information and movement information, and is used to adjust the acoustic data.

[0097] A "smart audio device" refers to a device that plays audio data via an internet connection and provides users with a music experience.

[0098] To implement this invention, a system is constructed in which a server, a terminal, and a smart audio device work together. The user inputs work information related to their daily activities through the smart audio device. The terminal then receives this input information and sends it to the server. The server uses natural language processing technology to analyze the received work information. Specifically, it utilizes Python and natural language processing libraries (e.g., NLTK and spaCy) to determine the optimal musical characteristics for the user's task.

[0099] Once the musical characteristics are determined, the server generates audio data. This generation uses a music generation library (e.g., Magenta) to create digital music data tailored to the musical characteristics. The generated audio data is stored in the cloud and further refined based on user activity data.

[0100] The user's device collects activity data in real time from smart audio equipment and provides it to the server. This activity data includes heart rate, movement, and location information. The server uses this data to dynamically adjust the audio data and provide a music experience that is optimal for the user's current situation. For example, if the user is doing fitness activities, high-tempo music is provided, but if the heart rate increases, the tempo is slowed down.

[0101] This system can improve performance and concentration by providing music optimized for various user activities using a generative AI model. It can enhance the user experience by using prompts such as, "What kind of music is best suited for a user who is 'concentrating on work'?"

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] The user inputs work information by voice through a smart audio device, and the terminal receives that information. The input voice data is converted into text data using speech recognition software (for example, Google® Speech-to-Text API). The converted text data is sent to the server.

[0105] Step 2:

[0106] The server analyzes the received work information text. Using natural language processing techniques, it analyzes the work information using Python's NLTK and spaCy libraries to determine the optimal musical characteristics for the user's task. The input is text data of the work information, and the output is data of musical characteristics (e.g., tempo, rhythm).

[0107] Step 3:

[0108] The server generates audio data based on musical characteristics. It uses a music generation library (e.g., Magenta) to generate audio data with specific musical characteristics. A generation AI model is utilized in this process. The input is data on musical characteristics, and the output is the generated audio data (digital music file).

[0109] Step 4:

[0110] The device collects activity data in real time from smart audio devices. This includes heart rate, motion information, and location information. The collected data is acquired using sensor technology and provided to the server. The input is the user's physiological data, and the output is activity data that reflects this.

[0111] Step 5:

[0112] The server analyzes the received activity data and adjusts the acoustic data in real time. As a preprocessing step, it cleanses the data using a data analysis tool (e.g., Pandas) and dynamically adjusts the tempo and rhythm of the music according to the type of activity. The input is activity data, and the output is the adjusted acoustic data.

[0113] Step 6:

[0114] The adjusted audio data is streamed to the user via the internet through a terminal using a smart audio device. This allows the user to receive an optimal music experience in real time. The input is the adjusted audio data, and the output is the music stream to the user.

[0115] 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.

[0116] In implementing the present invention, a system is configured that combines a user, a terminal, a server, and an emotion engine. The user can use a terminal with a dedicated application installed to input their daily tasks and current emotions. The terminal receives the information input by the user and transmits it to the server.

[0117] The server receives this information and determines the optimal musical characteristics related to the task, such as tempo and style. The emotion engine within the server recognizes the user's emotions in real time by analyzing the user's facial expressions and voice tone. This allows the server to evaluate how the user's emotional state affects the musical characteristics and adjust the music accordingly.

[0118] The server uses a generation algorithm to generate music data based on musical characteristics. This generated music data is dynamically adjusted to match the user's activity and emotional data. By adjusting the music genre and tempo, users can listen to music that suits their emotions and activity level, thereby facilitating task completion.

[0119] For example, if a user wants to "concentrate," the server generates and delivers fast-paced, energetic yet emotionally calming music. If the emotion engine detects stress from the user's face or voice, it instructs the system to switch to a calmer melody. This provides the user with an adaptive musical experience, making it easier to concentrate on tasks while reducing stress.

[0120] This system allows users to efficiently perform tasks while listening to music optimized for their emotions and activities, thereby maximizing their performance.

[0121] The following describes the processing flow.

[0122] Step 1:

[0123] The user accesses the application on their device and enters details of their current task and their feelings. The device receives this information and prepares to begin processing.

[0124] Step 2:

[0125] The terminal sends task and emotional information obtained from the user to the server. The server then begins analysis based on this information.

[0126] Step 3:

[0127] The server analyzes the received task information using natural language processing techniques to identify the characteristics of the task. This allows it to establish general standards for musical characteristics.

[0128] Step 4:

[0129] An emotion engine built into the server analyzes real-time facial expression data and voice tone sent from the user's device to recognize the user's emotional state.

[0130] Step 5:

[0131] The server generates optimal music data, taking into account the musical characteristics suitable for the task and the user's emotional state. It executes a generation algorithm to create personalized music.

[0132] Step 6:

[0133] The device uses a smart device to collect user activity data. This activity data includes the user's heart rate and step count. The collected data is sent to a server.

[0134] Step 7:

[0135] The server dynamically adjusts the tempo and style of the music data based on activity data and recognized emotion data. This ensures that the music is tailored to the user's current state.

[0136] Step 8:

[0137] The device receives the optimized music data and plays it on the user's device. In this way, the user can concentrate on their tasks while listening to optimized music.

[0138] Step 9:

[0139] Through an adaptive music experience, users can lower their stress levels and increase their concentration. In this state, they can continue tasks and improve their efficiency.

[0140] (Example 2)

[0141] 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 will be referred to as the "terminal."

[0142] In modern society, many people face various stresses and pressures, which often lead to decreased work efficiency. Furthermore, conventional music distribution systems struggle to provide music that is tailored to the emotional state of individual users, resulting in a challenge in providing a music experience that matches the user's situation and emotions.

[0143] 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.

[0144] In this invention, the server includes means for receiving task data and emotion data from the user, means for analyzing the user's facial expression data and voice data to recognize the emotional state in real time, and means for generating music data based on the emotional state and music attributes. This makes it possible to provide optimal music according to the user's situation, thereby improving work efficiency and stabilizing emotions.

[0145] "Task data" refers to information about the tasks and goals that a user intends to accomplish. This data includes details about the user's action plan and activities.

[0146] "Emotional data" refers to information that indicates a user's emotional state. This data is obtained from facial expressions, tone of voice, and other sources, and is used to analyze the user's mental state.

[0147] "Musical attributes" are parameters that indicate the characteristics of music, including tempo, melody, and harmony. They are elements that create an appropriate musical experience according to the user's situation.

[0148] "Facial expression data" is data obtained by analyzing a user's facial expressions. This allows us to infer the user's emotional state.

[0149] "Voice data" refers to data collected from the user's voice, and is used to evaluate emotional state by analyzing factors such as voice tone and pitch.

[0150] "Emotional state" refers to the state that indicates the user's current mental or emotional condition. This state is grasped in real time through data analysis.

[0151] "Music data" refers to data related to generated music, including information necessary for music playback. It is generated and adjusted according to the target user.

[0152] "Dynamic adjustment" refers to the process of modifying the attributes of music data in real time according to the user's changing circumstances and emotional state.

[0153] This invention provides a system that combines a user, a terminal, a server, and an emotion engine to improve a specific user experience. The user uses a terminal with a dedicated application installed. The application can be installed on a smartphone or tablet, and task data and emotion data can be input through that application.

[0154] The terminal quickly and securely transmits data entered by the user to the server. The data is transferred over the network and processed on the server. On the server, a computer system equipped with a high-performance processor runs, performing data analysis using a generative AI model. The server executes a process to determine music attributes based on the user's task data, using a proprietary algorithm.

[0155] Furthermore, the server is equipped with an emotion engine that analyzes the user's facial expression data and voice data to recognize the user's emotional state in real time. This analysis uses machine learning and voice analysis technologies. For example, if a user inputs task data such as "I want to improve my concentration," and the emotion engine recognizes that the user's facial expression indicates a state of concentration, fast-paced music will be generated.

[0156] The generated music data is dynamically adjusted on the server according to the user's changing activity and emotional state. This system allows for switching to calmer, slower-tempo music if stress is detected. The generating AI model is constantly learning from new data and used to continuously optimize the music.

[0157] A concrete example of a prompt might be a question like, "Which music attributes should be adjusted to suggest appropriate music when the user is in a relaxed state?" In this way, users can enjoy a music experience tailored to their situation and improve the efficiency of their tasks.

[0158] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0159] Step 1:

[0160] Users input task data and emotional data through their device. This data includes information such as "What tasks do I want to focus on today?" and "What is my current emotional state?". The device then identifies the data obtained from the user and organizes it into a dataset to prepare for the next step.

[0161] Step 2:

[0162] The device sends task data and sentiment data received from the user to the server. The transmitted data travels over the network and its format is adjusted for processing on the server. Specifically, the data is sent via HTTP requests and received as input by the server.

[0163] Step 3:

[0164] The server determines music attributes based on the received task data. Based on the input task data, logic is executed to select the music tempo and genre. For example, if the task is to improve concentration, the server will select and output music with energetic and fast tempo attributes.

[0165] Step 4:

[0166] The server uses an emotion engine to analyze the user's emotional state. Inputs include the user's facial expression data and voice data, which are analyzed to recognize the user's emotions in real time. Emotion analysis is performed using machine learning algorithms, generating outputs that quantify the user's stress level and concentration level.

[0167] Step 5:

[0168] The server integrates data on musical attributes and emotional states to generate musical data. The music generation algorithm takes this data as input and outputs appropriate musical data. For example, it might generate fast-paced music suitable for a state of concentration.

[0169] Step 6:

[0170] The server dynamically adjusts the generated music data according to the user's activity level. The input activity data is analyzed in real time as feedback, and the music tempo and volume are adjusted accordingly. The output is music data tailored to the user's state.

[0171] Step 7:

[0172] The device plays the adjusted music data received from the server to the user. By listening to this adjusted music, the user can enjoy a musical experience adapted to their emotions and tasks at that moment. In this way, the device provides real-time music delivery to the user.

[0173] (Application Example 2)

[0174] 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".

[0175] The challenge in online shopping is to improve the purchasing experience by providing an appropriate audio experience that responds to the user's emotional state. In particular, it is necessary to alleviate user stress, maintain their desire to purchase, and make the shopping process more comfortable.

[0176] 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.

[0177] In this invention, the server includes means for receiving emotional state information and shopping behavior information from the user, means for determining appropriate acoustic characteristics based on the emotional state information, and means for generating acoustic data based on the acoustic characteristics. This enables a comfortable and stress-free shopping experience by dynamically adjusting the acoustic experience according to the user's emotional state.

[0178] "User emotional state information" refers to data that indicates a user's emotions, obtained based on their facial expressions and voice.

[0179] "Shopping behavior information" refers to data related to a user's behavior patterns and purchase history when shopping online.

[0180] "Acoustic characteristics" refer to the fundamental elements that make up music, such as genre, tempo, and volume.

[0181] "Audio data" refers to digital information that is played back as music or sound.

[0182] "Facial expression information" refers to data obtained from the user's facial movements and expressions, which makes it possible to infer their emotions.

[0183] "Audio information" refers to data about the sounds and tone of voice emitted by the user, and is used to analyze the user's emotional state.

[0184] To implement this invention, the user needs to install a dedicated application on a mobile device such as a smartphone. When the user launches the application and engages in online shopping, the device uses its built-in camera and microphone to collect the user's facial expressions and voice information. This information is transmitted to a server in real time. Based on this information, the server performs emotion analysis and generates information about the user's emotional state.

[0185] The server utilizes emotional state information and shopping behavior information to determine acoustic characteristics. This can involve using emotion analysis APIs provided by cloud services such as Google Cloud. Based on the determined acoustic characteristics, acoustic data is generated using a music generation library with Python. This generated acoustic data is then adjusted to match the user's emotions and delivered to the device.

[0186] For example, if the server detects that a user is starting to feel tired after shopping for an extended period, it can play relaxing music to sustain the user's desire to purchase. In this way, it is possible to provide an optimized audio experience for the user.

[0187] An example of an input prompt for a generative AI model might be: "If the user is currently experiencing stress, please suggest what musical characteristics would be most relaxing." Using this prompt, the generative AI can generate suggestions for optimizing acoustic characteristics.

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] The user launches a smartphone application. The device collects the user's facial expressions and voice information in real time through its built-in camera and microphone, and sends this as input data to the server. This input is basic data used to analyze the user's emotional state.

[0191] Step 2:

[0192] The server performs emotion analysis using the received facial expression and voice information. This utilizes the Google Cloud Emotion Analysis API. Based on the emotion data as input, a data analysis algorithm is used to output information about the user's emotional state. This analysis identifies the user's current emotion.

[0193] Step 3:

[0194] The server receives user emotional state and shopping behavior information as input to determine acoustic characteristics. It then inputs a prompt to the generative AI model, such as, "If the user is currently experiencing stress, what musical characteristics would provide the most relaxation?" The server evaluates this prompt and outputs appropriate acoustic characteristics, including music genre, tempo, and volume.

[0195] Step 4:

[0196] The server generates acoustic data based on the determined acoustic characteristics. Using a music generation library with Python, it outputs sound data based on the input acoustic characteristics. The music generated here is designed to match the user's emotions.

[0197] Step 5:

[0198] The server adjusts the generated audio data in real time and delivers it to the user. During this process, it references input information, including the latest data on the user's shopping activities, to dynamically optimize the music's tempo and atmosphere. Finally, the adjusted audio data is sent to the user's device, allowing them to listen to it while shopping for a comfortable and stress-free shopping experience.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] [Second Embodiment]

[0203] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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.

[0208] 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).

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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".

[0215] To implement this invention, a system is constructed in which a user, a terminal, and a server work together. The user inputs daily task information through an application on the terminal and provides it to the system. For example, the user can input tasks such as "create documents" or "training." The terminal receives this input information and sends it to the server.

[0216] Based on the received task information, the server analyzes the task content using natural language processing techniques. Here, the server determines the optimal musical characteristics for the task, such as tempo and pitch. The user's past selection and activity history are considered in determining these musical characteristics. The server then uses a generation algorithm to generate music data based on the determined musical characteristics. This generated music data is optimized for the user's activities and helps the user efficiently complete the task.

[0217] The terminal collects activity data from the user's smart device. This activity data includes heart rate, movement, and location information, and is provided to the server in real time. The server dynamically adjusts the generated music data according to the activity data. If the user wants to improve their concentration, the rhythm can be changed or the tempo adjusted to improve work efficiency. By dynamically changing the music to suit the user in this way, an optimal state can always be maintained.

[0218] As a concrete example, consider the case where a user performs a "jogging" task. When the user inputs "jogging" as the task, the server determines high-tempo, energetic musical characteristics and generates music. If the user's heart rate becomes too high while jogging, the server adjusts the music to a slower tempo to maintain a calm state.

[0219] This system allows users to have a musical experience that helps them efficiently perform their daily tasks, maximizing their task performance.

[0220] The following describes the processing flow.

[0221] Step 1:

[0222] The user launches an application on their device and enters information about their current task, such as "housework" or "data entry." The device then accurately receives this input information.

[0223] Step 2:

[0224] The terminal sends task information received from the user to the server. The server receives this information and prepares to analyze the task details.

[0225] Step 3:

[0226] The server uses natural language processing technology to analyze task information and identify relevant keywords and work environment. Based on this, it derives the optimal musical characteristics for the task, namely tempo and genre.

[0227] Step 4:

[0228] Based on the determined musical characteristics, the server generates new music data using a generation algorithm. This music data also takes into account the user's past selection history.

[0229] Step 5:

[0230] The device collects activity data in real time from the user's smart device. This activity data includes heart rate, exercise level, and posture information. The device sends this data to the server.

[0231] Step 6:

[0232] The server analyzes the collected activity data and dynamically adjusts the music data to suit the user's situation. For example, if the user is seeking relaxation, the music tempo will be slowed down.

[0233] Step 7:

[0234] The terminal receives music data that has been adjusted from the server and delivers it to the user's device. This ensures that the user can always listen to music that is appropriate for their task.

[0235] Step 8:

[0236] Users perform tasks while listening to the provided music. The music adapts to the user's activity rhythm, improving work efficiency.

[0237] (Example 1)

[0238] 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."

[0239] In modern society, many people use music to carry out their activities efficiently, but selecting music that is suitable for individual activities and physiological states is not easy. In particular, there is a need to optimize activity performance by dynamically changing the characteristics of music according to the activity. Conventional music selection methods have the challenge of not being able to provide music that is quickly and appropriately suited to this purpose.

[0240] 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.

[0241] In this invention, the server includes means for receiving activity information provided by a human-operated device, means for determining optimal acoustic characteristics using the activity information, and means for generating acoustic data based on the acoustic characteristics. This makes it possible to dynamically provide music that is suitable for the user's activity and physiological state.

[0242] "Activity information" refers to information provided by a person when they perform a specific activity, such as exercise or work activities.

[0243] "Acoustic properties" refer to the characteristics of a sound, including elements such as tempo, rhythm, and tone.

[0244] "Acoustic data" refers to sound information generated based on acoustic characteristics, representing music or sounds tailored to specific activities or states.

[0245] "Activity status data" refers to information that records a person's physical or behavioral state in real time, including heart rate and movement information.

[0246] "Means of adjustment" refers to methods of processing acoustic data to modify or optimize it according to activity status data.

[0247] "Means of provision" refers to methods of transmitting and making available the generated audio data to people.

[0248] To implement this invention, a system needs to be built in cooperation with a user, a terminal, and a server. The user inputs activity information through a dedicated application on a digital device. The information that the user can input could include activity details such as exercise or work. For example, activity information such as "jogging" or "document creation" can be entered.

[0249] The terminal is responsible for receiving the entered activity information and transmitting it to the server via the network. This transmission process uses data communication based on the HTTP protocol.

[0250] The server uses natural language processing techniques to analyze the received activity information. This process uses Python's natural language processing libraries, NLTK and spaCy, to analyze text data and determine the acoustic characteristics required for the activity. In determining the acoustic characteristics, the server retrieves the user's past preference history from a database (e.g., MySQL) and takes it into consideration.

[0251] After the acoustic characteristics are determined, the server generates acoustic data using a generative AI model. The generation of acoustic data is performed using a machine learning model specifically designed for music generation. Specifically, advanced music generation models such as OpenAI's MuseNet are used. An example of a prompt to the generative AI model would be, "Generate fast-paced, energetic music."

[0252] The device uses sensors from the smart device to collect user activity data. This activity data includes heart rate, movement, and location information, which is transmitted to the server in real time.

[0253] The server dynamically adjusts the generated audio data based on the received activity status data. When the user needs to concentrate, the rhythm and tempo of the music are changed accordingly to provide an audio experience tailored to the purpose. In this way, the system aims to improve the quality of activity by providing a comfortable audio environment that is in line with the user's situation and needs.

[0254] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0255] Step 1:

[0256] The user enters activity information using a terminal application. This information includes specific activities such as "jogging" or "studying." This information becomes the input data sent to the next processing step.

[0257] Step 2:

[0258] The terminal sends activity information received from the user to the server. The data is transferred to the server using the HTTP protocol, and the server receives it as input data for analysis. In this case, the data is generally sent in JSON format.

[0259] Step 3:

[0260] The server performs natural language processing based on the received activity information. Specifically, it analyzes the text data using Python's NLTK or spaCy to extract important keywords and context. Based on this processing, foundational data for determining acoustic characteristics is generated. This foundational data includes, for example, indicators related to appropriate tempo and rhythm.

[0261] Step 4:

[0262] The server retrieves and analyzes past user preference history from a database. This historical data is retrieved using SQL from relational databases such as MySQL, and current activity is compared with past patterns to further refine the acoustic characteristics. This process determines the detailed parameters necessary for generating the music.

[0263] Step 5:

[0264] The server generates acoustic data using a generative AI model based on acoustic characteristics. The AI ​​model used is specialized for music generation, and specific examples such as "Generate high-tempo, energetic music" are input to the model as prompts. Based on this, the model outputs music data. This data is sent to the terminal as a music file.

[0265] Step 6:

[0266] The device collects user activity data from the smart device's sensors. This data includes real-time heart rate, motion data, and location information, and is immediately transmitted to the server. The activity data provides the input data necessary for real-time music adjustments.

[0267] Step 7:

[0268] The server dynamically adjusts the generated audio data based on activity data. For example, if the user's heart rate increases, the tempo of the music is adjusted. The purpose of this process is to always maintain optimal user performance. The adjusted music data is then delivered to the user through the terminal, creating a feedback loop.

[0269] (Application Example 1)

[0270] 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."

[0271] There is a lack of means to provide music in real time that is optimal for the daily activities and tasks of diverse users, thereby promoting effects such as improved concentration and relaxation. Furthermore, the technology to achieve this using smart audio devices is not yet sufficiently developed, which is a challenge.

[0272] 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.

[0273] In this invention, the server includes means for receiving work information from the user, means for determining appropriate musical characteristics based on the work information, and means for generating acoustic data based on the musical characteristics. This makes it possible to provide optimal acoustic data in real time for a variety of user activities.

[0274] A "user" refers to an individual who interacts with this system and provides work information and activity data.

[0275] "Work information" refers to information about the tasks and activities that the user is trying to perform, and it is the data that forms the basis for determining the musical characteristics.

[0276] "Musical characteristics" refer to elements that describe the sonic features optimized for the user's work or activity, such as the rhythm, tempo, and atmosphere of the music.

[0277] "Audio data" refers to digital data that represents sound information generated based on musical characteristics and is provided to the user as music.

[0278] "Activity data" refers to data acquired in real time, such as the user's physiological information and movement information, and is used to adjust the acoustic data.

[0279] A "smart audio device" refers to a device that plays audio data via an Internet connection and provides users with a music experience.

[0280] To implement this invention, a system is constructed in which a server, a terminal, and smart audio devices operate in cooperation. A user inputs work information related to daily activities through the smart audio device. Then, the terminal receives this input information and transmits it to the server. The server uses natural language processing technology to analyze the received work information. Specifically, Python and natural language processing libraries (such as NLTK or spaCy) are utilized to determine the optimal music characteristics for the user's task.

[0281] Once the music characteristics are determined, the server generates audio data. For the generation, a music generation library (such as Magenta, etc.) is used to create digital music piece data that matches the music characteristics. The generated audio data is stored on the cloud and further adjusted based on the user's activity data.

[0282] The user's terminal collects activity data from the smart audio device in real time and provides it to the server. This activity data includes heart rate, movements, location information, etc. The server uses these data to dynamically adjust the audio data and provide an optimal music experience for the user's current situation. As a specific example, when the user is engaged in a fitness activity, high-tempo music is provided, but when the heart rate increases, the tempo is adjusted to be slower, etc.

[0283] This system can improve its performance and concentration by providing music optimized for various activities of the user using a generative AI model. It is possible to improve the user experience using a prompt sentence such as "When the user is 'concentrating on work', what kind of music is optimal?"

[0284] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0285] Step 1:

[0286] The user inputs work information via a smart audio device, and the terminal receives the information. The input voice data is converted into text data using voice recognition software (e.g., Google Speech-to-Text API). The converted text data is sent to the server.

[0287] Step 2:

[0288] The server analyzes the received work information text. Using natural language processing technology and leveraging Python's NLTK and spaCy libraries, it analyzes the work information to determine the optimal music characteristics for the user's task. The input is the text data of the work information, and the output is the data of music characteristics (e.g., tempo, rhythm).

[0289] Step 3:

[0290] The server generates audio data based on the music characteristics. Using a music generation library (e.g., Magenta), it generates audio data with specific music characteristics. At this time, it utilizes a generation AI model. The input is the data of music characteristics, and the output is the generated audio data (digital music file).

[0291] Step 4:

[0292] The terminal collects activity data in real time from the smart audio device. This includes heart rate, motion information, location information, etc. The collected data is obtained using sensor technology and provided to the server. The input is the user's physiological data, and the output is the activity data reflecting this.

[0293] Step 5:

[0294] The server analyzes the received activity data and adjusts the acoustic data in real time. As a preprocessing step, it cleanses the data using a data analysis tool (e.g., Pandas) and dynamically adjusts the tempo and rhythm of the music according to the type of activity. The input is activity data, and the output is the adjusted acoustic data.

[0295] Step 6:

[0296] The adjusted audio data is streamed to the user via the internet through a terminal using a smart audio device. This allows the user to receive an optimal music experience in real time. The input is the adjusted audio data, and the output is the music stream to the user.

[0297] 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.

[0298] In implementing the present invention, a system is configured that combines a user, a terminal, a server, and an emotion engine. The user can use a terminal with a dedicated application installed to input their daily tasks and current emotions. The terminal receives the information input by the user and transmits it to the server.

[0299] The server receives this information and determines the optimal musical characteristics related to the task, such as tempo and style. The emotion engine within the server recognizes the user's emotions in real time by analyzing the user's facial expressions and voice tone. This allows the server to evaluate how the user's emotional state affects the musical characteristics and adjust the music accordingly.

[0300] The server uses a generation algorithm to generate music data based on music characteristics. This generated music data is dynamically adjusted in a form that matches the user's activity data and emotional data. By adjusting the genre and tempo of the music, the user can listen to music according to their own emotions and activity situations, and the accomplishment of tasks is promoted.

[0301] As an example, when the user wants to "concentrate", the server generates and distributes energetic but emotionally calming music at a fast tempo. When the emotion engine detects stress from the user's face or voice, it instructs a switch to a gentle melody. Thereby, the user obtains an adaptive music experience and can easily concentrate on the task while reducing stress.

[0302] With this system, the user can efficiently accomplish tasks while listening to music optimized for their own emotions and activities, achieving the maximization of performance.

[0303] The following describes the processing flow.

[0304] Step 1:

[0305] The user accesses the application on the terminal and inputs the content and mood of the current task. The terminal receives this information and prepares to start processing.

[0306] Step 2:

[0307] The terminal sends the task and mood information obtained from the user to the server. The server starts analysis based on this information.

[0308] Step 3:

[0309] The server analyzes the received task information using natural language processing technology to identify the characteristics of the task. Thereby, criteria regarding general music characteristics are set.

[0310] Step 4:

[0311] An emotion engine built into the server analyzes real-time facial expression data and voice tone sent from the user's device to recognize the user's emotional state.

[0312] Step 5:

[0313] The server generates optimal music data, taking into account the musical characteristics suitable for the task and the user's emotional state. It executes a generation algorithm to create personalized music.

[0314] Step 6:

[0315] The device uses a smart device to collect user activity data. This activity data includes the user's heart rate and step count. The collected data is sent to a server.

[0316] Step 7:

[0317] The server dynamically adjusts the tempo and style of the music data based on activity data and recognized emotion data. This ensures that the music is tailored to the user's current state.

[0318] Step 8:

[0319] The device receives the optimized music data and plays it on the user's device. In this way, the user can concentrate on their tasks while listening to optimized music.

[0320] Step 9:

[0321] Through an adaptive music experience, users can lower their stress levels and increase their concentration. In this state, they can continue tasks and improve their efficiency.

[0322] (Example 2)

[0323] 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".

[0324] In modern society, many people face various stresses and pressures, which often lead to decreased work efficiency. Furthermore, conventional music distribution systems struggle to provide music that is tailored to the emotional state of individual users, resulting in a challenge in providing a music experience that matches the user's situation and emotions.

[0325] 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.

[0326] In this invention, the server includes means for receiving task data and emotion data from the user, means for analyzing the user's facial expression data and voice data to recognize the emotional state in real time, and means for generating music data based on the emotional state and music attributes. This makes it possible to provide optimal music according to the user's situation, thereby improving work efficiency and stabilizing emotions.

[0327] "Task data" refers to information about the tasks and goals that a user intends to accomplish. This data includes details about the user's action plan and activities.

[0328] "Emotional data" refers to information that indicates a user's emotional state. This data is obtained from facial expressions, tone of voice, and other sources, and is used to analyze the user's mental state.

[0329] "Musical attributes" are parameters that indicate the characteristics of music, including tempo, melody, and harmony. They are elements that create an appropriate musical experience according to the user's situation.

[0330] "Facial expression data" is data obtained by analyzing a user's facial expressions. This allows us to infer the user's emotional state.

[0331] "Voice data" refers to data collected from the user's voice, and is used to evaluate emotional state by analyzing factors such as voice tone and pitch.

[0332] "Emotional state" refers to the state that indicates the user's current mental or emotional condition. This state is grasped in real time through data analysis.

[0333] "Music data" refers to data related to generated music, including information necessary for music playback. It is generated and adjusted according to the target user.

[0334] "Dynamic adjustment" refers to the process of modifying the attributes of music data in real time according to the user's changing circumstances and emotional state.

[0335] This invention provides a system that combines a user, a terminal, a server, and an emotion engine to improve a specific user experience. The user uses a terminal with a dedicated application installed. The application can be installed on a smartphone or tablet, and task data and emotion data can be input through that application.

[0336] The terminal quickly and securely transmits data entered by the user to the server. The data is transferred over the network and processed on the server. On the server, a computer system equipped with a high-performance processor runs, performing data analysis using a generative AI model. The server executes a process to determine music attributes based on the user's task data, using a proprietary algorithm.

[0337] Furthermore, the server is equipped with an emotion engine that analyzes the user's facial expression data and voice data to recognize the user's emotional state in real time. This analysis uses machine learning and voice analysis technologies. For example, if a user inputs task data such as "I want to improve my concentration," and the emotion engine recognizes that the user's facial expression indicates a state of concentration, fast-paced music will be generated.

[0338] The generated music data is dynamically adjusted on the server according to the user's changing activity and emotional state. This system allows for switching to calmer, slower-tempo music if stress is detected. The generating AI model is constantly learning from new data and used to continuously optimize the music.

[0339] A concrete example of a prompt might be a question like, "Which music attributes should be adjusted to suggest appropriate music when the user is in a relaxed state?" In this way, users can enjoy a music experience tailored to their situation and improve the efficiency of their tasks.

[0340] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0341] Step 1:

[0342] Users input task data and emotional data through their device. This data includes information such as "What tasks do I want to focus on today?" and "What is my current emotional state?". The device then identifies the data obtained from the user and organizes it into a dataset to prepare for the next step.

[0343] Step 2:

[0344] The device sends task data and sentiment data received from the user to the server. The transmitted data travels over the network and its format is adjusted for processing on the server. Specifically, the data is sent via HTTP requests and received as input by the server.

[0345] Step 3:

[0346] The server determines music attributes based on the received task data. Based on the input task data, logic is executed to select the music tempo and genre. For example, if the task is to improve concentration, the server will select and output music with energetic and fast tempo attributes.

[0347] Step 4:

[0348] The server uses an emotion engine to analyze the user's emotional state. Inputs include the user's facial expression data and voice data, which are analyzed to recognize the user's emotions in real time. Emotion analysis is performed using machine learning algorithms, generating outputs that quantify the user's stress level and concentration level.

[0349] Step 5:

[0350] The server integrates data on musical attributes and emotional states to generate musical data. The music generation algorithm takes this data as input and outputs appropriate musical data. For example, it might generate fast-paced music suitable for a state of concentration.

[0351] Step 6:

[0352] The server dynamically adjusts the generated music data according to the user's activity level. The input activity data is analyzed in real time as feedback, and the music tempo and volume are adjusted accordingly. The output is music data tailored to the user's state.

[0353] Step 7:

[0354] The device plays the adjusted music data received from the server to the user. By listening to this adjusted music, the user can enjoy a musical experience adapted to their emotions and tasks at that moment. In this way, the device provides real-time music delivery to the user.

[0355] (Application Example 2)

[0356] 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."

[0357] The challenge in online shopping is to improve the purchasing experience by providing an appropriate audio experience that responds to the user's emotional state. In particular, it is necessary to alleviate user stress, maintain their desire to purchase, and make the shopping process more comfortable.

[0358] 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.

[0359] In this invention, the server includes means for receiving emotional state information and shopping behavior information from the user, means for determining appropriate acoustic characteristics based on the emotional state information, and means for generating acoustic data based on the acoustic characteristics. This enables a comfortable and stress-free shopping experience by dynamically adjusting the acoustic experience according to the user's emotional state.

[0360] "User emotional state information" refers to data that indicates a user's emotions, obtained based on their facial expressions and voice.

[0361] "Shopping behavior information" refers to data related to a user's behavior patterns and purchase history when shopping online.

[0362] "Acoustic characteristics" refer to the fundamental elements that make up music, such as genre, tempo, and volume.

[0363] "Audio data" refers to digital information that is played back as music or sound.

[0364] "Facial expression information" refers to data obtained from the user's facial movements and expressions, which makes it possible to infer their emotions.

[0365] "Audio information" refers to data about the sounds and tone of voice emitted by the user, and is used to analyze the user's emotional state.

[0366] To implement this invention, the user needs to install a dedicated application on a mobile device such as a smartphone. When the user launches the application and engages in online shopping, the device uses its built-in camera and microphone to collect the user's facial expressions and voice information. This information is transmitted to a server in real time. Based on this information, the server performs emotion analysis and generates information about the user's emotional state.

[0367] The server utilizes emotional state information and shopping behavior information to determine acoustic characteristics. This can involve using emotion analysis APIs provided by cloud services such as Google Cloud. Based on the determined acoustic characteristics, acoustic data is generated using a music generation library with Python. This generated acoustic data is then adjusted to match the user's emotions and delivered to the device.

[0368] For example, if the server detects that a user is starting to feel tired after shopping for an extended period, it can play relaxing music to sustain the user's desire to purchase. In this way, it is possible to provide an optimized audio experience for the user.

[0369] An example of an input prompt for a generative AI model might be: "If the user is currently experiencing stress, please suggest what musical characteristics would be most relaxing." Using this prompt, the generative AI can generate suggestions for optimizing acoustic characteristics.

[0370] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0371] Step 1:

[0372] The user launches a smartphone application. The device collects the user's facial expressions and voice information in real time through its built-in camera and microphone, and sends this as input data to the server. This input is basic data used to analyze the user's emotional state.

[0373] Step 2:

[0374] The server performs emotion analysis using the received facial expression and voice information. This utilizes the Google Cloud Emotion Analysis API. Based on the emotion data as input, a data analysis algorithm is used to output information about the user's emotional state. This analysis identifies the user's current emotion.

[0375] Step 3:

[0376] The server receives user emotional state and shopping behavior information as input to determine acoustic characteristics. It then inputs a prompt to the generative AI model, such as, "If the user is currently experiencing stress, what musical characteristics would provide the most relaxation?" The server evaluates this prompt and outputs appropriate acoustic characteristics, including music genre, tempo, and volume.

[0377] Step 4:

[0378] The server generates acoustic data based on the determined acoustic characteristics. Using a music generation library with Python, it outputs sound data based on the input acoustic characteristics. The music generated here is designed to match the user's emotions.

[0379] Step 5:

[0380] The server adjusts the generated audio data in real time and delivers it to the user. During this process, it references input information, including the latest data on the user's shopping activities, to dynamically optimize the music's tempo and atmosphere. Finally, the adjusted audio data is sent to the user's device, allowing them to listen to it while shopping for a comfortable and stress-free shopping experience.

[0381] 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.

[0382] 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.

[0383] 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.

[0384] [Third Embodiment]

[0385] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0386] 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.

[0387] 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).

[0388] 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.

[0389] 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.

[0390] 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).

[0391] 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.

[0392] 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.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] 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".

[0397] To implement this invention, a system is constructed in which a user, a terminal, and a server work together. The user inputs daily task information through an application on the terminal and provides it to the system. For example, the user can input tasks such as "create documents" or "training." The terminal receives this input information and sends it to the server.

[0398] Based on the received task information, the server analyzes the task content using natural language processing techniques. Here, the server determines the optimal musical characteristics for the task, such as tempo and pitch. The user's past selection and activity history are considered in determining these musical characteristics. The server then uses a generation algorithm to generate music data based on the determined musical characteristics. This generated music data is optimized for the user's activities and helps the user efficiently complete the task.

[0399] The terminal collects activity data from the user's smart device. This activity data includes heart rate, movement, and location information, and is provided to the server in real time. The server dynamically adjusts the generated music data according to the activity data. If the user wants to improve their concentration, the rhythm can be changed or the tempo adjusted to improve work efficiency. By dynamically changing the music to suit the user in this way, an optimal state can always be maintained.

[0400] As a concrete example, consider the case where a user performs a "jogging" task. When the user inputs "jogging" as the task, the server determines high-tempo, energetic musical characteristics and generates music. If the user's heart rate becomes too high while jogging, the server adjusts the music to a slower tempo to maintain a calm state.

[0401] This system allows users to have a musical experience that helps them efficiently perform their daily tasks, maximizing their task performance.

[0402] The following describes the processing flow.

[0403] Step 1:

[0404] The user launches an application on their device and enters information about their current task, such as "housework" or "data entry." The device then accurately receives this input information.

[0405] Step 2:

[0406] The terminal sends task information received from the user to the server. The server receives this information and prepares to analyze the task details.

[0407] Step 3:

[0408] The server uses natural language processing technology to analyze task information and identify relevant keywords and work environment. Based on this, it derives the optimal musical characteristics for the task, namely tempo and genre.

[0409] Step 4:

[0410] Based on the determined musical characteristics, the server generates new music data using a generation algorithm. This music data also takes into account the user's past selection history.

[0411] Step 5:

[0412] The device collects activity data in real time from the user's smart device. This activity data includes heart rate, exercise level, and posture information. The device sends this data to the server.

[0413] Step 6:

[0414] The server analyzes the collected activity data and dynamically adjusts the music data to suit the user's situation. For example, if the user is seeking relaxation, the music tempo will be slowed down.

[0415] Step 7:

[0416] The terminal receives music data that has been adjusted from the server and delivers it to the user's device. This ensures that the user can always listen to music that is appropriate for their task.

[0417] Step 8:

[0418] Users perform tasks while listening to the provided music. The music adapts to the user's activity rhythm, improving work efficiency.

[0419] (Example 1)

[0420] 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."

[0421] In modern society, many people use music to carry out their activities efficiently, but selecting music that is suitable for individual activities and physiological states is not easy. In particular, there is a need to optimize activity performance by dynamically changing the characteristics of music according to the activity. Conventional music selection methods have the challenge of not being able to provide music that is quickly and appropriately suited to this purpose.

[0422] 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.

[0423] In this invention, the server includes means for receiving activity information provided by a human-operated device, means for determining optimal acoustic characteristics using the activity information, and means for generating acoustic data based on the acoustic characteristics. This makes it possible to dynamically provide music that is suitable for the user's activity and physiological state.

[0424] "Activity information" refers to information provided by a person when they perform a specific activity, such as exercise or work activities.

[0425] "Acoustic properties" refer to the characteristics of a sound, including elements such as tempo, rhythm, and tone.

[0426] "Acoustic data" refers to sound information generated based on acoustic characteristics, representing music or sounds tailored to specific activities or states.

[0427] "Activity status data" refers to information that records a person's physical or behavioral state in real time, including heart rate and movement information.

[0428] "Means of adjustment" refers to methods of processing acoustic data to modify or optimize it according to activity status data.

[0429] "Means of provision" refers to methods of transmitting and making available the generated audio data to people.

[0430] To implement this invention, a system needs to be built in cooperation with a user, a terminal, and a server. The user inputs activity information through a dedicated application on a digital device. The information that the user can input could include activity details such as exercise or work. For example, activity information such as "jogging" or "document creation" can be entered.

[0431] The terminal is responsible for receiving the entered activity information and transmitting it to the server via the network. This transmission process uses data communication based on the HTTP protocol.

[0432] The server uses natural language processing techniques to analyze the received activity information. This process uses Python's natural language processing libraries, NLTK and spaCy, to analyze text data and determine the acoustic characteristics required for the activity. In determining the acoustic characteristics, the server retrieves the user's past preference history from a database (e.g., MySQL) and takes it into consideration.

[0433] After the acoustic characteristics are determined, the server generates acoustic data using a generative AI model. The generation of acoustic data is performed using a machine learning model specifically designed for music generation. Specifically, advanced music generation models such as OpenAI's MuseNet are used. An example of a prompt to the generative AI model would be, "Generate fast-paced, energetic music."

[0434] The device uses sensors from the smart device to collect user activity data. This activity data includes heart rate, movement, and location information, which is transmitted to the server in real time.

[0435] The server dynamically adjusts the generated audio data based on the received activity status data. When the user needs to concentrate, the rhythm and tempo of the music are changed accordingly to provide an audio experience tailored to the purpose. In this way, the system aims to improve the quality of activity by providing a comfortable audio environment that is in line with the user's situation and needs.

[0436] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0437] Step 1:

[0438] The user enters activity information using a terminal application. This information includes specific activities such as "jogging" or "studying." This information becomes the input data sent to the next processing step.

[0439] Step 2:

[0440] The terminal sends activity information received from the user to the server. The data is transferred to the server using the HTTP protocol, and the server receives it as input data for analysis. In this case, the data is generally sent in JSON format.

[0441] Step 3:

[0442] The server performs natural language processing based on the received activity information. Specifically, it analyzes the text data using Python's NLTK or spaCy to extract important keywords and context. Based on this processing, foundational data for determining acoustic characteristics is generated. This foundational data includes, for example, indicators related to appropriate tempo and rhythm.

[0443] Step 4:

[0444] The server retrieves and analyzes past user preference history from a database. This historical data is retrieved using SQL from relational databases such as MySQL, and current activity is compared with past patterns to further refine the acoustic characteristics. This process determines the detailed parameters necessary for generating the music.

[0445] Step 5:

[0446] The server generates acoustic data using a generative AI model based on acoustic characteristics. The AI ​​model used is specialized for music generation, and specific examples such as "Generate high-tempo, energetic music" are input to the model as prompts. Based on this, the model outputs music data. This data is sent to the terminal as a music file.

[0447] Step 6:

[0448] The device collects user activity data from the smart device's sensors. This data includes real-time heart rate, motion data, and location information, and is immediately transmitted to the server. The activity data provides the input data necessary for real-time music adjustments.

[0449] Step 7:

[0450] The server dynamically adjusts the generated audio data based on activity data. For example, if the user's heart rate increases, the tempo of the music is adjusted. The purpose of this process is to always maintain optimal user performance. The adjusted music data is then delivered to the user through the terminal, creating a feedback loop.

[0451] (Application Example 1)

[0452] 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."

[0453] There is a lack of means to provide music in real time that is optimal for the daily activities and tasks of diverse users, thereby promoting effects such as improved concentration and relaxation. Furthermore, the technology to achieve this using smart audio devices is not yet sufficiently developed, which is a challenge.

[0454] 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.

[0455] In this invention, the server includes means for receiving work information from the user, means for determining appropriate musical characteristics based on the work information, and means for generating acoustic data based on the musical characteristics. This makes it possible to provide optimal acoustic data in real time for a variety of user activities.

[0456] A "user" refers to an individual who interacts with this system and provides work information and activity data.

[0457] "Work information" refers to information about the tasks and activities that the user is trying to perform, and it is the data that forms the basis for determining the musical characteristics.

[0458] "Musical characteristics" refer to elements that describe the sonic features optimized for the user's work or activity, such as the rhythm, tempo, and atmosphere of the music.

[0459] "Audio data" refers to digital data that represents sound information generated based on musical characteristics and is provided to the user as music.

[0460] "Activity data" refers to data acquired in real time, such as the user's physiological information and movement information, and is used to adjust the acoustic data.

[0461] A "smart audio device" refers to a device that plays audio data via an internet connection and provides users with a music experience.

[0462] To implement this invention, a system is constructed in which a server, a terminal, and a smart audio device work together. The user inputs work information related to their daily activities through the smart audio device. The terminal then receives this input information and sends it to the server. The server uses natural language processing technology to analyze the received work information. Specifically, it utilizes Python and natural language processing libraries (e.g., NLTK and spaCy) to determine the optimal musical characteristics for the user's task.

[0463] Once the musical characteristics are determined, the server generates audio data. This generation uses a music generation library (e.g., Magenta) to create digital music data tailored to the musical characteristics. The generated audio data is stored in the cloud and further refined based on user activity data.

[0464] The user's device collects activity data in real time from smart audio equipment and provides it to the server. This activity data includes heart rate, movement, and location information. The server uses this data to dynamically adjust the audio data and provide a music experience that is optimal for the user's current situation. For example, if the user is doing fitness activities, high-tempo music is provided, but if the heart rate increases, the tempo is slowed down.

[0465] This system can improve performance and concentration by providing music optimized for various user activities using a generative AI model. It can enhance the user experience by using prompts such as, "What kind of music is best suited for a user who is 'concentrating on work'?"

[0466] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0467] Step 1:

[0468] The user inputs work information by voice through a smart audio device, and the device receives that information. The input voice data is converted into text data using speech recognition software (e.g., Google Speech-to-Text API). The converted text data is sent to the server.

[0469] Step 2:

[0470] The server analyzes the received work information text. Using natural language processing techniques, it analyzes the work information using Python's NLTK and spaCy libraries to determine the optimal musical characteristics for the user's task. The input is text data of the work information, and the output is data of musical characteristics (e.g., tempo, rhythm).

[0471] Step 3:

[0472] The server generates audio data based on musical characteristics. It uses a music generation library (e.g., Magenta) to generate audio data with specific musical characteristics. A generation AI model is utilized in this process. The input is data on musical characteristics, and the output is the generated audio data (digital music file).

[0473] Step 4:

[0474] The device collects activity data in real time from smart audio devices. This includes heart rate, motion information, and location information. The collected data is acquired using sensor technology and provided to the server. The input is the user's physiological data, and the output is activity data that reflects this.

[0475] Step 5:

[0476] The server analyzes the received activity data and adjusts the acoustic data in real time. As a preprocessing step, it cleanses the data using a data analysis tool (e.g., Pandas) and dynamically adjusts the tempo and rhythm of the music according to the type of activity. The input is activity data, and the output is the adjusted acoustic data.

[0477] Step 6:

[0478] The adjusted audio data is streamed to the user via the internet through a terminal using a smart audio device. This allows the user to receive an optimal music experience in real time. The input is the adjusted audio data, and the output is the music stream to the user.

[0479] 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.

[0480] In implementing the present invention, a system is configured that combines a user, a terminal, a server, and an emotion engine. The user can use a terminal with a dedicated application installed to input their daily tasks and current emotions. The terminal receives the information input by the user and transmits it to the server.

[0481] The server receives this information and determines the optimal musical characteristics related to the task, such as tempo and style. The emotion engine within the server recognizes the user's emotions in real time by analyzing the user's facial expressions and voice tone. This allows the server to evaluate how the user's emotional state affects the musical characteristics and adjust the music accordingly.

[0482] The server uses a generation algorithm to generate music data based on musical characteristics. This generated music data is dynamically adjusted to match the user's activity and emotional data. By adjusting the music genre and tempo, users can listen to music that suits their emotions and activity level, thereby facilitating task completion.

[0483] For example, if a user wants to "concentrate," the server generates and delivers fast-paced, energetic yet emotionally calming music. If the emotion engine detects stress from the user's face or voice, it instructs the system to switch to a calmer melody. This provides the user with an adaptive musical experience, making it easier to concentrate on tasks while reducing stress.

[0484] This system allows users to efficiently perform tasks while listening to music optimized for their emotions and activities, thereby maximizing their performance.

[0485] The following describes the processing flow.

[0486] Step 1:

[0487] The user accesses the application on their device and enters details of their current task and their feelings. The device receives this information and prepares to begin processing.

[0488] Step 2:

[0489] The terminal sends task and emotional information obtained from the user to the server. The server then begins analysis based on this information.

[0490] Step 3:

[0491] The server analyzes the received task information using natural language processing techniques to identify the characteristics of the task. This allows it to establish general standards for musical characteristics.

[0492] Step 4:

[0493] An emotion engine built into the server analyzes real-time facial expression data and voice tone sent from the user's device to recognize the user's emotional state.

[0494] Step 5:

[0495] The server generates optimal music data, taking into account the musical characteristics suitable for the task and the user's emotional state. It executes a generation algorithm to create personalized music.

[0496] Step 6:

[0497] The device uses a smart device to collect user activity data. This activity data includes the user's heart rate and step count. The collected data is sent to a server.

[0498] Step 7:

[0499] The server dynamically adjusts the tempo and style of the music data based on activity data and recognized emotion data. This ensures that the music is tailored to the user's current state.

[0500] Step 8:

[0501] The device receives the optimized music data and plays it on the user's device. In this way, the user can concentrate on their tasks while listening to optimized music.

[0502] Step 9:

[0503] Through an adaptive music experience, users can lower their stress levels and increase their concentration. In this state, they can continue tasks and improve their efficiency.

[0504] (Example 2)

[0505] 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."

[0506] In modern society, many people face various stresses and pressures, which often lead to decreased work efficiency. Furthermore, conventional music distribution systems struggle to provide music that is tailored to the emotional state of individual users, resulting in a challenge in providing a music experience that matches the user's situation and emotions.

[0507] 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.

[0508] In this invention, the server includes means for receiving task data and emotion data from the user, means for analyzing the user's facial expression data and voice data to recognize the emotional state in real time, and means for generating music data based on the emotional state and music attributes. This makes it possible to provide optimal music according to the user's situation, thereby improving work efficiency and stabilizing emotions.

[0509] "Task data" refers to information about the tasks and goals that a user intends to accomplish. This data includes details about the user's action plan and activities.

[0510] "Emotional data" refers to information that indicates a user's emotional state. This data is obtained from facial expressions, tone of voice, and other sources, and is used to analyze the user's mental state.

[0511] "Musical attributes" are parameters that indicate the characteristics of music, including tempo, melody, and harmony. They are elements that create an appropriate musical experience according to the user's situation.

[0512] "Facial expression data" is data obtained by analyzing a user's facial expressions. This allows us to infer the user's emotional state.

[0513] "Voice data" refers to data collected from the user's voice, and is used to evaluate emotional state by analyzing factors such as voice tone and pitch.

[0514] "Emotional state" refers to the state that indicates the user's current mental or emotional condition. This state is grasped in real time through data analysis.

[0515] "Music data" refers to data related to generated music, including information necessary for music playback. It is generated and adjusted according to the target user.

[0516] "Dynamic adjustment" refers to the process of modifying the attributes of music data in real time according to the user's changing circumstances and emotional state.

[0517] This invention provides a system that combines a user, a terminal, a server, and an emotion engine to improve a specific user experience. The user uses a terminal with a dedicated application installed. The application can be installed on a smartphone or tablet, and task data and emotion data can be input through that application.

[0518] The terminal quickly and securely transmits data entered by the user to the server. The data is transferred over the network and processed on the server. On the server, a computer system equipped with a high-performance processor runs, performing data analysis using a generative AI model. The server executes a process to determine music attributes based on the user's task data, using a proprietary algorithm.

[0519] Furthermore, the server is equipped with an emotion engine that analyzes the user's facial expression data and voice data to recognize the user's emotional state in real time. This analysis uses machine learning and voice analysis technologies. For example, if a user inputs task data such as "I want to improve my concentration," and the emotion engine recognizes that the user's facial expression indicates a state of concentration, fast-paced music will be generated.

[0520] The generated music data is dynamically adjusted on the server according to the user's changing activity and emotional state. This system allows for switching to calmer, slower-tempo music if stress is detected. The generating AI model is constantly learning from new data and used to continuously optimize the music.

[0521] A concrete example of a prompt might be a question like, "Which music attributes should be adjusted to suggest appropriate music when the user is in a relaxed state?" In this way, users can enjoy a music experience tailored to their situation and improve the efficiency of their tasks.

[0522] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0523] Step 1:

[0524] Users input task data and emotional data through their device. This data includes information such as "What tasks do I want to focus on today?" and "What is my current emotional state?". The device then identifies the data obtained from the user and organizes it into a dataset to prepare for the next step.

[0525] Step 2:

[0526] The device sends task data and sentiment data received from the user to the server. The transmitted data travels over the network and its format is adjusted for processing on the server. Specifically, the data is sent via HTTP requests and received as input by the server.

[0527] Step 3:

[0528] The server determines music attributes based on the received task data. Based on the input task data, logic is executed to select the music tempo and genre. For example, if the task is to improve concentration, the server will select and output music with energetic and fast tempo attributes.

[0529] Step 4:

[0530] The server uses an emotion engine to analyze the user's emotional state. Inputs include the user's facial expression data and voice data, which are analyzed to recognize the user's emotions in real time. Emotion analysis is performed using machine learning algorithms, generating outputs that quantify the user's stress level and concentration level.

[0531] Step 5:

[0532] The server integrates data on musical attributes and emotional states to generate musical data. The music generation algorithm takes this data as input and outputs appropriate musical data. For example, it might generate fast-paced music suitable for a state of concentration.

[0533] Step 6:

[0534] The server dynamically adjusts the generated music data according to the user's activity level. The input activity data is analyzed in real time as feedback, and the music tempo and volume are adjusted accordingly. The output is music data tailored to the user's state.

[0535] Step 7:

[0536] The device plays the adjusted music data received from the server to the user. By listening to this adjusted music, the user can enjoy a musical experience adapted to their emotions and tasks at that moment. In this way, the device provides real-time music delivery to the user.

[0537] (Application Example 2)

[0538] 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."

[0539] The challenge in online shopping is to improve the purchasing experience by providing an appropriate audio experience that responds to the user's emotional state. In particular, it is necessary to alleviate user stress, maintain their desire to purchase, and make the shopping process more comfortable.

[0540] 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.

[0541] In this invention, the server includes means for receiving emotional state information and shopping behavior information from the user, means for determining appropriate acoustic characteristics based on the emotional state information, and means for generating acoustic data based on the acoustic characteristics. This enables a comfortable and stress-free shopping experience by dynamically adjusting the acoustic experience according to the user's emotional state.

[0542] "User emotional state information" refers to data that indicates a user's emotions, obtained based on their facial expressions and voice.

[0543] "Shopping behavior information" refers to data related to a user's behavior patterns and purchase history when shopping online.

[0544] "Acoustic characteristics" refer to the fundamental elements that make up music, such as genre, tempo, and volume.

[0545] "Audio data" refers to digital information that is played back as music or sound.

[0546] "Facial expression information" refers to data obtained from the user's facial movements and expressions, which makes it possible to infer their emotions.

[0547] "Audio information" refers to data about the sounds and tone of voice emitted by the user, and is used to analyze the user's emotional state.

[0548] To implement this invention, the user needs to install a dedicated application on a mobile device such as a smartphone. When the user launches the application and engages in online shopping, the device uses its built-in camera and microphone to collect the user's facial expressions and voice information. This information is transmitted to a server in real time. Based on this information, the server performs emotion analysis and generates information about the user's emotional state.

[0549] The server utilizes emotional state information and shopping behavior information to determine acoustic characteristics. This can involve using emotion analysis APIs provided by cloud services such as Google Cloud. Based on the determined acoustic characteristics, acoustic data is generated using a music generation library with Python. This generated acoustic data is then adjusted to match the user's emotions and delivered to the device.

[0550] For example, if the server detects that a user is starting to feel tired after shopping for an extended period, it can play relaxing music to sustain the user's desire to purchase. In this way, it is possible to provide an optimized audio experience for the user.

[0551] An example of an input prompt for a generative AI model might be: "If the user is currently experiencing stress, please suggest what musical characteristics would be most relaxing." Using this prompt, the generative AI can generate suggestions for optimizing acoustic characteristics.

[0552] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0553] Step 1:

[0554] The user launches a smartphone application. The device collects the user's facial expressions and voice information in real time through its built-in camera and microphone, and sends this as input data to the server. This input is basic data used to analyze the user's emotional state.

[0555] Step 2:

[0556] The server performs emotion analysis using the received facial expression and voice information. This utilizes the Google Cloud Emotion Analysis API. Based on the emotion data as input, a data analysis algorithm is used to output information about the user's emotional state. This analysis identifies the user's current emotion.

[0557] Step 3:

[0558] The server receives user emotional state and shopping behavior information as input to determine acoustic characteristics. It then inputs a prompt to the generative AI model, such as, "If the user is currently experiencing stress, what musical characteristics would provide the most relaxation?" The server evaluates this prompt and outputs appropriate acoustic characteristics, including music genre, tempo, and volume.

[0559] Step 4:

[0560] The server generates acoustic data based on the determined acoustic characteristics. Using a music generation library with Python, it outputs sound data based on the input acoustic characteristics. The music generated here is designed to match the user's emotions.

[0561] Step 5:

[0562] The server adjusts the generated audio data in real time and delivers it to the user. During this process, it references input information, including the latest data on the user's shopping activities, to dynamically optimize the music's tempo and atmosphere. Finally, the adjusted audio data is sent to the user's device, allowing them to listen to it while shopping for a comfortable and stress-free shopping experience.

[0563] 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.

[0564] 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.

[0565] 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.

[0566] [Fourth Embodiment]

[0567] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0568] 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.

[0569] 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).

[0570] 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.

[0571] 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.

[0572] 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).

[0573] 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.

[0574] 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.

[0575] 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.

[0576] 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.

[0577] 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.

[0578] 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.

[0579] 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".

[0580] To implement this invention, a system is constructed in which a user, a terminal, and a server work together. The user inputs daily task information through an application on the terminal and provides it to the system. For example, the user can input tasks such as "create documents" or "training." The terminal receives this input information and sends it to the server.

[0581] Based on the received task information, the server analyzes the task content using natural language processing techniques. Here, the server determines the optimal musical characteristics for the task, such as tempo and pitch. The user's past selection and activity history are considered in determining these musical characteristics. The server then uses a generation algorithm to generate music data based on the determined musical characteristics. This generated music data is optimized for the user's activities and helps the user efficiently complete the task.

[0582] The terminal collects activity data from the user's smart device. This activity data includes heart rate, movement, and location information, and is provided to the server in real time. The server dynamically adjusts the generated music data according to the activity data. If the user wants to improve their concentration, the rhythm can be changed or the tempo adjusted to improve work efficiency. By dynamically changing the music to suit the user in this way, an optimal state can always be maintained.

[0583] As a concrete example, consider the case where a user performs a "jogging" task. When the user inputs "jogging" as the task, the server determines high-tempo, energetic musical characteristics and generates music. If the user's heart rate becomes too high while jogging, the server adjusts the music to a slower tempo to maintain a calm state.

[0584] This system allows users to have a musical experience that helps them efficiently perform their daily tasks, maximizing their task performance.

[0585] The following describes the processing flow.

[0586] Step 1:

[0587] The user launches an application on their device and enters information about their current task, such as "housework" or "data entry." The device then accurately receives this input information.

[0588] Step 2:

[0589] The terminal sends task information received from the user to the server. The server receives this information and prepares to analyze the task details.

[0590] Step 3:

[0591] The server uses natural language processing technology to analyze task information and identify relevant keywords and work environment. Based on this, it derives the optimal musical characteristics for the task, namely tempo and genre.

[0592] Step 4:

[0593] Based on the determined musical characteristics, the server generates new music data using a generation algorithm. This music data also takes into account the user's past selection history.

[0594] Step 5:

[0595] The device collects activity data in real time from the user's smart device. This activity data includes heart rate, exercise level, and posture information. The device sends this data to the server.

[0596] Step 6:

[0597] The server analyzes the collected activity data and dynamically adjusts the music data to suit the user's situation. For example, if the user is seeking relaxation, the music tempo will be slowed down.

[0598] Step 7:

[0599] The terminal receives music data that has been adjusted from the server and delivers it to the user's device. This ensures that the user can always listen to music that is appropriate for their task.

[0600] Step 8:

[0601] Users perform tasks while listening to the provided music. The music adapts to the user's activity rhythm, improving work efficiency.

[0602] (Example 1)

[0603] 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".

[0604] In modern society, many people use music to carry out their activities efficiently, but selecting music that is suitable for individual activities and physiological states is not easy. In particular, there is a need to optimize activity performance by dynamically changing the characteristics of music according to the activity. Conventional music selection methods have the challenge of not being able to provide music that is quickly and appropriately suited to this purpose.

[0605] 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.

[0606] In this invention, the server includes means for receiving activity information provided by a human-operated device, means for determining optimal acoustic characteristics using the activity information, and means for generating acoustic data based on the acoustic characteristics. This makes it possible to dynamically provide music that is suitable for the user's activity and physiological state.

[0607] "Activity information" refers to information provided by a person when they perform a specific activity, such as exercise or work activities.

[0608] "Acoustic properties" refer to the characteristics of a sound, including elements such as tempo, rhythm, and tone.

[0609] "Acoustic data" refers to sound information generated based on acoustic characteristics, representing music or sounds tailored to specific activities or states.

[0610] "Activity status data" refers to information that records a person's physical or behavioral state in real time, including heart rate and movement information.

[0611] "Means of adjustment" refers to methods of processing acoustic data to modify or optimize it according to activity status data.

[0612] "Means of provision" refers to methods of transmitting and making available the generated audio data to people.

[0613] To implement this invention, a system needs to be built in cooperation with a user, a terminal, and a server. The user inputs activity information through a dedicated application on a digital device. The information that the user can input could include activity details such as exercise or work. For example, activity information such as "jogging" or "document creation" can be entered.

[0614] The terminal is responsible for receiving the entered activity information and transmitting it to the server via the network. This transmission process uses data communication based on the HTTP protocol.

[0615] The server uses natural language processing techniques to analyze the received activity information. This process uses Python's natural language processing libraries, NLTK and spaCy, to analyze text data and determine the acoustic characteristics required for the activity. In determining the acoustic characteristics, the server retrieves the user's past preference history from a database (e.g., MySQL) and takes it into consideration.

[0616] After the acoustic characteristics are determined, the server generates acoustic data using a generative AI model. The generation of acoustic data is performed using a machine learning model specifically designed for music generation. Specifically, advanced music generation models such as OpenAI's MuseNet are used. An example of a prompt to the generative AI model would be, "Generate fast-paced, energetic music."

[0617] The device uses sensors from the smart device to collect user activity data. This activity data includes heart rate, movement, and location information, which is transmitted to the server in real time.

[0618] The server dynamically adjusts the generated audio data based on the received activity status data. When the user needs to concentrate, the rhythm and tempo of the music are changed accordingly to provide an audio experience tailored to the purpose. In this way, the system aims to improve the quality of activity by providing a comfortable audio environment that is in line with the user's situation and needs.

[0619] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0620] Step 1:

[0621] The user enters activity information using a terminal application. This information includes specific activities such as "jogging" or "studying." This information becomes the input data sent to the next processing step.

[0622] Step 2:

[0623] The terminal sends activity information received from the user to the server. The data is transferred to the server using the HTTP protocol, and the server receives it as input data for analysis. In this case, the data is generally sent in JSON format.

[0624] Step 3:

[0625] The server performs natural language processing based on the received activity information. Specifically, it analyzes the text data using Python's NLTK or spaCy to extract important keywords and context. Based on this processing, foundational data for determining acoustic characteristics is generated. This foundational data includes, for example, indicators related to appropriate tempo and rhythm.

[0626] Step 4:

[0627] The server retrieves and analyzes past user preference history from a database. This historical data is retrieved using SQL from relational databases such as MySQL, and current activity is compared with past patterns to further refine the acoustic characteristics. This process determines the detailed parameters necessary for generating the music.

[0628] Step 5:

[0629] The server generates acoustic data using a generative AI model based on acoustic characteristics. The AI ​​model used is specialized for music generation, and specific examples such as "Generate high-tempo, energetic music" are input to the model as prompts. Based on this, the model outputs music data. This data is sent to the terminal as a music file.

[0630] Step 6:

[0631] The device collects user activity data from the smart device's sensors. This data includes real-time heart rate, motion data, and location information, and is immediately transmitted to the server. The activity data provides the input data necessary for real-time music adjustments.

[0632] Step 7:

[0633] The server dynamically adjusts the generated audio data based on activity data. For example, if the user's heart rate increases, the tempo of the music is adjusted. The purpose of this process is to always maintain optimal user performance. The adjusted music data is then delivered to the user through the terminal, creating a feedback loop.

[0634] (Application Example 1)

[0635] 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".

[0636] There is a lack of means to provide music in real time that is optimal for the daily activities and tasks of diverse users, thereby promoting effects such as improved concentration and relaxation. Furthermore, the technology to achieve this using smart audio devices is not yet sufficiently developed, which is a challenge.

[0637] 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.

[0638] In this invention, the server includes means for receiving work information from the user, means for determining appropriate musical characteristics based on the work information, and means for generating acoustic data based on the musical characteristics. This makes it possible to provide optimal acoustic data in real time for a variety of user activities.

[0639] A "user" refers to an individual who interacts with this system and provides work information and activity data.

[0640] "Work information" refers to information about the tasks and activities that the user is trying to perform, and it is the data that forms the basis for determining the musical characteristics.

[0641] "Musical characteristics" refer to elements that describe the sonic features optimized for the user's work or activity, such as the rhythm, tempo, and atmosphere of the music.

[0642] "Audio data" refers to digital data that represents sound information generated based on musical characteristics and is provided to the user as music.

[0643] "Activity data" refers to data acquired in real time, such as the user's physiological information and movement information, and is used to adjust the acoustic data.

[0644] A "smart audio device" refers to a device that plays audio data via an internet connection and provides users with a music experience.

[0645] To implement this invention, a system is constructed in which a server, a terminal, and a smart audio device work together. The user inputs work information related to their daily activities through the smart audio device. The terminal then receives this input information and sends it to the server. The server uses natural language processing technology to analyze the received work information. Specifically, it utilizes Python and natural language processing libraries (e.g., NLTK and spaCy) to determine the optimal musical characteristics for the user's task.

[0646] Once the musical characteristics are determined, the server generates audio data. This generation uses a music generation library (e.g., Magenta) to create digital music data tailored to the musical characteristics. The generated audio data is stored in the cloud and further refined based on user activity data.

[0647] The user's device collects activity data in real time from smart audio equipment and provides it to the server. This activity data includes heart rate, movement, and location information. The server uses this data to dynamically adjust the audio data and provide a music experience that is optimal for the user's current situation. For example, if the user is doing fitness activities, high-tempo music is provided, but if the heart rate increases, the tempo is slowed down.

[0648] This system can improve performance and concentration by providing music optimized for various user activities using a generative AI model. It can enhance the user experience by using prompts such as, "What kind of music is best suited for a user who is 'concentrating on work'?"

[0649] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0650] Step 1:

[0651] The user inputs work information by voice through a smart audio device, and the device receives that information. The input voice data is converted into text data using speech recognition software (e.g., Google Speech-to-Text API). The converted text data is sent to the server.

[0652] Step 2:

[0653] The server analyzes the received work information text. Using natural language processing techniques, it analyzes the work information using Python's NLTK and spaCy libraries to determine the optimal musical characteristics for the user's task. The input is text data of the work information, and the output is data of musical characteristics (e.g., tempo, rhythm).

[0654] Step 3:

[0655] The server generates audio data based on musical characteristics. It uses a music generation library (e.g., Magenta) to generate audio data with specific musical characteristics. A generation AI model is utilized in this process. The input is data on musical characteristics, and the output is the generated audio data (digital music file).

[0656] Step 4:

[0657] The device collects activity data in real time from smart audio devices. This includes heart rate, motion information, and location information. The collected data is acquired using sensor technology and provided to the server. The input is the user's physiological data, and the output is activity data that reflects this.

[0658] Step 5:

[0659] The server analyzes the received activity data and adjusts the acoustic data in real time. As a preprocessing step, it cleanses the data using a data analysis tool (e.g., Pandas) and dynamically adjusts the tempo and rhythm of the music according to the type of activity. The input is activity data, and the output is the adjusted acoustic data.

[0660] Step 6:

[0661] The adjusted audio data is streamed to the user via the internet through a terminal using a smart audio device. This allows the user to receive an optimal music experience in real time. The input is the adjusted audio data, and the output is the music stream to the user.

[0662] 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.

[0663] In implementing the present invention, a system is configured that combines a user, a terminal, a server, and an emotion engine. The user can use a terminal with a dedicated application installed to input their daily tasks and current emotions. The terminal receives the information input by the user and transmits it to the server.

[0664] The server receives this information and determines the optimal musical characteristics related to the task, such as tempo and style. The emotion engine within the server recognizes the user's emotions in real time by analyzing the user's facial expressions and voice tone. This allows the server to evaluate how the user's emotional state affects the musical characteristics and adjust the music accordingly.

[0665] The server uses a generation algorithm to generate music data based on musical characteristics. This generated music data is dynamically adjusted to match the user's activity and emotional data. By adjusting the music genre and tempo, users can listen to music that suits their emotions and activity level, thereby facilitating task completion.

[0666] For example, if a user wants to "concentrate," the server generates and delivers fast-paced, energetic yet emotionally calming music. If the emotion engine detects stress from the user's face or voice, it instructs the system to switch to a calmer melody. This provides the user with an adaptive musical experience, making it easier to concentrate on tasks while reducing stress.

[0667] This system allows users to efficiently perform tasks while listening to music optimized for their emotions and activities, thereby maximizing their performance.

[0668] The following describes the processing flow.

[0669] Step 1:

[0670] The user accesses the application on their device and enters details of their current task and their feelings. The device receives this information and prepares to begin processing.

[0671] Step 2:

[0672] The terminal sends task and emotional information obtained from the user to the server. The server then begins analysis based on this information.

[0673] Step 3:

[0674] The server analyzes the received task information using natural language processing techniques to identify the characteristics of the task. This allows it to establish general standards for musical characteristics.

[0675] Step 4:

[0676] An emotion engine built into the server analyzes real-time facial expression data and voice tone sent from the user's device to recognize the user's emotional state.

[0677] Step 5:

[0678] The server generates optimal music data, taking into account the musical characteristics suitable for the task and the user's emotional state. It executes a generation algorithm to create personalized music.

[0679] Step 6:

[0680] The device uses a smart device to collect user activity data. This activity data includes the user's heart rate and step count. The collected data is sent to a server.

[0681] Step 7:

[0682] The server dynamically adjusts the tempo and style of the music data based on activity data and recognized emotion data. This ensures that the music is tailored to the user's current state.

[0683] Step 8:

[0684] The device receives the optimized music data and plays it on the user's device. In this way, the user can concentrate on their tasks while listening to optimized music.

[0685] Step 9:

[0686] Through an adaptive music experience, users can lower their stress levels and increase their concentration. In this state, they can continue tasks and improve their efficiency.

[0687] (Example 2)

[0688] 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".

[0689] In modern society, many people face various stresses and pressures, which often lead to decreased work efficiency. Furthermore, conventional music distribution systems struggle to provide music that is tailored to the emotional state of individual users, resulting in a challenge in providing a music experience that matches the user's situation and emotions.

[0690] 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.

[0691] In this invention, the server includes means for receiving task data and emotion data from the user, means for analyzing the user's facial expression data and voice data to recognize the emotional state in real time, and means for generating music data based on the emotional state and music attributes. This makes it possible to provide optimal music according to the user's situation, thereby improving work efficiency and stabilizing emotions.

[0692] "Task data" refers to information about the tasks and goals that a user intends to accomplish. This data includes details about the user's action plan and activities.

[0693] "Emotional data" refers to information that indicates a user's emotional state. This data is obtained from facial expressions, tone of voice, and other sources, and is used to analyze the user's mental state.

[0694] "Musical attributes" are parameters that indicate the characteristics of music, including tempo, melody, and harmony. They are elements that create an appropriate musical experience according to the user's situation.

[0695] "Facial expression data" is data obtained by analyzing a user's facial expressions. This allows us to infer the user's emotional state.

[0696] "Voice data" refers to data collected from the user's voice, and is used to evaluate emotional state by analyzing factors such as voice tone and pitch.

[0697] "Emotional state" refers to the state that indicates the user's current mental or emotional condition. This state is grasped in real time through data analysis.

[0698] "Music data" refers to data related to generated music, including information necessary for music playback. It is generated and adjusted according to the target user.

[0699] "Dynamic adjustment" refers to the process of modifying the attributes of music data in real time according to the user's changing circumstances and emotional state.

[0700] This invention provides a system that combines a user, a terminal, a server, and an emotion engine to improve a specific user experience. The user uses a terminal with a dedicated application installed. The application can be installed on a smartphone or tablet, and task data and emotion data can be input through that application.

[0701] The terminal quickly and securely transmits data entered by the user to the server. The data is transferred over the network and processed on the server. On the server, a computer system equipped with a high-performance processor runs, performing data analysis using a generative AI model. The server executes a process to determine music attributes based on the user's task data, using a proprietary algorithm.

[0702] Furthermore, the server is equipped with an emotion engine that analyzes the user's facial expression data and voice data to recognize the user's emotional state in real time. This analysis uses machine learning and voice analysis technologies. For example, if a user inputs task data such as "I want to improve my concentration," and the emotion engine recognizes that the user's facial expression indicates a state of concentration, fast-paced music will be generated.

[0703] The generated music data is dynamically adjusted on the server according to the user's changing activity and emotional state. This system allows for switching to calmer, slower-tempo music if stress is detected. The generating AI model is constantly learning from new data and used to continuously optimize the music.

[0704] A concrete example of a prompt might be a question like, "Which music attributes should be adjusted to suggest appropriate music when the user is in a relaxed state?" In this way, users can enjoy a music experience tailored to their situation and improve the efficiency of their tasks.

[0705] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0706] Step 1:

[0707] Users input task data and emotional data through their device. This data includes information such as "What tasks do I want to focus on today?" and "What is my current emotional state?". The device then identifies the data obtained from the user and organizes it into a dataset to prepare for the next step.

[0708] Step 2:

[0709] The device sends task data and sentiment data received from the user to the server. The transmitted data travels over the network and its format is adjusted for processing on the server. Specifically, the data is sent via HTTP requests and received as input by the server.

[0710] Step 3:

[0711] The server determines music attributes based on the received task data. Based on the input task data, logic is executed to select the music tempo and genre. For example, if the task is to improve concentration, the server will select and output music with energetic and fast tempo attributes.

[0712] Step 4:

[0713] The server uses an emotion engine to analyze the user's emotional state. Inputs include the user's facial expression data and voice data, which are analyzed to recognize the user's emotions in real time. Emotion analysis is performed using machine learning algorithms, generating outputs that quantify the user's stress level and concentration level.

[0714] Step 5:

[0715] The server integrates data on musical attributes and emotional states to generate musical data. The music generation algorithm takes this data as input and outputs appropriate musical data. For example, it might generate fast-paced music suitable for a state of concentration.

[0716] Step 6:

[0717] The server dynamically adjusts the generated music data according to the user's activity level. The input activity data is analyzed in real time as feedback, and the music tempo and volume are adjusted accordingly. The output is music data tailored to the user's state.

[0718] Step 7:

[0719] The device plays the adjusted music data received from the server to the user. By listening to this adjusted music, the user can enjoy a musical experience adapted to their emotions and tasks at that moment. In this way, the device provides real-time music delivery to the user.

[0720] (Application Example 2)

[0721] 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".

[0722] The challenge in online shopping is to improve the purchasing experience by providing an appropriate audio experience that responds to the user's emotional state. In particular, it is necessary to alleviate user stress, maintain their desire to purchase, and make the shopping process more comfortable.

[0723] 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.

[0724] In this invention, the server includes means for receiving emotional state information and shopping behavior information from the user, means for determining appropriate acoustic characteristics based on the emotional state information, and means for generating acoustic data based on the acoustic characteristics. This enables a comfortable and stress-free shopping experience by dynamically adjusting the acoustic experience according to the user's emotional state.

[0725] "User emotional state information" refers to data that indicates a user's emotions, obtained based on their facial expressions and voice.

[0726] "Shopping behavior information" refers to data related to a user's behavior patterns and purchase history when shopping online.

[0727] "Acoustic characteristics" refer to the fundamental elements that make up music, such as genre, tempo, and volume.

[0728] "Audio data" refers to digital information that is played back as music or sound.

[0729] "Facial expression information" refers to data obtained from the user's facial movements and expressions, which makes it possible to infer their emotions.

[0730] "Audio information" refers to data about the sounds and tone of voice emitted by the user, and is used to analyze the user's emotional state.

[0731] To implement this invention, the user needs to install a dedicated application on a mobile device such as a smartphone. When the user launches the application and engages in online shopping, the device uses its built-in camera and microphone to collect the user's facial expressions and voice information. This information is transmitted to a server in real time. Based on this information, the server performs emotion analysis and generates information about the user's emotional state.

[0732] The server utilizes emotional state information and shopping behavior information to determine acoustic characteristics. This can involve using emotion analysis APIs provided by cloud services such as Google Cloud. Based on the determined acoustic characteristics, acoustic data is generated using a music generation library with Python. This generated acoustic data is then adjusted to match the user's emotions and delivered to the device.

[0733] For example, if the server detects that a user is starting to feel tired after shopping for an extended period, it can play relaxing music to sustain the user's desire to purchase. In this way, it is possible to provide an optimized audio experience for the user.

[0734] An example of an input prompt for a generative AI model might be: "If the user is currently experiencing stress, please suggest what musical characteristics would be most relaxing." Using this prompt, the generative AI can generate suggestions for optimizing acoustic characteristics.

[0735] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0736] Step 1:

[0737] The user launches a smartphone application. The device collects the user's facial expressions and voice information in real time through its built-in camera and microphone, and sends this as input data to the server. This input is basic data used to analyze the user's emotional state.

[0738] Step 2:

[0739] The server performs emotion analysis using the received facial expression and voice information. This utilizes the Google Cloud Emotion Analysis API. Based on the emotion data as input, a data analysis algorithm is used to output information about the user's emotional state. This analysis identifies the user's current emotion.

[0740] Step 3:

[0741] The server receives user emotional state and shopping behavior information as input to determine acoustic characteristics. It then inputs a prompt to the generative AI model, such as, "If the user is currently experiencing stress, what musical characteristics would provide the most relaxation?" The server evaluates this prompt and outputs appropriate acoustic characteristics, including music genre, tempo, and volume.

[0742] Step 4:

[0743] The server generates acoustic data based on the determined acoustic characteristics. Using a music generation library with Python, it outputs sound data based on the input acoustic characteristics. The music generated here is designed to match the user's emotions.

[0744] Step 5:

[0745] The server adjusts the generated audio data in real time and delivers it to the user. During this process, it references input information, including the latest data on the user's shopping activities, to dynamically optimize the music's tempo and atmosphere. Finally, the adjusted audio data is sent to the user's device, allowing them to listen to it while shopping for a comfortable and stress-free shopping experience.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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."

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0767] The following is further disclosed regarding the embodiments described above.

[0768] (Claim 1)

[0769] A means of receiving work information from users,

[0770] A means for determining appropriate musical characteristics based on the aforementioned work information,

[0771] means for generating music data based on the aforementioned musical characteristics,

[0772] A means of monitoring user activity data in real time,

[0773] means for adjusting the generated music data according to the activity data,

[0774] A means for distributing the adjusted music data to the user,

[0775] A system that includes this.

[0776] (Claim 2)

[0777] The system according to claim 1, which refers to the past user selection history in determining the aforementioned musical characteristics.

[0778] (Claim 3)

[0779] The system according to claim 1, wherein the monitoring of the activity data uses the user's heart rate information.

[0780] "Example 1"

[0781] (Claim 1)

[0782] A means of receiving activity information provided by a human-operated device,

[0783] A means for determining the optimal acoustic characteristics using the aforementioned activity information,

[0784] means for generating acoustic data based on the aforementioned acoustic characteristics,

[0785] A means of instantly recording human activity data,

[0786] Means for adjusting the generated acoustic data according to the activity status data,

[0787] Means for providing the adjusted acoustic data to a person,

[0788] A system that includes this.

[0789] (Claim 2)

[0790] The system according to claim 1, which utilizes past human preference history in determining the aforementioned acoustic characteristics.

[0791] (Claim 3)

[0792] The system according to claim 1, wherein the recording of the activity status data uses human heart rate information.

[0793] "Application Example 1"

[0794] (Claim 1)

[0795] A means of receiving work information from users,

[0796] A means for determining appropriate musical characteristics based on the aforementioned work information,

[0797] means for generating acoustic data based on the aforementioned musical characteristics,

[0798] A means of monitoring user activity data in real time,

[0799] means for adjusting the generated acoustic data according to the activity data,

[0800] A communication means for providing the adjusted acoustic data to the user,

[0801] A means for distributing the aforementioned audio data via a smart audio device,

[0802] A system that includes this.

[0803] (Claim 2)

[0804] The system according to claim 1, wherein in determining the aforementioned musical characteristics, the musical characteristics are improved based on the past selection history of the user.

[0805] (Claim 3)

[0806] The system according to claim 1, wherein the monitoring of the activity data involves using the user's physiological information.

[0807] "Example 2 of combining an emotion engine"

[0808] (Claim 1)

[0809] A means of receiving task data and sentiment data from users,

[0810] A means for determining appropriate musical attributes based on the aforementioned task data,

[0811] A means of analyzing the user's facial expression data and voice data to recognize their emotional state in real time,

[0812] means for generating music data based on the aforementioned emotional state and musical attributes,

[0813] The generated music data is dynamically adjusted according to the user's activity status,

[0814] A means of distributing the adjusted music data to the user and continuing to make adjustments based on feedback,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, which, in determining the aforementioned music attributes, refers to the past user selection history and emotional history.

[0818] (Claim 3)

[0819] The system according to claim 1, wherein the recognition of the emotional state involves using the user's voice tone and facial expression data.

[0820] "Application example 2 when combining with an emotional engine"

[0821] (Claim 1)

[0822] A means of receiving emotional state information and shopping behavior information from users,

[0823] means for determining appropriate acoustic characteristics based on the aforementioned emotional state information,

[0824] means for generating acoustic data based on the aforementioned acoustic characteristics,

[0825] A means of monitoring users' shopping activity data in real time,

[0826] means for adjusting the generated sound data according to the shopping activity data,

[0827] A means for distributing the adjusted audio data to the user,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, which, in determining the aforementioned acoustic characteristics, refers to the past selection history and purchase history of users.

[0831] (Claim 3)

[0832] The system according to claim 1, wherein the monitoring of the shopping activity data includes the use of the user's facial expression information and voice information. [Explanation of symbols]

[0833] 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. A means of receiving work information from users, A means for determining appropriate musical characteristics based on the aforementioned work information, means for generating music data based on the aforementioned musical characteristics, A means of monitoring user activity data in real time, means for adjusting the generated music data according to the activity data, A means for distributing the adjusted music data to the user, A system that includes this.

2. The system according to claim 1, which refers to the past user selection history in determining the aforementioned musical characteristics.

3. The system according to claim 1, wherein the monitoring of the activity data uses the user's heart rate information.

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