system

Abstract

Provide a system. The device includes a means for detecting food type, eating speed, and sound using sensors; a means for analyzing the detected data and determining a music genre according to the type of food and a tempo according to the eating speed; means for generating the determined music data and playing it back in real time; A system including:

Description

[Technical Field]

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

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

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

[0004] In modern society, eating quickly or feeling stressed during meals can lead to indigestion and poor nutrient absorption. Furthermore, a monotonous dining environment can reduce the enjoyment of meals and the richness of food culture. To solve these problems, a system is needed that provides appropriate music during meals to adjust the pace of eating and create a relaxing atmosphere. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for detecting food type, eating speed, and sound using sensors, a means for analyzing the detected data, determining a music genre according to the food type and a tempo according to the eating speed, and a means for generating and playing the determined music data in real time. This allows for natural adjustment of meal pace, improving meal quality, and promoting healthy eating habits. Furthermore, by identifying food type through image recognition and detecting eating speed using an acceleration sensor, more accurate and effective music provision is achieved.

[0006] "Sensors" are devices for detecting food type, eating speed, and sound.

[0007] "Food type" refers to the specific type of food a user consumes during a meal, such as pasta or sushi.

[0008] "Eating rate" refers to the speed or pace at which a user consumes food, measured by the amount or number of times food is consumed per time.

[0009] "Sound" refers to a physical acoustic phenomenon that occurs when handling tableware or food, such as the sound of a fork or chopsticks.

[0010] "Analysis" is the process of analyzing the detected data to understand the type of food, the eating rate, and the sound.

[0011] "Music genre" refers to a style or category of music determined by the type of food, such as Italian melodies or Japanese rhythms.

[0012] "Tempo" refers to the speed and rhythm of the music, which is adjusted according to the user's eating speed.

[0013] "Music data" refers to music information created in a playable format.

[0014] "Real-time" refers to processing that occurs immediately without delay, meaning music can be played instantly while the user is eating.

[0015] "Image recognition" is a technology that analyzes images taken with a camera and identifies objects.

[0016] An "accelerometer" is a sensor that measures the acceleration of an object and is used to detect the user's eating movements. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0025] [First embodiment]

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

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

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0038] The present invention aims to provide appropriate music during meals, thereby adjusting the pace of the meal and creating a relaxed atmosphere. The specific configuration and operation of the system will be described below.

[0039] System configuration

[0040] The system consists of three main parts:

[0041] 1. Terminal: A device containing sensors (camera, microphone, accelerometer) that is installed on the dining table.

[0042] 2. Server: A central processing unit that analyzes data from the terminals and generates appropriate music data.

[0043] 3. User: The person who eats the food.

[0044] Operational Overview

[0045] The system uses devices to collect environmental data during meals, analyzes that data on a server, and generates and plays appropriate music. By repeating this cycle, the pace of meals is naturally adjusted, enriching food culture.

[0046] How it works

[0047] 1. Sensor data collection by the device

[0048] Camera: The device's built-in camera takes pictures of food on the table while you eat, and uses image recognition algorithms to identify the type of food.

[0049] Microphone: The device's microphone collects sounds made while eating, specifically the sounds of chopsticks and forks touching plates and the sounds of food being brought to the mouth.

[0050] Acceleration sensor: The device's built-in acceleration sensor detects the movement of the user's hands and tableware, and measures the speed at which the user eats.

[0051] 2. Data submission and analysis

[0052] Device: The device sends the sensor data (image data, sound data, acceleration data) collected to the server.

[0053] Server: The server analyzes the data it receives. For example, it identifies the type of food from image data, the type of tableware and the sound of the food being eaten from sound data, and the speed at which the food is eaten from acceleration data.

[0054] 3. Generating music data

[0055] Server: Based on the data analyzed by the server, it determines the appropriate music genre (for example, Italian for pasta) and tempo (according to the eating speed). It then generates music data in real time based on the determined music genre and tempo.

[0056] 4. Playing Music

[0057] Device: The device receives music data from the server, stores it in a buffer, and plays the music in real time. Users can listen to appropriate music while eating, and naturally adjust the pace of their meal.

[0058] Specific examples

[0059] Example 1: When eating pasta

[0060] The user begins to eat pasta. The device's camera detects the pasta and sends the image data to the server. The device's microphone collects the sound of the fork and plate touching each other and sends the sound data to the server. The device's accelerometer also measures the user's hand movements and sends the data to the server.

[0061] The server analyzes the data it receives and determines that the food is pasta and that the person is eating it quickly. The server then generates an Italian melody at a slightly slower tempo and sends the music data to the device. The device then plays the music in real time, allowing the user to naturally adjust the pace of their meal and enjoy a relaxed meal.

[0062] Example 2: Eating sushi

[0063] The user begins to eat sushi. The device's camera detects the sushi and sends the image data to the server. The device's microphone collects the sound of the chopsticks hitting the plate and sends the sound data to the server. The device's accelerometer also measures the user's hand movements and sends the data to the server.

[0064] The server analyzes the received data and determines that the food is sushi and that the user is eating at an appropriate speed. The server then generates a Japanese-style rhythm at the appropriate tempo and sends the music data to the device. The device plays the music in real time, allowing the user to continue eating at an appropriate pace while enjoying the Japanese cuisine.

[0065] This invention allows users to naturally adjust the pace of their meals, enriching food culture, and is expected to promote healthy eating habits and increase the enjoyment of meals.

[0066] The processing flow will be explained below.

[0067] Step 1:

[0068] The device activates the camera, microphone, and accelerometer installed on the table.

[0069] Step 2:

[0070] The device uses a camera to take pictures of food on the table and collect image data in real time.

[0071] Step 3:

[0072] The device uses image recognition algorithms to identify the type of food from the collected image data.

[0073] Step 4:

[0074] The device uses a microphone to collect sounds such as dishes clashing and other sounds made while eating.

[0075] Step 5:

[0076] The device uses an accelerometer to detect the movement of the user's hands and tableware and measure the speed at which they eat.

[0077] Step 6:

[0078] The image data, sound data, and acceleration data collected by the terminal are combined into a single data packet and transmitted to the server.

[0079] Step 7:

[0080] The server receives the data packets and analyzes the image data to determine the type of food.

[0081] Step 8:

[0082] The server analyzes the sound data and identifies the type of tableware and the sound of its operation.

[0083] Step 9:

[0084] The server analyzes the acceleration data and calculates the user's eating speed.

[0085] Step 10:

[0086] Based on the analysis results, the server determines the music genre according to the type of food and the tempo according to the eating speed.

[0087] Step 11:

[0088] The server uses a music generation algorithm to generate music data in real time according to the determined genre and tempo.

[0089] Step 12:

[0090] The server transmits the generated music data to the terminal.

[0091] Step 13:

[0092] The terminal stores the music data received from the server in a buffer and plays it in real time.

[0093] Step 14:

[0094] The user continues eating while listening to the played music and paces the meal.

[0095] Step 15:

[0096] The device continuously collects sensor data and periodically transmits the data to the server.

[0097] Step 16:

[0098] The server analyzes the new data it receives and readjusts the tempo and genre of the music as needed.

[0099] Through this series of steps, the system can provide appropriate music while the user is eating, naturally pacing the meal and creating a relaxed atmosphere.

[0100] Example 1

[0101] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0102] In traditional dining environments, there is a lack of ways to adjust the pace and atmosphere of the meal, making it difficult to promote healthy eating habits and improve the enjoyment of meals. While there are systems that provide music, there are no systems that dynamically generate music in real time according to the food and pace of the meal. This makes it difficult to maximize the enjoyment and relaxation of the meal.

[0103] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0104] In this invention, the server includes means for detecting the type of food, eating speed, and sound using sensors, means for transmitting the detected data to a cloud computing device, means for analyzing the detected data in the cloud computing device, means for determining a music genre according to the type of food and a tempo according to the eating speed based on the analysis results, means for generating the determined music data in real time using a generative AI model, means for transmitting the generated music data to a terminal, and means for playing the generated music data in real time. This makes it possible to dynamically adjust the environment during the meal and optimize the pace and atmosphere of the user's meal.

[0105] "Sensors" are devices for detecting food type, eating speed, and sound.

[0106] A "cloud computing device" is an external computer system provided over the Internet for performing data analysis and computational processing.

[0107] A "terminal" is a user-side device that contains sensors, collects and transmits data, and plays music.

[0108] "Image recognition" is a technology that analyzes image data captured by a camera to identify specific objects or types of food.

[0109] An "accelerometer" is a device that detects the user's movements and the movement of tableware, and is used to measure eating speed.

[0110] A "generative AI model" is an algorithm or program that uses artificial intelligence to generate music data in real time.

[0111] A "prompt sentence" is text data input into a generative AI model, and is a sentence that specifies the processing content and conditions to be performed by the generative AI model.

[0112] "Real-time" means that the processing or reaction occurs almost instantaneously or in a very short time.

[0113] MODE FOR CARRYING OUT THE INVENTION

[0114] This system aims to adjust the pace of a meal and create a relaxing atmosphere by providing appropriate music during the meal. This system is mainly composed of three elements: a terminal, a server, and a user.

[0115] Hardware and software used

[0116] Hardware:

[0117] Terminal: A device that includes sensors and is installed on the dining table. For example, a Raspberry Pi with its camera module, microphone module, and accelerometer module can be used.

[0118] Server: A high-performance central processing unit. Uses cloud services (e.g., AWS (registered trademark) or Google (registered trademark) Cloud).

[0119] software:

[0120] Terminal software: A custom Python script running on the Raspberry Pi, used to collect data from the sensors and send it to the server.

[0121] Server-side software: Runs on the cloud and performs data analysis and music generation, such as data analysis algorithms written in Python and generative AI models for music generation (e.g., OpenAI® GPT-3® and Jukedeck).

[0122] System Operation

[0123] This system uses devices to collect environmental data during meals, analyzes that data on a server, and generates and plays appropriate music. By repeating this cycle, the pace of meals is naturally adjusted, enriching food culture.

[0124] Specific operation example

[0125] Example 1: When eating pasta

[0126] 1. The device detects pasta on the table using a camera, analyzes the image data using OpenCV, and recognizes that it is pasta.

[0127] 2. The device's microphone collects the sound of the fork and plate touching each other, and Librosa is used to analyze the sound data.

[0128] 3. The device's accelerometer measures the user's hand movements, and a Python script analyzes the eating speed.

[0129] 4. The device sends this data to the server via an HTTP request.

[0130] 5. The server recognizes the pasta from the image data and confirms from the sound and acceleration data that the user is eating quickly.

[0131] 6. The server generates music with a slightly slower tempo and Italian melody using a generative AI model.

[0132] Example prompt:

[0133] "Recognize pasta from image data, and if the user is eating quickly from sound and acceleration data, generate music with a slightly slower tempo using an Italian melody."

[0134] 7. The server sends the generated music data to the device.

[0135] 8. The device plays music data in real time, allowing the user to relax and enjoy their meal.

[0136] Example 2: Eating sushi

[0137] 1. The device detects sushi on the table using a camera, analyzes the image data using OpenCV, and recognizes that it is sushi.

[0138] 2. The device's microphone collects the sound of the chopsticks and plate touching each other, and the sound data is analyzed using Librosa.

[0139] 3. The device's accelerometer measures the user's hand movements, and a Python script analyzes the eating speed.

[0140] 4. The device sends this data to the server via an HTTP request.

[0141] 5. The server recognizes the sushi from the image data and confirms that it is being eaten at the appropriate speed from the sound and acceleration data.

[0142] 6. The server generates music with an appropriate tempo and Japanese rhythm using an AI model.

[0143] Example prompt:

[0144] "Recognize sushi from image data, and generate Japanese-style rhythmic music if the user is eating at the appropriate speed based on sound and acceleration data."

[0145] 7. The server sends the generated music data to the device.

[0146] 8. The device plays music data in real time, allowing the user to enjoy their meal at a comfortable pace.

[0147] conclusion

[0148] This invention allows users to naturally adjust the pace of their meals, increasing the enjoyment of meals and is expected to promote healthy eating habits.

[0149] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0150] Step 1:

[0151] Sensor data is acquired by the device.

[0152] Specific behavior:

[0153] The camera takes a picture of the food on the table at a rate of one per second and collects image data.

[0154] A microphone continuously records the acoustic environment at the table, collecting the sounds of tableware and food as digital signals.

[0155] The acceleration sensor measures the movement of the user's hands and tableware in real time and collects acceleration data.

[0156] Input: Image data, sound data, and acceleration data from sensors installed on the dining table.

[0157] Output: Collected raw data (image data, sound data, acceleration data).

[0158] Step 2:

[0159] The device sends the collected sensor data to the server.

[0160] Specific behavior:

[0161] The device sends this data to the server as an HTTP request at regular intervals, for example, every 10 seconds.

[0162] Input: Raw data collected by the device (image data, sound data, acceleration data).

[0163] Output: The raw data sent to the server.

[0164] Step 3:

[0165] The server parses the data it receives.

[0166] Specific behavior:

[0167] The server analyzes the image data using a computer vision algorithm (e.g., OpenCV or TENSORFLOW®) to identify the type of food.

[0168] The server analyzes the sound data using an acoustic analysis library (e.g., Librosa) to identify the type of tableware and the sound of its operation.

[0169] The server analyzes the acceleration data and uses Python's NumPy library to measure the user's eating pace.

[0170] Input: Raw data (image data, sound data, acceleration data) sent to the server.

[0171] Output: Analyzed results (type of food, sound of dishes, eating pace).

[0172] Step 4:

[0173] The server generates music data based on the analysis results.

[0174] Specific behavior:

[0175] The server then uses the analysis results to determine the appropriate music genre and tempo. For example, if the food is pasta and the eating pace is fast, a slower Italian melody will be selected.

[0176] The server inputs prompt sentences into a generative AI model (e.g., OpenAI's GPT-3 or Jukedeck) and generates music data in real time.

[0177] Example prompt:

[0178] "Recognize pasta from image data, and if the user is eating quickly from sound and acceleration data, generate music with a slightly slower tempo using an Italian melody."

[0179] Input: Analyzed results (food type, dish sounds, eating pace).

[0180] Output: The generated music data.

[0181] Step 5:

[0182] The server transmits the generated music data to the terminal.

[0183] Specific behavior:

[0184] The music data generated by the server is sent to the terminal as an HTTP response.

[0185] Input: Generated music data.

[0186] Output: The music data sent to the device.

[0187] Step 6:

[0188] The device plays the music data in real time.

[0189] Specific behavior:

[0190] The music data received by the device is stored in a buffer and played in real time using Python's Pygame library, etc.

[0191] Users can relax and enjoy their meal while listening to music.

[0192] Input: Music data sent to the device.

[0193] Output: The played music.

[0194] (Application example 1)

[0195] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0196] In today's virtual stores and restaurants, unlike physical environments, users' behavior is left unmonitored while they eat or shop, making it difficult to adjust the pace of the meal or create a relaxing atmosphere. Furthermore, there is a lack of systems that can enhance the enjoyment of meals by providing appropriate music and promote healthy eating habits. Furthermore, the technology to dynamically adjust music for individual users is underdeveloped.

[0197] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0198] In this invention, the server includes means for detecting a user's behavior on the electronic device, the type of food, the eating speed, and sounds using sensors, means for analyzing the detected data and determining a music genre according to the type of food and a tempo according to the eating speed, means for generating the determined music data and playing it in real time, and means for dynamically changing the music data using a generative AI model to adjust the user's eating pace, thereby providing a relaxed atmosphere during the user's meal, allowing the user to naturally adjust the eating pace, and enriching the experience in the virtual store or virtual restaurant.

[0199] definition statement

[0200] "Sensor" is a general term for electronic devices that detect user behavior and environmental data in real time.

[0201] "Electronic device" refers to any electronic device that can connect to the internet and that a user uses while eating or shopping. Examples include smartphones, tablets, and personal computers.

[0202] "User behavior" refers to the actions and movements of users when eating or shopping, including hand movements, use of tableware, etc.

[0203] "Food type" refers to the specific type of food or dish a user is eating, such as pasta or sushi.

[0204] "Eating speed" refers to the speed at which a user brings food to their mouth when eating.

[0205] "Sound" refers to the sounds that occur while eating, including, for example, the sound of chewing food and the sound of dishes clashing.

[0206] "Means" refers to the methods or devices used to achieve a particular purpose.

[0207] "Analyzing data" refers to performing calculations and processing to extract information from collected data and find patterns and trends.

[0208] "Music genre" refers to a type or style of music, such as classical, jazz, or pop.

[0209] "Tempo" refers to the speed and rhythm of music or movement.

[0210] "Generating music data" refers to creating new music based on the analysis results.

[0211] "Playing in real time" means playing the data immediately after receiving it.

[0212] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to generate and analyze data.

[0213] "Dynamically changing music data" refers to changing the content of the music being played in real time based on new data or conditions along the way.

[0214] MODE FOR CARRYING OUT THE INVENTION

[0215] This invention is a system that monitors the user's behavior and eating pace in a virtual store or restaurant and provides appropriate music. This system uses sensors to collect user behavior data in real time and dynamically generates and changes music data based on the analysis results, enriching the user's dining experience.

[0216] System configuration and operation overview

[0217] The system has the following components:

[0218] 1. Device: An electronic device equipped with sensors such as a camera, microphone, and accelerometer. This device collects information on user behavior, eating speed, and environmental sounds in real time.

[0219] 2. Server: A central processing unit that analyzes data sent from the device and determines the music genre and tempo according to the type and speed of eating, and dynamically changes the music data using a generative AI model.

[0220] 3. User: A person who dine in a virtual environment. They listen to music from their device and enjoy a meal in a relaxed atmosphere.

[0221] How it works

[0222] Device data collection:

[0223] It uses a camera to capture footage of the user while they eat and analyzes the type of food they eat and the pace at which they eat.

[0224] It uses a microphone to collect sounds made by the user while eating, such as the clinking of dishes and chewing.

[0225] It uses an accelerometer to measure the movement of the user's hands and utensils to determine the speed at which they are eating.

[0226] Data analysis and music data generation:

[0227] The data collected by the device is sent to a server, which analyzes it and identifies the type of food and the speed at which it is eaten.

[0228] Based on the analyzed data, the server determines the appropriate music genre (for example, Italian music for Italian cuisine, Japanese music for Japanese cuisine) and tempo.

[0229] Furthermore, a generative AI model is used to dynamically generate and change music data based on the analysis results, providing a music environment that is optimal for the user's eating pace.

[0230] Real-time music playback:

[0231] The generated music data is sent to the device and played in real time, allowing users to naturally adjust the pace of their meal and enjoy it in a relaxed atmosphere.

[0232] Specific Examples

[0233] Example 1: Eating pasta at a virtual restaurant:

[0234] If a user is eating pasta in a virtual restaurant, the camera will recognize the pasta and find that they are eating quickly. The server will then analyze this data and generate Italian music at a slower tempo, encouraging the user to slow down and relax.

[0235] Example 2: Drinking coffee at a virtual cafe:

[0236] If a user is relaxing in a virtual cafe with a cup of coffee, the camera will recognize the coffee cup and detect a lack of movement. The server will then analyze this data and select acoustic music to create a more relaxing environment.

[0237] Prompt Sentence Examples

[0238] Below are some examples of specific prompts for implementing patents:

[0239] When a user eats pasta at a virtual restaurant, a camera and microphone capture their behavior and send the data to a server. The server analyzes the speed at which the user is eating and plays appropriate Italian music at a slightly slower tempo. The system uses Python's OpenCV, sounddevice, NumPy, and asyncio libraries.

[0240] This invention will improve the user experience in the virtual store, allowing them to enjoy their food and eat at a healthy pace.

[0241] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0242] Program processing steps

[0243] Step 1:

[0244] The device uses a camera, microphone, and accelerometer to collect real-time data on the user's behavior, ambient sounds, food type, and eating speed. During this data collection process, the camera captures images of the table and uses a recognition algorithm to identify the type of food (e.g., pasta, sushi, etc.). In addition, the microphone collects ambient sounds (e.g., the sound of dishes clattering, chewing, etc.), and the accelerometer measures the user's eating speed from the movement of their hands and dishes.

[0245] Input: Camera video data, audio data, acceleration data

[0246] Output: User behavior data (type of food, eating speed, environmental sounds)

[0247] Step 2:

[0248] The device sends the collected data to the server. In the data transmission process, the device uploads image data of the type of food recognized by the camera, audio data collected by the microphone, and eating speed data obtained by the accelerometer to the server.

[0249] Input: User behavior data (type of food, eating speed, environmental sounds)

[0250] Output: Raw data sent to the server

[0251] Step 3:

[0252] The server analyzes the received data to identify the type of food, eating speed, and environmental sounds. During the analysis process, an image recognition algorithm (using OpenCV) is used to identify the type of food, an audio analysis algorithm is used to classify the collected environmental sounds, and the speed of eating is calculated based on the acceleration data. This allows for specific analysis results to be obtained based on the characteristics of each data.

[0253] Input: Raw data sent to the server

[0254] Output: Analyzed data (food type, eating speed, environmental sound classification)

[0255] Step 4:

[0256] The server determines the appropriate music genre and tempo based on the analysis results. In the process of dynamically generating or changing music data using a generative AI model according to the analysis data, for example, if you eat quickly, it will select relaxing music with a slow tempo, and it will determine the appropriate music genre depending on the type of food (e.g., Italian music for pasta).

[0257] Input: Analyzed data (food type, eating speed, environmental sound classification)

[0258] Output: Determined music genre and tempo

[0259] Step 5:

[0260] The server sends the generated music data to the device, which then plays the music in real time. During the music playback process, the music data dynamically modified by the generative AI model is sent to the device, which stores the music in a buffer and then plays it through the speaker.

[0261] Input: Determined music genre and tempo

[0262] Output: Music data to be played

[0263] Step 6:

[0264] The user continues eating while listening to appropriate music. As a result, the user can naturally adjust the pace of their meal and enjoy it in a relaxed atmosphere. The device again monitors the user's reactions and behavior, and the entire system operates in a feedback loop.

[0265] Input: Music data to be played

[0266] Output: Relaxed eating environment, naturally adjusted eating pace

[0267] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0268] The present invention aims to provide appropriate music during meals to adjust the pace of the meal and create a relaxing atmosphere, and by combining it with an emotion engine that recognizes the user's emotions, it provides a more personalized music experience.

[0269] System configuration

[0270] The system consists of four main parts:

[0271] 1. Terminal: A device installed on the dining table that includes sensors (camera, microphone, accelerometer) and an emotion engine.

[0272] 2. Server: A central processing unit that analyzes data from the terminals and generates appropriate music data.

[0273] 3. Emotion engine: Recognizes the user's emotions and sends the data to the server.

[0274] 4. User: The person who eats the food.

[0275] Operational Overview

[0276] The system uses the device to collect environmental data during the meal, analyzes that data on the server, and takes into account data from the emotion engine to generate and play appropriate music. By repeating this cycle, the pace of the meal is naturally adjusted, enriching food culture.

[0277] How it works

[0278] 1. Sensor data collection by the device

[0279] Camera: The device's built-in camera takes pictures of the food on the table while the user is eating. Image recognition algorithms are used to identify the type of food. The device also analyzes the user's facial expressions and sends them to the emotion engine as emotional data.

[0280] Microphone: The device's microphone collects sounds made while eating, such as the sound of chopsticks and forks touching tableware and the tone of the user's voice, and sends these to the emotion engine.

[0281] Acceleration sensor: The device's built-in accelerometer detects the movement of the user's hands and tableware and measures the speed at which they eat.

[0282] 2. Data submission and analysis

[0283] Device: The device sends the image data, sound data, acceleration data collected, and emotion data analyzed by the emotion engine to the server.

[0284] Server: The server analyzes the data it receives. For example, it identifies the type of food from image data, the type of tableware and the sound of the movements from sound data, the speed of eating from acceleration data, and the user's current emotional state from emotion data.

[0285] 3. Generating music data

[0286] Server: Based on the data analyzed by the server, the music genre is determined according to the type of food and the tempo according to the eating speed, and the music is selected according to the user's emotional state. For example, if the user is feeling stressed, it will generate relaxing music, and if the user is in a happy mood, it will generate cheerful music.

[0287] Server: Generates music data in real time based on the determined music genre, tempo, and emotion.

[0288] 4. Playing Music

[0289] Device: The device stores the music data received from the server in a buffer and plays the music in real time. Users can listen to music that matches their emotions while eating, allowing them to naturally adjust the pace of their meal and enjoy a relaxed meal.

[0290] Specific examples

[0291] Example 1: When eating pasta

[0292] The user begins to eat pasta. The device's camera detects the pasta and sends the image data to the server. The device's microphone collects the sound of the fork and plate touching each other and sends the sound data to the server. The device's accelerometer measures the user's hand movements and sends the data to the server. The emotion engine then analyzes the user's facial expressions, determines that they are feeling stressed, and sends the emotion data to the server.

[0293] The server analyzes the data it receives, recognizes that the food is pasta and that the user is eating it quickly, and generates a relaxing Italian melody at a slow tempo because the user is feeling stressed. The music data is then sent to the device, which plays the music in real time, allowing the user to naturally adjust the pace of their meal and enjoy a relaxed meal.

[0294] Example 2: Eating sushi

[0295] The user begins to eat sushi. The device's camera detects the sushi and sends the image data to the server. The device's microphone collects the sound of the chopsticks hitting the plate and sends the sound data to the server. The device's accelerometer measures the user's hand movements and sends the data to the server. The emotion engine then analyzes the user's tone of voice, determines that they are enjoying the food, and sends the emotion data to the server.

[0296] The server analyzes the data it receives, recognizes that the food is sushi and that the user is eating at an appropriate pace, and because the user is enjoying themselves, generates a cheerful Japanese-style rhythm at the same tempo and sends the music data to the device. The device plays the music in real time, allowing the user to continue eating at an appropriate pace while enjoying the Japanese food atmosphere.

[0297] This invention allows users to naturally adjust the pace of their meals and enrich their food culture. It is also expected to promote healthy eating habits and increase the enjoyment of meals. Furthermore, by utilizing the emotion engine, it is possible to provide a personalized music experience that corresponds to the user's emotional state.

[0298] The processing flow will be explained below.

[0299] Step 1:

[0300] The device activates the camera, microphone, accelerometer, and emotion engine installed on the table.

[0301] Step 2:

[0302] The device uses a camera to capture images of food on the table and collects image data in real time, while the emotion engine simultaneously collects facial expression data from the user.

[0303] Step 3:

[0304] The device uses image recognition algorithms to identify the type of food from the collected image data, while an emotion engine analyzes facial expression data to identify the user's emotional state.

[0305] Step 4:

[0306] The device uses a microphone to collect sounds such as the clinking of dishes and other sounds made while eating, and the emotion engine also collects tone data from the user's voice.

[0307] Step 5:

[0308] The device uses an accelerometer to detect the movement of the user's hands and tableware and measure the speed at which they eat.

[0309] Step 6:

[0310] The image data, sound data, acceleration data collected by the terminal, and emotion data analyzed by the emotion engine are compiled into a single data packet and sent to the server.

[0311] Step 7:

[0312] The server receives the data packets and analyzes the image data to determine the type of food.

[0313] Step 8:

[0314] The server analyzes the sound data and identifies the type of tableware and the sound of its operation.

[0315] Step 9:

[0316] The server analyzes the acceleration data and calculates the user's eating speed.

[0317] Step 10:

[0318] The server analyzes the emotional data and recognizes the user's current emotional state.

[0319] Step 11:

[0320] Based on the analysis results, the server determines the music genre according to the type of food, the tempo according to the eating speed, and the characteristics of the music according to the emotional state.

[0321] Step 12:

[0322] The server uses a music generation algorithm to generate music data in real time according to the determined genre, tempo, and emotion.

[0323] Step 13:

[0324] The server transmits the generated music data to the terminal.

[0325] Step 14:

[0326] The terminal stores the music data received from the server in a buffer and plays it in real time.

[0327] Step 15:

[0328] The user continues eating while listening to the played music, adjusting the pace of the meal and emotional state.

[0329] Step 16:

[0330] The device continuously collects sensor data and emotion data and periodically transmits the data to a server.

[0331] Step 17:

[0332] The server analyzes the new data it receives and readjusts the tempo, genre and emotional characteristics of the music as needed.

[0333] Through this series of steps, the system can provide appropriate and personalized music while the user is eating, naturally pacing the meal and creating a relaxing atmosphere.

[0334] Example 2

[0335] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0336] Providing appropriate music during meals, naturally adjusting the pace of eating, and creating a relaxing atmosphere are challenges many people desire. However, existing systems have difficulty selecting music that reflects the user's eating situation and emotional state, and are limited to playing generic music. Furthermore, there are also limited systems that can reflect the type of food and eating speed in real time. Therefore, the present invention aims to solve these challenges.

[0337] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for detecting the type of food, eating speed, and sound using sensors, means for analyzing the detected data and selecting music according to the music genre corresponding to the type of food, the tempo corresponding to the eating speed, and the emotional state of the user, and means for generating the selected music data and playing it in real time. This makes it possible to provide a more personalized music experience based on environmental data during the meal and emotional data of the user.

[0338] text

[0339] A "sensor" is a device that detects physical changes and the surrounding environment and outputs that information as a signal.

[0340] "Food type" refers to the specific type of food consumed during a meal, primarily identified through image recognition.

[0341] "Eating speed" indicates the speed at which the user eats, and is measured using an acceleration sensor.

[0342] "Sound" refers to the sounds and noises generated during a meal and collected through a microphone.

[0343] "Detection" is the act of sensing specific information or data using sensors or other devices.

[0344] "Analysis" is the act of analyzing collected data to find meaning and patterns.

[0345] "Musical genre" is a term used to describe a category of music with a particular characteristic or style.

[0346] "Tempo" refers to the speed or rhythmic frequency of music.

[0347] "User's emotional state" refers to the emotions and moods the user is feeling at that time, as analyzed through the emotion engine.

[0348] "Selection" is the act of choosing the most suitable candidate from among multiple candidates.

[0349] "Generation" is the act of creating new content or results based on necessary data and information.

[0350] "Real-time" means that data is processed and output the instant it is collected.

[0351] "Music data" refers to digital data stored in a format that can be played as music.

[0352] text

[0353] The purpose of this invention is to adjust the pace of a meal and create a relaxing atmosphere by providing appropriate music during the meal. To achieve this, we will build a system that combines sensors and an emotion engine to collect user data, and a server analyzes the data and generates music. Specifically, the following components are required:

[0354] First, the device is equipped with a built-in camera, microphone, and accelerometer. These sensors collect environmental data while eating. The camera has high-resolution image capture capabilities and can identify the type of food and the user's facial expressions. The microphone collects audio, capturing the sound of dishes clashing and the tone of the user's voice. The accelerometer detects the movement of the user's hands and dishes and measures the speed of eating.

[0355] As a specific example, consider the case where a user is eating pasta. When the user starts to eat the pasta, the device's camera detects the pasta and sends the image data to the server. The microphone collects the sound of the fork and plate touching each other, and the accelerometer measures the user's hand movements. The emotion engine analyzes the user's facial expression and determines that they are feeling stressed.

[0356] The server receives and analyzes this data. Using a high-performance GPU, the server runs an image recognition algorithm to identify the food as pasta. It also measures the eating speed and recognizes that the user is eating quickly. Based on the data obtained from the emotion engine, it selects an Italian melody that will relax the user, who is feeling stressed, and generates that music data.

[0357] The generated music data is sent from the server to the device, which then stores it in a buffer and plays it in real time, allowing the user to naturally adjust the pace of their meal and enjoy a relaxed meal.

[0358] An example of a prompt sentence is, "Please explain the data processing flow of a system that automatically selects and plays appropriate music for a user eating pasta."

[0359] As described above, this system utilizes sensor data and emotional data to provide appropriate music to users, improving their dining experience.

[0360] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0361] text

[0362] Step 1:

[0363] (sensor data collection)

[0364] The device uses a camera, microphone, and accelerometer to collect environmental data while eating. The camera takes pictures of the meal and detects the type of food and the user's facial expression. The microphone collects the sound of dishes touching each other and the user's voice. The accelerometer detects the movement of the user's hands and the movement of dishes.

[0365] Input: Camera video, audio signal, acceleration data

[0366] Output: image data, audio data, acceleration data

[0367] Specific behavior:

[0368] The device takes a photo of the table every second while the user is eating and processes the images in real time. For example, it analyzes video of a user eating pasta to identify the type of pasta and the user's facial expression. At the same time, the microphone collects the sounds of dishes clashing and the tone of conversation, and the accelerometer captures data on the movement of the dishes.

[0369] Step 2:

[0370] (Data transmission)

[0371] The device sends the collected image data, voice data, acceleration data, and emotion data analyzed by the emotion engine to the server.

[0372] Input: image data, audio data, acceleration data, emotion data

[0373] Output: Compressed data packets

[0374] Specific behavior:

[0375] All data collected every second is compressed, packetized, and sent to a server over the Internet. Data is sent in a prioritized order to minimize data latency.

[0376] Step 3:

[0377] (Data Analysis)

[0378] The server analyzes the data sent from the device. It identifies the type of food and the user's facial expression from the image data, analyzes the sound of dishes clashing and the tone of the user's voice from the audio data, measures the eating speed from the acceleration data, and identifies the user's emotional state from the emotion data.

[0379] Input: Compressed data packet

[0380] Output: food type, eating rate, emotional state

[0381] Specific behavior:

[0382] The server uses a high-performance GPU to execute image recognition algorithms, such as detecting pasta from the received image data and identifying its type, while simultaneously measuring eating speed and analyzing emotional data from the sound of dishes clashing and the tone of the user's voice.

[0383] Step 4:

[0384] (Music data generation)

[0385] Based on the analysis results, the server determines the appropriate music genre and tempo, and generates music that matches the user's emotional state.

[0386] Input: food type, eating rate, emotional state

[0387] Output: Music data

[0388] Specific behavior:

[0389] The system determines the appropriate music genre and tempo based on the analysis results. For example, if it determines that a user is eating pasta quickly and feeling stressed, it will select a relaxing Italian melody and generate music data with a slow tempo.

[0390] Step 5:

[0391] (Music playing)

[0392] The device stores the music data received from the server in a buffer and plays the music in real time.

[0393] Input: Music data

[0394] Output: Playback music

[0395] Specific behavior:

[0396] When the device receives music data from the server, it immediately launches the music player and starts playing. This allows users to listen to music that matches their mood while eating. For example, if you are enjoying a relaxing meal of pasta, you can play a relaxing Italian melody.

[0397] (Application example 2)

[0398] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0399] Conventional dining environments lacked mechanisms to improve the user's dining experience, making it particularly difficult to respond in real time to the speed of eating and the user's emotions. Furthermore, the relaxing effect of personalized music was not fully realized. This resulted in a decline in meal quality and satisfaction, making it difficult to promote healthy eating habits.

[0400] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for detecting the type of food, eating speed, and sound using sensors, means for analyzing the detected data and determining a music genre according to the type of food and a tempo according to the eating speed, means for analyzing the user's emotions and personalizing music based on the analysis results, and means for generating the determined music data and playing it in real time. This makes it possible to provide personalized music in real time based on the captured data, improving the user's dining experience.

[0401] A "sensor" is a device that detects the environment or a specific situation and collects that information as electronic data.

[0402] "Food type" is a category for classifying specific foods in a meal, and is identified using image recognition or other methods.

[0403] "Eating speed" refers to the speed and rhythm of movements while eating, and is measured using an acceleration sensor or the like.

[0404] "Sound" refers to various acoustic information generated during a meal, which is collected using a microphone.

[0405] "Analysis" is the process of extracting and analyzing meaningful information based on data collected using sensors, etc.

[0406] A "music genre" is a category for classifying music, which is classified based on certain characteristics.

[0407] "Tempo" refers to the speed or beat of music and is the element that determines the speed of the rhythm.

[0408] "User emotion" refers to the emotional state determined from a person's facial expressions and voice as analyzed by the emotion engine.

[0409] "Personalization" means providing content that is optimized to suit the preferences and circumstances of each individual user.

[0410] "Real-time" means that processing and operations are carried out at the same speed as real time, and that they are reflected immediately without delay.

[0411] A "system" is a set of devices or programs that combine multiple components and functions and work together to achieve a specific purpose.

[0412] This invention is a system that uses sensors and an emotion engine to provide appropriate music in real time to improve the user's dining experience. The main hardware used is a terminal that includes a camera, microphone, acceleration sensor, and emotion engine, and a server that analyzes data and generates music.

[0413] System configuration

[0414] Terminal

[0415] Camera: Collects data to analyze the user's facial expressions and the type of food they eat.

[0416] Microphone: Collects sounds while eating, capturing the user's voice, the sound of dishes, etc.

[0417] Accelerometer: Measures the movement of the user's hands and the movement of the tableware to detect the speed at which the meal is being eaten.

[0418] Emotion engine: Analyzes the user's emotions based on data obtained from the camera and microphone.

[0419] server

[0420] Data analysis engine: Analyzes various data sent from the device and extracts food type, eating speed, and user emotions.

[0421] Music generation engine: Based on information from the data analysis engine, it determines the optimal music genre and tempo and generates music.

[0422] Real-time playback engine: The generated music is sent to the device in real time and provided to the user.

[0423] How it works

[0424] Camera: The camera captures the user's eating scene in real time and sends the image data to a server. Image recognition technology is used to analyze the type of food and the user's facial expression.

[0425] Microphone: The microphone collects sounds while you eat, capturing the tone of your voice and the sound of your tableware. The collected voice data is sent to a server and analyzed by the emotion engine.

[0426] Accelerometer: The accelerometer measures the movement of the user's hands and tableware to detect the speed at which the food is being eaten. This data is also sent to the server for analysis.

[0427] Emotion Engine: The emotion engine uses data from the camera and microphone to analyze the user's current emotional state. For example, if the user is feeling stressed, it can suggest relaxing music.

[0428] Server: The server receives and analyzes various data sent from the device. It determines the music genre according to the type of food, the tempo according to the eating speed, and also generates the optimal music based on the user's emotions.

[0429] Real-time playback: Music data generated on the server is sent to the device and played in real time, allowing users to naturally adjust the speed of their meal and enjoy a relaxed meal.

[0430] Specific examples

[0431] Example 1: When eating pasta

[0432] The user begins to eat pasta. The device's camera detects the pasta and sends the image data to the server. The device's microphone collects the sound of the fork and plate hitting each other and sends the sound data to the server. The device's accelerometer also measures the user's hand movements and sends the data to the server. The emotion engine then analyzes the user's facial expression, determines that the user is feeling stressed, and sends the emotion data to the server. The server analyzes the received data, recognizes that the pasta is being eaten quickly, and because the user is feeling stressed, generates relaxing music and sends the music data to the device. The device plays the music in real time, allowing the user to naturally adjust the pace of their meal and enjoy a relaxed meal.

[0433] Example 2: Eating sushi

[0434] The user begins to eat sushi. The device's camera detects the sushi and sends the image data to the server. The device's microphone collects the sound of the chopsticks hitting the plate and sends the sound data to the server. The device's accelerometer also measures the user's hand movements and sends the data to the server. The emotion engine then analyzes the user's tone of voice, determines that they are enjoying themselves, and sends the emotion data to the server. The server analyzes the received data, recognizes that the sushi is being eaten at an appropriate speed, generates upbeat music because the user is enjoying themselves, and sends the music data to the device. The device plays the music in real time, allowing the user to continue eating at an appropriate pace while enjoying the Japanese cuisine atmosphere.

[0435] Prompt Sentence Examples

[0436] "Emotion Engine": Analyzes the user's emotions from facial expressions.

[0437] "Meal Status Monitoring": The camera and microphone automatically detect the dining situation and play appropriate music.

[0438] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0439] Step 1:

[0440] The device captures the eating scene with a camera and collects image data. The camera captures the user's facial expressions and the type of food while eating, and sends the image data to a server in real time. The input is the image data acquired from the camera, and the output is the data sent to the server.

[0441] Step 2:

[0442] The device uses a microphone to collect voice data while eating. The microphone captures the tone of the user's voice and the sounds of tableware moving, and sends the voice data to a server. The input is the voice data acquired from the microphone, and the output is the data sent to the server.

[0443] Step 3:

[0444] The device uses an accelerometer to measure the movement of the user's hands and tableware. The accelerometer detects the speed of eating and sends that data to the server. The input is the movement data obtained from the accelerometer, and the output is the data sent to the server.

[0445] Step 4:

[0446] The server analyzes the received image data and identifies the type of food and the user's facial expression. Image recognition algorithms are used to identify the food classification and the user's emotional state. The input is the image data sent from the device, and the output is the classified food information and the user's emotional data.

[0447] Step 5:

[0448] The server analyzes the voice data and estimates the user's emotional state from the tone of their voice and the sounds of tableware movements. Using a voice analysis algorithm, emotions such as stress or joy are identified. The input is the voice data sent from the device, and the output is the estimated emotional state.

[0449] Step 6:

[0450] The server analyzes the acceleration data and identifies the speed at which the user is eating. Based on the data from the acceleration sensor, it determines how fast the user is eating. The input is the acceleration data sent from the device, and the output is the speed at which the user is eating.

[0451] Step 7:

[0452] The server determines the music genre and tempo based on the analyzed data. It selects the optimal music taking into account the type of ingredients, eating speed, and the user's emotional state. The input is food information, emotional data, and eating speed information, and the output is optimized music information.

[0453] Step 8:

[0454] The server generates music in real time based on the determined music information. Using a generative AI model, music data is created in real time and sent to the device. The input is optimized music information, and the output is generated music data.

[0455] Step 9:

[0456] The device stores the received music data in a buffer and plays the music in real time. Users can listen to appropriate music while eating, naturally adjusting the pace of their meal and enjoying a relaxed meal. The input is the music data sent from the server, and the output is the music being played.

[0457] Step 10:

[0458] The device continuously collects data and sends it to the server. By repeating this cycle, the music is adjusted appropriately according to the user's state. Input is data from the camera, microphone, and accelerometer, and output is data sent to the server.

[0459] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0460] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0461] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0462] [Second embodiment]

[0463] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0464] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0465] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0466] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0467] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0468] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0469] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0470] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0471] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0472] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0473] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0474] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0475] The present invention aims to provide appropriate music during meals, thereby adjusting the pace of the meal and creating a relaxed atmosphere. The specific configuration and operation of the system will be described below.

[0476] System configuration

[0477] The system consists of three main parts:

[0478] 1. Terminal: A device containing sensors (camera, microphone, accelerometer) that is installed on the dining table.

[0479] 2. Server: A central processing unit that analyzes data from the terminals and generates appropriate music data.

[0480] 3. User: The person who eats the food.

[0481] Operational Overview

[0482] The system uses devices to collect environmental data during meals, analyzes that data on a server, and generates and plays appropriate music. By repeating this cycle, the pace of meals is naturally adjusted, enriching food culture.

[0483] How it works

[0484] 1. Sensor data collection by the device

[0485] Camera: The device's built-in camera takes pictures of food on the table while you eat, and uses image recognition algorithms to identify the type of food.

[0486] Microphone: The device's microphone collects sounds made while eating, specifically the sounds of chopsticks and forks touching plates and the sounds of food being brought to the mouth.

[0487] Acceleration sensor: The device's built-in acceleration sensor detects the movement of the user's hands and tableware, and measures the speed at which the user eats.

[0488] 2. Data submission and analysis

[0489] Device: The device sends the sensor data (image data, sound data, acceleration data) collected to the server.

[0490] Server: The server analyzes the data it receives. For example, it identifies the type of food from image data, the type of tableware and the sound of the food being eaten from sound data, and the speed at which the food is eaten from acceleration data.

[0491] 3. Generating music data

[0492] Server: Based on the data analyzed by the server, it determines the appropriate music genre (for example, Italian for pasta) and tempo (according to the eating speed). It then generates music data in real time based on the determined music genre and tempo.

[0493] 4. Playing Music

[0494] Device: The device receives music data from the server, stores it in a buffer, and plays the music in real time. Users can listen to appropriate music while eating, and naturally adjust the pace of their meal.

[0495] Specific examples

[0496] Example 1: When eating pasta

[0497] The user begins to eat pasta. The device's camera detects the pasta and sends the image data to the server. The device's microphone collects the sound of the fork and plate touching each other and sends the sound data to the server. The device's accelerometer also measures the user's hand movements and sends the data to the server.

[0498] The server analyzes the data it receives and determines that the food is pasta and that the person is eating it quickly. The server then generates an Italian melody at a slightly slower tempo and sends the music data to the device. The device then plays the music in real time, allowing the user to naturally adjust the pace of their meal and enjoy a relaxed meal.

[0499] Example 2: Eating sushi

[0500] The user begins to eat sushi. The device's camera detects the sushi and sends the image data to the server. The device's microphone collects the sound of the chopsticks hitting the plate and sends the sound data to the server. The device's accelerometer also measures the user's hand movements and sends the data to the server.

[0501] The server analyzes the received data and determines that the food is sushi and that the user is eating at an appropriate speed. The server then generates a Japanese-style rhythm at the appropriate tempo and sends the music data to the device. The device plays the music in real time, allowing the user to continue eating at an appropriate pace while enjoying the Japanese cuisine.

[0502] This invention allows users to naturally adjust the pace of their meals, enriching food culture, and is expected to promote healthy eating habits and increase the enjoyment of meals.

[0503] The processing flow will be explained below.

[0504] Step 1:

[0505] The device activates the camera, microphone, and accelerometer installed on the table.

[0506] Step 2:

[0507] The device uses a camera to take pictures of food on the table and collect image data in real time.

[0508] Step 3:

[0509] The device uses image recognition algorithms to identify the type of food from the collected image data.

[0510] Step 4:

[0511] The device uses a microphone to collect sounds such as dishes clashing and other sounds made while eating.

[0512] Step 5:

[0513] The device uses an accelerometer to detect the movement of the user's hands and tableware and measure the speed at which they eat.

[0514] Step 6:

[0515] The image data, sound data, and acceleration data collected by the terminal are combined into a single data packet and transmitted to the server.

[0516] Step 7:

[0517] The server receives the data packets and analyzes the image data to determine the type of food.

[0518] Step 8:

[0519] The server analyzes the sound data and identifies the type of tableware and the sound of its operation.

[0520] Step 9:

[0521] The server analyzes the acceleration data and calculates the user's eating speed.

[0522] Step 10:

[0523] Based on the analysis results, the server determines the music genre according to the type of food and the tempo according to the eating speed.

[0524] Step 11:

[0525] The server uses a music generation algorithm to generate music data in real time according to the determined genre and tempo.

[0526] Step 12:

[0527] The server transmits the generated music data to the terminal.

[0528] Step 13:

[0529] The terminal stores the music data received from the server in a buffer and plays it in real time.

[0530] Step 14:

[0531] The user continues eating while listening to the played music and paces the meal.

[0532] Step 15:

[0533] The device continuously collects sensor data and periodically transmits the data to the server.

[0534] Step 16:

[0535] The server analyzes the new data it receives and readjusts the tempo and genre of the music as needed.

[0536] Through this series of steps, the system can provide appropriate music while the user is eating, naturally pacing the meal and creating a relaxed atmosphere.

[0537] Example 1

[0538] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0539] In traditional dining environments, there is a lack of ways to adjust the pace and atmosphere of the meal, making it difficult to promote healthy eating habits and improve the enjoyment of meals. While there are systems that provide music, there are no systems that dynamically generate music in real time according to the food and pace of the meal. This makes it difficult to maximize the enjoyment and relaxation of the meal.

[0540] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0541] In this invention, the server includes means for detecting the type of food, eating speed, and sound using sensors, means for transmitting the detected data to a cloud computing device, means for analyzing the detected data in the cloud computing device, means for determining a music genre according to the type of food and a tempo according to the eating speed based on the analysis results, means for generating the determined music data in real time using a generative AI model, means for transmitting the generated music data to a terminal, and means for playing the generated music data in real time. This makes it possible to dynamically adjust the environment during the meal and optimize the pace and atmosphere of the user's meal.

[0542] "Sensors" are devices for detecting food type, eating speed, and sound.

[0543] A "cloud computing device" is an external computer system provided over the Internet for performing data analysis and computational processing.

[0544] A "terminal" is a user-side device that contains sensors, collects and transmits data, and plays music.

[0545] "Image recognition" is a technology that analyzes image data captured by a camera to identify specific objects or types of food.

[0546] An "accelerometer" is a device that detects the user's movements and the movement of tableware, and is used to measure eating speed.

[0547] A "generative AI model" is an algorithm or program that uses artificial intelligence to generate music data in real time.

[0548] A "prompt sentence" is text data input into a generative AI model, and is a sentence that specifies the processing content and conditions to be performed by the generative AI model.

[0549] "Real-time" means that the processing or reaction occurs almost instantaneously or in a very short time.

[0550] MODE FOR CARRYING OUT THE INVENTION

[0551] This system aims to adjust the pace of a meal and create a relaxing atmosphere by providing appropriate music during the meal. This system is mainly composed of three elements: a terminal, a server, and a user.

[0552] Hardware and software used

[0553] Hardware:

[0554] Terminal: A device that includes sensors and is installed on the dining table. For example, a Raspberry Pi with its camera module, microphone module, and accelerometer module can be used.

[0555] Server: A high-performance central processing unit. Uses cloud services (e.g., AWS or Google Cloud).

[0556] software:

[0557] Terminal software: A custom Python script running on the Raspberry Pi, used to collect data from the sensors and send it to the server.

[0558] Server-side software: Runs on the cloud and performs data analysis and music generation. For example, data analysis algorithms written in Python and generative AI models for music generation (e.g., OpenAI GPT-3 and Jukedeck).

[0559] System Operation

[0560] This system uses devices to collect environmental data during meals, analyzes that data on a server, and generates and plays appropriate music. By repeating this cycle, the pace of meals is naturally adjusted, enriching food culture.

[0561] Specific operation example

[0562] Example 1: When eating pasta

[0563] 1. The device detects pasta on the table using a camera, analyzes the image data using OpenCV, and recognizes that it is pasta.

[0564] 2. The device's microphone collects the sound of the fork and plate touching each other, and Librosa is used to analyze the sound data.

[0565] 3. The device's accelerometer measures the user's hand movements, and a Python script analyzes the eating speed.

[0566] 4. The device sends this data to the server via an HTTP request.

[0567] 5. The server recognizes the pasta from the image data and confirms from the sound and acceleration data that the user is eating quickly.

[0568] 6. The server generates music with a slightly slower tempo and Italian melody using a generative AI model.

[0569] Example prompt:

[0570] "Recognize pasta from image data, and if the user is eating quickly from sound and acceleration data, generate music with a slightly slower tempo using an Italian melody."

[0571] 7. The server sends the generated music data to the device.

[0572] 8. The device plays music data in real time, allowing the user to relax and enjoy their meal.

[0573] Example 2: Eating sushi

[0574] 1. The device detects sushi on the table using a camera, analyzes the image data using OpenCV, and recognizes that it is sushi.

[0575] 2. The device's microphone collects the sound of the chopsticks and plate touching each other, and the sound data is analyzed using Librosa.

[0576] 3. The device's accelerometer measures the user's hand movements, and a Python script analyzes the eating speed.

[0577] 4. The device sends this data to the server via an HTTP request.

[0578] 5. The server recognizes the sushi from the image data and confirms that it is being eaten at the appropriate speed from the sound and acceleration data.

[0579] 6. The server generates music with an appropriate tempo and Japanese rhythm using an AI model.

[0580] Example prompt:

[0581] "Recognize sushi from image data, and generate Japanese-style rhythmic music if the user is eating at the appropriate speed based on sound and acceleration data."

[0582] 7. The server sends the generated music data to the device.

[0583] 8. The device plays music data in real time, allowing the user to enjoy their meal at a comfortable pace.

[0584] conclusion

[0585] This invention allows users to naturally adjust the pace of their meals, increasing the enjoyment of meals and is expected to promote healthy eating habits.

[0586] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0587] Step 1:

[0588] Sensor data is acquired by the device.

[0589] Specific behavior:

[0590] The camera takes a picture of the food on the table at a rate of one per second and collects image data.

[0591] A microphone continuously records the acoustic environment at the table, collecting the sounds of tableware and food as digital signals.

[0592] The acceleration sensor measures the movement of the user's hands and tableware in real time and collects acceleration data.

[0593] Input: Image data, sound data, and acceleration data from sensors installed on the dining table.

[0594] Output: Collected raw data (image data, sound data, acceleration data).

[0595] Step 2:

[0596] The device sends the collected sensor data to the server.

[0597] Specific behavior:

[0598] The device sends this data to the server as an HTTP request at regular intervals, for example, every 10 seconds.

[0599] Input: Raw data collected by the device (image data, sound data, acceleration data).

[0600] Output: The raw data sent to the server.

[0601] Step 3:

[0602] The server parses the data it receives.

[0603] Specific behavior:

[0604] The server analyzes the image data using computer vision algorithms (e.g., OpenCV or TensorFlow) to identify the type of food.

[0605] The server analyzes the sound data using an acoustic analysis library (e.g., Librosa) to identify the type of tableware and the sound of its operation.

[0606] The server analyzes the acceleration data and uses Python's NumPy library to measure the user's eating pace.

[0607] Input: Raw data (image data, sound data, acceleration data) sent to the server.

[0608] Output: Analyzed results (type of food, sound of dishes, eating pace).

[0609] Step 4:

[0610] The server generates music data based on the analysis results.

[0611] Specific behavior:

[0612] The server then uses the analysis results to determine the appropriate music genre and tempo. For example, if the food is pasta and the eating pace is fast, a slower Italian melody will be selected.

[0613] The server inputs prompt sentences into a generative AI model (e.g., OpenAI's GPT-3 or Jukedeck) and generates music data in real time.

[0614] Example prompt:

[0615] "Recognize pasta from image data, and if the user is eating quickly from sound and acceleration data, generate music with a slightly slower tempo using an Italian melody."

[0616] Input: Analyzed results (food type, dish sounds, eating pace).

[0617] Output: The generated music data.

[0618] Step 5:

[0619] The server transmits the generated music data to the terminal.

[0620] Specific behavior:

[0621] The music data generated by the server is sent to the terminal as an HTTP response.

[0622] Input: Generated music data.

[0623] Output: The music data sent to the device.

[0624] Step 6:

[0625] The device plays the music data in real time.

[0626] Specific behavior:

[0627] The music data received by the device is stored in a buffer and played in real time using Python's Pygame library, etc.

[0628] Users can relax and enjoy their meal while listening to music.

[0629] Input: Music data sent to the device.

[0630] Output: The played music.

[0631] (Application example 1)

[0632] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0633] In today's virtual stores and restaurants, unlike physical environments, users' behavior is left unmonitored while they eat or shop, making it difficult to adjust the pace of the meal or create a relaxing atmosphere. Furthermore, there is a lack of systems that can enhance the enjoyment of meals by providing appropriate music and promote healthy eating habits. Furthermore, the technology to dynamically adjust music for individual users is underdeveloped.

[0634] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0635] In this invention, the server includes means for detecting a user's behavior on the electronic device, the type of food, the eating speed, and sounds using sensors, means for analyzing the detected data and determining a music genre according to the type of food and a tempo according to the eating speed, means for generating the determined music data and playing it in real time, and means for dynamically changing the music data using a generative AI model to adjust the user's eating pace, thereby providing a relaxed atmosphere during the user's meal, allowing the user to naturally adjust the eating pace, and enriching the experience in the virtual store or virtual restaurant.

[0636] definition statement

[0637] "Sensor" is a general term for electronic devices that detect user behavior and environmental data in real time.

[0638] "Electronic device" refers to any electronic device that can connect to the internet and that a user uses while eating or shopping. Examples include smartphones, tablets, and personal computers.

[0639] "User behavior" refers to the actions and movements of users when eating or shopping, including hand movements, use of tableware, etc.

[0640] "Food type" refers to the specific type of food or dish a user is eating, such as pasta or sushi.

[0641] "Eating speed" refers to the speed at which a user brings food to their mouth when eating.

[0642] "Sound" refers to the sounds that occur while eating, including, for example, the sound of chewing food and the sound of dishes clashing.

[0643] "Means" refers to the methods or devices used to achieve a particular purpose.

[0644] "Analyzing data" refers to performing calculations and processing to extract information from collected data and find patterns and trends.

[0645] "Music genre" refers to a type or style of music, such as classical, jazz, or pop.

[0646] "Tempo" refers to the speed and rhythm of music or movement.

[0647] "Generating music data" refers to creating new music based on the analysis results.

[0648] "Playing in real time" means playing the data immediately after receiving it.

[0649] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to generate and analyze data.

[0650] "Dynamically changing music data" refers to changing the content of the music being played in real time based on new data or conditions along the way.

[0651] MODE FOR CARRYING OUT THE INVENTION

[0652] This invention is a system that monitors the user's behavior and eating pace in a virtual store or restaurant and provides appropriate music. This system uses sensors to collect user behavior data in real time and dynamically generates and changes music data based on the analysis results, enriching the user's dining experience.

[0653] System configuration and operation overview

[0654] The system has the following components:

[0655] 1. Device: An electronic device equipped with sensors such as a camera, microphone, and accelerometer. This device collects information on user behavior, eating speed, and environmental sounds in real time.

[0656] 2. Server: A central processing unit that analyzes data sent from the device and determines the music genre and tempo according to the type and speed of eating, and dynamically changes the music data using a generative AI model.

[0657] 3. User: A person who dine in a virtual environment. They listen to music from their device and enjoy a meal in a relaxed atmosphere.

[0658] How it works

[0659] Device data collection:

[0660] It uses a camera to capture footage of the user while they eat and analyzes the type of food they eat and the pace at which they eat.

[0661] It uses a microphone to collect sounds made by the user while eating, such as the clinking of dishes and chewing.

[0662] It uses an accelerometer to measure the movement of the user's hands and utensils to determine the speed at which they are eating.

[0663] Data analysis and music data generation:

[0664] The data collected by the device is sent to a server, which analyzes it and identifies the type of food and the speed at which it is eaten.

[0665] Based on the analyzed data, the server determines the appropriate music genre (for example, Italian music for Italian cuisine, Japanese music for Japanese cuisine) and tempo.

[0666] Furthermore, a generative AI model is used to dynamically generate and change music data based on the analysis results, providing a music environment that is optimal for the user's eating pace.

[0667] Real-time music playback:

[0668] The generated music data is sent to the device and played in real time, allowing users to naturally adjust the pace of their meal and enjoy it in a relaxed atmosphere.

[0669] Specific Examples

[0670] Example 1: Eating pasta at a virtual restaurant:

[0671] If a user is eating pasta in a virtual restaurant, the camera will recognize the pasta and find that they are eating quickly. The server will then analyze this data and generate Italian music at a slower tempo, encouraging the user to slow down and relax.

[0672] Example 2: Drinking coffee at a virtual cafe:

[0673] If a user is relaxing in a virtual cafe with a cup of coffee, the camera will recognize the coffee cup and detect a lack of movement. The server will then analyze this data and select acoustic music to create a more relaxing environment.

[0674] Prompt Sentence Examples

[0675] Below are some examples of specific prompts for implementing patents:

[0676] When a user eats pasta at a virtual restaurant, a camera and microphone capture their behavior and send the data to a server. The server analyzes the speed at which the user is eating and plays appropriate Italian music at a slightly slower tempo. The system uses Python's OpenCV, sounddevice, NumPy, and asyncio libraries.

[0677] This invention will improve the user experience in the virtual store, allowing them to enjoy their food and eat at a healthy pace.

[0678] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0679] Program processing steps

[0680] Step 1:

[0681] The device uses a camera, microphone, and accelerometer to collect real-time data on the user's behavior, ambient sounds, food type, and eating speed. During this data collection process, the camera captures images of the table and uses a recognition algorithm to identify the type of food (e.g., pasta, sushi, etc.). In addition, the microphone collects ambient sounds (e.g., the sound of dishes clattering, chewing, etc.), and the accelerometer measures the user's eating speed from the movement of their hands and dishes.

[0682] Input: Camera video data, audio data, acceleration data

[0683] Output: User behavior data (type of food, eating speed, environmental sounds)

[0684] Step 2:

[0685] The device sends the collected data to the server. In the data transmission process, the device uploads image data of the type of food recognized by the camera, audio data collected by the microphone, and eating speed data obtained by the accelerometer to the server.

[0686] Input: User behavior data (type of food, eating speed, environmental sounds)

[0687] Output: Raw data sent to the server

[0688] Step 3:

[0689] The server analyzes the received data to identify the type of food, eating speed, and environmental sounds. During the analysis process, an image recognition algorithm (using OpenCV) is used to identify the type of food, an audio analysis algorithm is used to classify the collected environmental sounds, and the speed of eating is calculated based on the acceleration data. This allows for specific analysis results to be obtained based on the characteristics of each data.

[0690] Input: Raw data sent to the server

[0691] Output: Analyzed data (food type, eating speed, environmental sound classification)

[0692] Step 4:

[0693] The server determines the appropriate music genre and tempo based on the analysis results. In the process of dynamically generating or changing music data using a generative AI model according to the analysis data, for example, if you eat quickly, it will select relaxing music with a slow tempo, and it will determine the appropriate music genre depending on the type of food (e.g., Italian music for pasta).

[0694] Input: Analyzed data (food type, eating speed, environmental sound classification)

[0695] Output: Determined music genre and tempo

[0696] Step 5:

[0697] The server sends the generated music data to the device, which then plays the music in real time. During the music playback process, the music data dynamically modified by the generative AI model is sent to the device, which stores the music in a buffer and then plays it through the speaker.

[0698] Input: Determined music genre and tempo

[0699] Output: Music data to be played

[0700] Step 6:

[0701] The user continues eating while listening to appropriate music. As a result, the user can naturally adjust the pace of their meal and enjoy it in a relaxed atmosphere. The device again monitors the user's reactions and behavior, and the entire system operates in a feedback loop.

[0702] Input: Music data to be played

[0703] Output: Relaxed eating environment, naturally adjusted eating pace

[0704] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0705] The present invention aims to provide appropriate music during meals to adjust the pace of the meal and create a relaxing atmosphere, and by combining it with an emotion engine that recognizes the user's emotions, it provides a more personalized music experience.

[0706] System configuration

[0707] The system consists of four main parts:

[0708] 1. Terminal: A device installed on the dining table that includes sensors (camera, microphone, accelerometer) and an emotion engine.

[0709] 2. Server: A central processing unit that analyzes data from the terminals and generates appropriate music data.

[0710] 3. Emotion engine: Recognizes the user's emotions and sends the data to the server.

[0711] 4. User: The person who eats the food.

[0712] Operational Overview

[0713] The system uses the device to collect environmental data during the meal, analyzes that data on the server, and takes into account data from the emotion engine to generate and play appropriate music. By repeating this cycle, the pace of the meal is naturally adjusted, enriching food culture.

[0714] How it works

[0715] 1. Sensor data collection by the device

[0716] Camera: The device's built-in camera takes pictures of the food on the table while the user is eating. Image recognition algorithms are used to identify the type of food. The device also analyzes the user's facial expressions and sends them to the emotion engine as emotional data.

[0717] Microphone: The device's microphone collects sounds made while eating, such as the sound of chopsticks and forks touching tableware and the tone of the user's voice, and sends these to the emotion engine.

[0718] Acceleration sensor: The device's built-in accelerometer detects the movement of the user's hands and tableware and measures the speed at which they eat.

[0719] 2. Data submission and analysis

[0720] Device: The device sends the image data, sound data, acceleration data collected, and emotion data analyzed by the emotion engine to the server.

[0721] Server: The server analyzes the data it receives. For example, it identifies the type of food from image data, the type of tableware and the sound of the movements from sound data, the speed of eating from acceleration data, and the user's current emotional state from emotion data.

[0722] 3. Generating music data

[0723] Server: Based on the data analyzed by the server, the music genre is determined according to the type of food and the tempo according to the eating speed, and the music is selected according to the user's emotional state. For example, if the user is feeling stressed, it will generate relaxing music, and if the user is in a happy mood, it will generate cheerful music.

[0724] Server: Generates music data in real time based on the determined music genre, tempo, and emotion.

[0725] 4. Playing Music

[0726] Device: The device stores the music data received from the server in a buffer and plays the music in real time. Users can listen to music that matches their emotions while eating, allowing them to naturally adjust the pace of their meal and enjoy a relaxed meal.

[0727] Specific examples

[0728] Example 1: When eating pasta

[0729] The user begins to eat pasta. The device's camera detects the pasta and sends the image data to the server. The device's microphone collects the sound of the fork and plate touching each other and sends the sound data to the server. The device's accelerometer measures the user's hand movements and sends the data to the server. The emotion engine then analyzes the user's facial expressions, determines that they are feeling stressed, and sends the emotion data to the server.

[0730] The server analyzes the data it receives, recognizes that the food is pasta and that the user is eating it quickly, and generates a relaxing Italian melody at a slow tempo because the user is feeling stressed. The music data is then sent to the device, which plays the music in real time, allowing the user to naturally adjust the pace of their meal and enjoy a relaxed meal.

[0731] Example 2: Eating sushi

[0732] The user begins to eat sushi. The device's camera detects the sushi and sends the image data to the server. The device's microphone collects the sound of the chopsticks hitting the plate and sends the sound data to the server. The device's accelerometer measures the user's hand movements and sends the data to the server. The emotion engine then analyzes the user's tone of voice, determines that they are enjoying the food, and sends the emotion data to the server.

[0733] The server analyzes the data it receives, recognizes that the food is sushi and that the user is eating at an appropriate pace, and because the user is enjoying themselves, generates a cheerful Japanese-style rhythm at the same tempo and sends the music data to the device. The device plays the music in real time, allowing the user to continue eating at an appropriate pace while enjoying the Japanese food atmosphere.

[0734] This invention allows users to naturally adjust the pace of their meals and enrich their food culture. It is also expected to promote healthy eating habits and increase the enjoyment of meals. Furthermore, by utilizing the emotion engine, it is possible to provide a personalized music experience that corresponds to the user's emotional state.

[0735] The processing flow will be explained below.

[0736] Step 1:

[0737] The device activates the camera, microphone, accelerometer, and emotion engine installed on the table.

[0738] Step 2:

[0739] The device uses a camera to capture images of food on the table and collects image data in real time, while the emotion engine simultaneously collects facial expression data from the user.

[0740] Step 3:

[0741] The device uses image recognition algorithms to identify the type of food from the collected image data, while an emotion engine analyzes facial expression data to identify the user's emotional state.

[0742] Step 4:

[0743] The device uses a microphone to collect sounds such as the clinking of dishes and other sounds made while eating, and the emotion engine also collects tone data from the user's voice.

[0744] Step 5:

[0745] The device uses an accelerometer to detect the movement of the user's hands and tableware and measure the speed at which they eat.

[0746] Step 6:

[0747] The image data, sound data, acceleration data collected by the terminal, and emotion data analyzed by the emotion engine are compiled into a single data packet and sent to the server.

[0748] Step 7:

[0749] The server receives the data packets and analyzes the image data to determine the type of food.

[0750] Step 8:

[0751] The server analyzes the sound data and identifies the type of tableware and the sound of its operation.

[0752] Step 9:

[0753] The server analyzes the acceleration data and calculates the user's eating speed.

[0754] Step 10:

[0755] The server analyzes the emotional data and recognizes the user's current emotional state.

[0756] Step 11:

[0757] Based on the analysis results, the server determines the music genre according to the type of food, the tempo according to the eating speed, and the characteristics of the music according to the emotional state.

[0758] Step 12:

[0759] The server uses a music generation algorithm to generate music data in real time according to the determined genre, tempo, and emotion.

[0760] Step 13:

[0761] The server transmits the generated music data to the terminal.

[0762] Step 14:

[0763] The terminal stores the music data received from the server in a buffer and plays it in real time.

[0764] Step 15:

[0765] The user continues eating while listening to the played music, adjusting the pace of the meal and emotional state.

[0766] Step 16:

[0767] The device continuously collects sensor data and emotion data and periodically transmits the data to a server.

[0768] Step 17:

[0769] The server analyzes the new data it receives and readjusts the tempo, genre and emotional characteristics of the music as needed.

[0770] Through this series of steps, the system can provide appropriate and personalized music while the user is eating, naturally pacing the meal and creating a relaxing atmosphere.

[0771] Example 2

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

[0773] Providing appropriate music during meals, naturally adjusting the pace of eating, and creating a relaxing atmosphere are challenges many people desire. However, existing systems have difficulty selecting music that reflects the user's eating situation and emotional state, and are limited to playing generic music. Furthermore, there are also limited systems that can reflect the type of food and eating speed in real time. Therefore, the present invention aims to solve these challenges.

[0774] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for detecting the type of food, eating speed, and sound using sensors, means for analyzing the detected data and selecting music according to the music genre corresponding to the type of food, the tempo corresponding to the eating speed, and the emotional state of the user, and means for generating the selected music data and playing it in real time. This makes it possible to provide a more personalized music experience based on environmental data during the meal and emotional data of the user.

[0775] text

[0776] A "sensor" is a device that detects physical changes and the surrounding environment and outputs that information as a signal.

[0777] "Food type" refers to the specific type of food consumed during a meal, primarily identified through image recognition.

[0778] "Eating speed" indicates the speed at which the user eats, and is measured using an acceleration sensor.

[0779] "Sound" refers to the sounds and noises generated during a meal and collected through a microphone.

[0780] "Detection" is the act of sensing specific information or data using sensors or other devices.

[0781] "Analysis" is the act of analyzing collected data to find meaning and patterns.

[0782] "Musical genre" is a term used to describe a category of music with a particular characteristic or style.

[0783] "Tempo" refers to the speed or rhythmic frequency of music.

[0784] "User's emotional state" refers to the emotions and moods the user is feeling at that time, as analyzed through the emotion engine.

[0785] "Selection" is the act of choosing the most suitable candidate from among multiple candidates.

[0786] "Generation" is the act of creating new content or results based on necessary data and information.

[0787] "Real-time" means that data is processed and output the instant it is collected.

[0788] "Music data" refers to digital data stored in a format that can be played as music.

[0789] text

[0790] The purpose of this invention is to adjust the pace of a meal and create a relaxing atmosphere by providing appropriate music during the meal. To achieve this, we will build a system that combines sensors and an emotion engine to collect user data, and a server analyzes the data and generates music. Specifically, the following components are required:

[0791] First, the device is equipped with a built-in camera, microphone, and accelerometer. These sensors collect environmental data while eating. The camera has high-resolution image capture capabilities and can identify the type of food and the user's facial expressions. The microphone collects audio, capturing the sound of dishes clashing and the tone of the user's voice. The accelerometer detects the movement of the user's hands and dishes and measures the speed of eating.

[0792] As a specific example, consider the case where a user is eating pasta. When the user starts to eat the pasta, the device's camera detects the pasta and sends the image data to the server. The microphone collects the sound of the fork and plate touching each other, and the accelerometer measures the user's hand movements. The emotion engine analyzes the user's facial expression and determines that they are feeling stressed.

[0793] The server receives and analyzes this data. Using a high-performance GPU, the server runs an image recognition algorithm to identify the food as pasta. It also measures the eating speed and recognizes that the user is eating quickly. Based on the data obtained from the emotion engine, it selects an Italian melody that will relax the user, who is feeling stressed, and generates that music data.

[0794] The generated music data is sent from the server to the device, which then stores it in a buffer and plays it in real time, allowing the user to naturally adjust the pace of their meal and enjoy a relaxed meal.

[0795] An example of a prompt sentence is, "Please explain the data processing flow of a system that automatically selects and plays appropriate music for a user eating pasta."

[0796] As described above, this system utilizes sensor data and emotional data to provide appropriate music to users, improving their dining experience.

[0797] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0798] text

[0799] Step 1:

[0800] (sensor data collection)

[0801] The device uses a camera, microphone, and accelerometer to collect environmental data while eating. The camera takes pictures of the meal and detects the type of food and the user's facial expression. The microphone collects the sound of dishes touching each other and the user's voice. The accelerometer detects the movement of the user's hands and the movement of dishes.

[0802] Input: Camera video, audio signal, acceleration data

[0803] Output: image data, audio data, acceleration data

[0804] Specific behavior:

[0805] The device takes a photo of the table every second while the user is eating and processes the images in real time. For example, it analyzes video of a user eating pasta to identify the type of pasta and the user's facial expression. At the same time, the microphone collects the sounds of dishes clashing and the tone of conversation, and the accelerometer captures data on the movement of the dishes.

[0806] Step 2:

[0807] (Data transmission)

[0808] The device sends the collected image data, voice data, acceleration data, and emotion data analyzed by the emotion engine to the server.

[0809] Input: image data, audio data, acceleration data, emotion data

[0810] Output: Compressed data packets

[0811] Specific behavior:

[0812] All data collected every second is compressed, packetized, and sent to a server over the Internet. Data is sent in a prioritized order to minimize data latency.

[0813] Step 3:

[0814] (Data Analysis)

[0815] The server analyzes the data sent from the device. It identifies the type of food and the user's facial expression from the image data, analyzes the sound of dishes clashing and the tone of the user's voice from the audio data, measures the eating speed from the acceleration data, and identifies the user's emotional state from the emotion data.

[0816] Input: Compressed data packet

[0817] Output: food type, eating rate, emotional state

[0818] Specific behavior:

[0819] The server uses a high-performance GPU to execute image recognition algorithms, such as detecting pasta from the received image data and identifying its type, while simultaneously measuring eating speed and analyzing emotional data from the sound of dishes clashing and the tone of the user's voice.

[0820] Step 4:

[0821] (Music data generation)

[0822] Based on the analysis results, the server determines the appropriate music genre and tempo, and generates music that matches the user's emotional state.

[0823] Input: food type, eating rate, emotional state

[0824] Output: Music data

[0825] Specific behavior:

[0826] The system determines the appropriate music genre and tempo based on the analysis results. For example, if it determines that a user is eating pasta quickly and feeling stressed, it will select a relaxing Italian melody and generate music data with a slow tempo.

[0827] Step 5:

[0828] (Music playing)

[0829] The device stores the music data received from the server in a buffer and plays the music in real time.

[0830] Input: Music data

[0831] Output: Playback music

[0832] Specific behavior:

[0833] When the device receives music data from the server, it immediately launches the music player and starts playing. This allows users to listen to music that matches their mood while eating. For example, if you are enjoying a relaxing meal of pasta, you can play a relaxing Italian melody.

[0834] (Application example 2)

[0835] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0836] Conventional dining environments lacked mechanisms to improve the user's dining experience, making it particularly difficult to respond in real time to the speed of eating and the user's emotions. Furthermore, the relaxing effect of personalized music was not fully realized. This resulted in a decline in meal quality and satisfaction, making it difficult to promote healthy eating habits.

[0837] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for detecting the type of food, eating speed, and sound using sensors, means for analyzing the detected data and determining a music genre according to the type of food and a tempo according to the eating speed, means for analyzing the user's emotions and personalizing music based on the analysis results, and means for generating the determined music data and playing it in real time. This makes it possible to provide personalized music in real time based on the captured data, improving the user's dining experience.

[0838] A "sensor" is a device that detects the environment or a specific situation and collects that information as electronic data.

[0839] "Food type" is a category for classifying specific foods in a meal, and is identified using image recognition or other methods.

[0840] "Eating speed" refers to the speed and rhythm of movements while eating, and is measured using an acceleration sensor or the like.

[0841] "Sound" refers to various acoustic information generated during a meal, which is collected using a microphone.

[0842] "Analysis" is the process of extracting and analyzing meaningful information based on data collected using sensors, etc.

[0843] A "music genre" is a category for classifying music, which is classified based on certain characteristics.

[0844] "Tempo" refers to the speed or beat of music and is the element that determines the speed of the rhythm.

[0845] "User emotion" refers to the emotional state determined from a person's facial expressions and voice as analyzed by the emotion engine.

[0846] "Personalization" means providing content that is optimized to suit the preferences and circumstances of each individual user.

[0847] "Real-time" means that processing and operations are carried out at the same speed as real time, and that they are reflected immediately without delay.

[0848] A "system" is a set of devices or programs that combine multiple components and functions and work together to achieve a specific purpose.

[0849] This invention is a system that uses sensors and an emotion engine to provide appropriate music in real time to improve the user's dining experience. The main hardware used is a terminal that includes a camera, microphone, acceleration sensor, and emotion engine, and a server that analyzes data and generates music.

[0850] System configuration

[0851] Terminal

[0852] Camera: Collects data to analyze the user's facial expressions and the type of food they eat.

[0853] Microphone: Collects sounds while eating, capturing the user's voice, the sound of dishes, etc.

[0854] Accelerometer: Measures the movement of the user's hands and the movement of the tableware to detect the speed at which the meal is being eaten.

[0855] Emotion engine: Analyzes the user's emotions based on data obtained from the camera and microphone.

[0856] server

[0857] Data analysis engine: Analyzes various data sent from the device and extracts food type, eating speed, and user emotions.

[0858] Music generation engine: Based on information from the data analysis engine, it determines the optimal music genre and tempo and generates music.

[0859] Real-time playback engine: The generated music is sent to the device in real time and provided to the user.

[0860] How it works

[0861] Camera: The camera captures the user's eating scene in real time and sends the image data to a server. Image recognition technology is used to analyze the type of food and the user's facial expression.

[0862] Microphone: The microphone collects sounds while you eat, capturing the tone of your voice and the sound of your tableware. The collected voice data is sent to a server and analyzed by the emotion engine.

[0863] Accelerometer: The accelerometer measures the movement of the user's hands and tableware to detect the speed at which the food is being eaten. This data is also sent to the server for analysis.

[0864] Emotion Engine: The emotion engine uses data from the camera and microphone to analyze the user's current emotional state. For example, if the user is feeling stressed, it can suggest relaxing music.

[0865] Server: The server receives and analyzes various data sent from the device. It determines the music genre according to the type of food, the tempo according to the eating speed, and also generates the optimal music based on the user's emotions.

[0866] Real-time playback: Music data generated on the server is sent to the device and played in real time, allowing users to naturally adjust the speed of their meal and enjoy a relaxed meal.

[0867] Specific examples

[0868] Example 1: When eating pasta

[0869] The user begins to eat pasta. The device's camera detects the pasta and sends the image data to the server. The device's microphone collects the sound of the fork and plate hitting each other and sends the sound data to the server. The device's accelerometer also measures the user's hand movements and sends the data to the server. The emotion engine then analyzes the user's facial expression, determines that the user is feeling stressed, and sends the emotion data to the server. The server analyzes the received data, recognizes that the pasta is being eaten quickly, and because the user is feeling stressed, generates relaxing music and sends the music data to the device. The device plays the music in real time, allowing the user to naturally adjust the pace of their meal and enjoy a relaxed meal.

[0870] Example 2: Eating sushi

[0871] The user begins to eat sushi. The device's camera detects the sushi and sends the image data to the server. The device's microphone collects the sound of the chopsticks hitting the plate and sends the sound data to the server. The device's accelerometer also measures the user's hand movements and sends the data to the server. The emotion engine then analyzes the user's tone of voice, determines that they are enjoying themselves, and sends the emotion data to the server. The server analyzes the received data, recognizes that the sushi is being eaten at an appropriate speed, generates upbeat music because the user is enjoying themselves, and sends the music data to the device. The device plays the music in real time, allowing the user to continue eating at an appropriate pace while enjoying the Japanese cuisine atmosphere.

[0872] Prompt Sentence Examples

[0873] "Emotion Engine": Analyzes the user's emotions from facial expressions.

[0874] "Meal Status Monitoring": The camera and microphone automatically detect the dining situation and play appropriate music.

[0875] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0876] Step 1:

[0877] The device captures the eating scene with a camera and collects image data. The camera captures the user's facial expressions and the type of food while eating, and sends the image data to a server in real time. The input is the image data acquired from the camera, and the output is the data sent to the server.

[0878] Step 2:

[0879] The device uses a microphone to collect voice data while eating. The microphone captures the tone of the user's voice and the sounds of tableware moving, and sends the voice data to a server. The input is the voice data acquired from the microphone, and the output is the data sent to the server.

[0880] Step 3:

[0881] The device uses an accelerometer to measure the movement of the user's hands and tableware. The accelerometer detects the speed of eating and sends that data to the server. The input is the movement data obtained from the accelerometer, and the output is the data sent to the server.

[0882] Step 4:

[0883] The server analyzes the received image data and identifies the type of food and the user's facial expression. Image recognition algorithms are used to identify the food classification and the user's emotional state. The input is the image data sent from the device, and the output is the classified food information and the user's emotional data.

[0884] Step 5:

[0885] The server analyzes the voice data and estimates the user's emotional state from the tone of their voice and the sounds of tableware movements. Using a voice analysis algorithm, emotions such as stress or joy are identified. The input is the voice data sent from the device, and the output is the estimated emotional state.

[0886] Step 6:

[0887] The server analyzes the acceleration data and identifies the speed at which the user is eating. Based on the data from the acceleration sensor, it determines how fast the user is eating. The input is the acceleration data sent from the device, and the output is the speed at which the user is eating.

[0888] Step 7:

[0889] The server determines the music genre and tempo based on the analyzed data. It selects the optimal music taking into account the type of ingredients, eating speed, and the user's emotional state. The input is food information, emotional data, and eating speed information, and the output is optimized music information.

[0890] Step 8:

[0891] The server generates music in real time based on the determined music information. Using a generative AI model, music data is created in real time and sent to the device. The input is optimized music information, and the output is generated music data.

[0892] Step 9:

[0893] The device stores the received music data in a buffer and plays the music in real time. Users can listen to appropriate music while eating, naturally adjusting the pace of their meal and enjoying a relaxed meal. The input is the music data sent from the server, and the output is the music being played.

[0894] Step 10:

[0895] The device continuously collects data and sends it to the server. By repeating this cycle, the music is adjusted appropriately according to the user's state. Input is data from the camera, microphone, and accelerometer, and output is data sent to the server.

[0896] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0897] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0898] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0899] [Third embodiment]

[0900] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0901] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0902] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0903] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0904] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0905] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0906] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0907] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0908] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0909] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0910] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0911] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0912] The present invention aims to provide appropriate music during meals, thereby adjusting the pace of the meal and creating a relaxed atmosphere. The specific configuration and operation of the system will be described below.

[0913] System configuration

[0914] The system consists of three main parts:

[0915] 1. Terminal: A device containing sensors (camera, microphone, accelerometer) that is installed on the dining table.

[0916] 2. Server: A central processing unit that analyzes data from the terminals and generates appropriate music data.

[0917] 3. User: The person who eats the food.

[0918] Operational Overview

[0919] The system uses devices to collect environmental data during meals, analyzes that data on a server, and generates and plays appropriate music. By repeating this cycle, the pace of meals is naturally adjusted, enriching food culture.

[0920] How it works

[0921] 1. Sensor data collection by the device

[0922] Camera: The device's built-in camera takes pictures of food on the table while you eat, and uses image recognition algorithms to identify the type of food.

[0923] Microphone: The device's microphone collects sounds made while eating, specifically the sounds of chopsticks and forks touching plates and the sounds of food being brought to the mouth.

[0924] Acceleration sensor: The device's built-in acceleration sensor detects the movement of the user's hands and tableware, and measures the speed at which the user eats.

[0925] 2. Data submission and analysis

[0926] Device: The device sends the sensor data (image data, sound data, acceleration data) collected to the server.

[0927] Server: The server analyzes the data it receives. For example, it identifies the type of food from image data, the type of tableware and the sound of the food being eaten from sound data, and the speed at which the food is eaten from acceleration data.

[0928] 3. Generating music data

[0929] Server: Based on the data analyzed by the server, it determines the appropriate music genre (for example, Italian for pasta) and tempo (according to the eating speed). It then generates music data in real time based on the determined music genre and tempo.

[0930] 4. Playing Music

[0931] Device: The device receives music data from the server, stores it in a buffer, and plays the music in real time. Users can listen to appropriate music while eating, and naturally adjust the pace of their meal.

[0932] Specific examples

[0933] Example 1: When eating pasta

[0934] The user begins to eat pasta. The device's camera detects the pasta and sends the image data to the server. The device's microphone collects the sound of the fork and plate touching each other and sends the sound data to the server. The device's accelerometer also measures the user's hand movements and sends the data to the server.

[0935] The server analyzes the data it receives and determines that the food is pasta and that the person is eating it quickly. The server then generates an Italian melody at a slightly slower tempo and sends the music data to the device. The device then plays the music in real time, allowing the user to naturally adjust the pace of their meal and enjoy a relaxed meal.

[0936] Example 2: Eating sushi

[0937] The user begins to eat sushi. The device's camera detects the sushi and sends the image data to the server. The device's microphone collects the sound of the chopsticks hitting the plate and sends the sound data to the server. The device's accelerometer also measures the user's hand movements and sends the data to the server.

[0938] The server analyzes the received data and determines that the food is sushi and that the user is eating at an appropriate speed. The server then generates a Japanese-style rhythm at the appropriate tempo and sends the music data to the device. The device plays the music in real time, allowing the user to continue eating at an appropriate pace while enjoying the Japanese cuisine.

[0939] This invention allows users to naturally adjust the pace of their meals, enriching food culture, and is expected to promote healthy eating habits and increase the enjoyment of meals.

[0940] The processing flow will be explained below.

[0941] Step 1:

[0942] The device activates the camera, microphone, and accelerometer installed on the table.

[0943] Step 2:

[0944] The device uses a camera to take pictures of food on the table and collect image data in real time.

[0945] Step 3:

[0946] The device uses image recognition algorithms to identify the type of food from the collected image data.

[0947] Step 4:

[0948] The device uses a microphone to collect sounds such as dishes clashing and other sounds made while eating.

[0949] Step 5:

[0950] The device uses an accelerometer to detect the movement of the user's hands and tableware and measure the speed at which they eat.

[0951] Step 6:

[0952] The image data, sound data, and acceleration data collected by the terminal are combined into a single data packet and transmitted to the server.

[0953] Step 7:

[0954] The server receives the data packets and analyzes the image data to determine the type of food.

[0955] Step 8:

[0956] The server analyzes the sound data and identifies the type of tableware and the sound of its operation.

[0957] Step 9:

[0958] The server analyzes the acceleration data and calculates the user's eating speed.

[0959] Step 10:

[0960] Based on the analysis results, the server determines the music genre according to the type of food and the tempo according to the eating speed.

[0961] Step 11:

[0962] The server uses a music generation algorithm to generate music data in real time according to the determined genre and tempo.

[0963] Step 12:

[0964] The server transmits the generated music data to the terminal.

[0965] Step 13:

[0966] The terminal stores the music data received from the server in a buffer and plays it in real time.

[0967] Step 14:

[0968] The user continues eating while listening to the played music and paces the meal.

[0969] Step 15:

[0970] The device continuously collects sensor data and periodically transmits the data to the server.

[0971] Step 16:

[0972] The server analyzes the new data it receives and readjusts the tempo and genre of the music as needed.

[0973] Through this series of steps, the system can provide appropriate music while the user is eating, naturally pacing the meal and creating a relaxed atmosphere.

[0974] Example 1

[0975] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0976] In traditional dining environments, there is a lack of ways to adjust the pace and atmosphere of the meal, making it difficult to promote healthy eating habits and improve the enjoyment of meals. While there are systems that provide music, there are no systems that dynamically generate music in real time according to the food and pace of the meal. This makes it difficult to maximize the enjoyment and relaxation of the meal.

[0977] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0978] In this invention, the server includes means for detecting the type of food, eating speed, and sound using sensors, means for transmitting the detected data to a cloud computing device, means for analyzing the detected data in the cloud computing device, means for determining a music genre according to the type of food and a tempo according to the eating speed based on the analysis results, means for generating the determined music data in real time using a generative AI model, means for transmitting the generated music data to a terminal, and means for playing the generated music data in real time. This makes it possible to dynamically adjust the environment during the meal and optimize the pace and atmosphere of the user's meal.

[0979] "Sensors" are devices for detecting food type, eating speed, and sound.

[0980] A "cloud computing device" is an external computer system provided over the Internet for performing data analysis and computational processing.

[0981] A "terminal" is a user-side device that contains sensors, collects and transmits data, and plays music.

[0982] "Image recognition" is a technology that analyzes image data captured by a camera to identify specific objects or types of food.

[0983] An "accelerometer" is a device that detects the user's movements and the movement of tableware, and is used to measure eating speed.

[0984] A "generative AI model" is an algorithm or program that uses artificial intelligence to generate music data in real time.

[0985] A "prompt sentence" is text data input into a generative AI model, and is a sentence that specifies the processing content and conditions to be performed by the generative AI model.

[0986] "Real-time" means that the processing or reaction occurs almost instantaneously or in a very short time.

[0987] MODE FOR CARRYING OUT THE INVENTION

[0988] This system aims to adjust the pace of a meal and create a relaxing atmosphere by providing appropriate music during the meal. This system is mainly composed of three elements: a terminal, a server, and a user.

[0989] Hardware and software used

[0990] Hardware:

[0991] Terminal: A device that includes sensors and is installed on the dining table. For example, a Raspberry Pi with its camera module, microphone module, and accelerometer module can be used.

[0992] Server: A high-performance central processing unit. Uses cloud services (e.g., AWS or Google Cloud).

[0993] software:

[0994] Terminal software: A custom Python script running on the Raspberry Pi, used to collect data from the sensors and send it to the server.

[0995] Server-side software: Runs on the cloud and performs data analysis and music generation. For example, data analysis algorithms written in Python and generative AI models for music generation (e.g., OpenAI GPT-3 and Jukedeck).

[0996] System Operation

[0997] This system uses devices to collect environmental data during meals, analyzes that data on a server, and generates and plays appropriate music. By repeating this cycle, the pace of meals is naturally adjusted, enriching food culture.

[0998] Specific operation example

[0999] Example 1: When eating pasta

[1000] 1. The device detects pasta on the table using a camera, analyzes the image data using OpenCV, and recognizes that it is pasta.

[1001] 2. The device's microphone collects the sound of the fork and plate touching each other, and Librosa is used to analyze the sound data.

[1002] 3. The device's accelerometer measures the user's hand movements, and a Python script analyzes the eating speed.

[1003] 4. The device sends this data to the server via an HTTP request.

[1004] 5. The server recognizes the pasta from the image data and confirms from the sound and acceleration data that the user is eating quickly.

[1005] 6. The server generates music with a slightly slower tempo and Italian melody using a generative AI model.

[1006] Example prompt:

[1007] "Recognize pasta from image data, and if the user is eating quickly from sound and acceleration data, generate music with a slightly slower tempo using an Italian melody."

[1008] 7. The server sends the generated music data to the device.

[1009] 8. The device plays music data in real time, allowing the user to relax and enjoy their meal.

[1010] Example 2: Eating sushi

[1011] 1. The device detects sushi on the table using a camera, analyzes the image data using OpenCV, and recognizes that it is sushi.

[1012] 2. The device's microphone collects the sound of the chopsticks and plate touching each other, and the sound data is analyzed using Librosa.

[1013] 3. The device's accelerometer measures the user's hand movements, and a Python script analyzes the eating speed.

[1014] 4. The device sends this data to the server via an HTTP request.

[1015] 5. The server recognizes the sushi from the image data and confirms that it is being eaten at the appropriate speed from the sound and acceleration data.

[1016] 6. The server generates music with an appropriate tempo and Japanese rhythm using an AI model.

[1017] Example prompt:

[1018] "Recognize sushi from image data, and generate Japanese-style rhythmic music if the user is eating at the appropriate speed based on sound and acceleration data."

[1019] 7. The server sends the generated music data to the device.

[1020] 8. The device plays music data in real time, allowing the user to enjoy their meal at a comfortable pace.

[1021] conclusion

[1022] This invention allows users to naturally adjust the pace of their meals, increasing the enjoyment of meals and is expected to promote healthy eating habits.

[1023] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1024] Step 1:

[1025] Sensor data is acquired by the device.

[1026] Specific behavior:

[1027] The camera takes a picture of the food on the table at a rate of one per second and collects image data.

[1028] A microphone continuously records the acoustic environment at the table, collecting the sounds of tableware and food as digital signals.

[1029] The acceleration sensor measures the movement of the user's hands and tableware in real time and collects acceleration data.

[1030] Input: Image data, sound data, and acceleration data from sensors installed on the dining table.

[1031] Output: Collected raw data (image data, sound data, acceleration data).

[1032] Step 2:

[1033] The device sends the collected sensor data to the server.

[1034] Specific behavior:

[1035] The device sends this data to the server as an HTTP request at regular intervals, for example, every 10 seconds.

[1036] Input: Raw data collected by the device (image data, sound data, acceleration data).

[1037] Output: The raw data sent to the server.

[1038] Step 3:

[1039] The server parses the data it receives.

[1040] Specific behavior:

[1041] The server analyzes the image data using computer vision algorithms (e.g., OpenCV or TensorFlow) to identify the type of food.

[1042] The server analyzes the sound data using an acoustic analysis library (e.g., Librosa) to identify the type of tableware and the sound of its operation.

[1043] The server analyzes the acceleration data and uses Python's NumPy library to measure the user's eating pace.

[1044] Input: Raw data (image data, sound data, acceleration data) sent to the server.

[1045] Output: Analyzed results (type of food, sound of dishes, eating pace).

[1046] Step 4:

[1047] The server generates music data based on the analysis results.

[1048] Specific behavior:

[1049] The server then uses the analysis results to determine the appropriate music genre and tempo. For example, if the food is pasta and the eating pace is fast, a slower Italian melody will be selected.

[1050] The server inputs prompt sentences into a generative AI model (e.g., OpenAI's GPT-3 or Jukedeck) and generates music data in real time.

[1051] Example prompt:

[1052] "Recognize pasta from image data, and if the user is eating quickly from sound and acceleration data, generate music with a slightly slower tempo using an Italian melody."

[1053] Input: Analyzed results (food type, dish sounds, eating pace).

[1054] Output: The generated music data.

[1055] Step 5:

[1056] The server transmits the generated music data to the terminal.

[1057] Specific behavior:

[1058] The music data generated by the server is sent to the terminal as an HTTP response.

[1059] Input: Generated music data.

[1060] Output: The music data sent to the device.

[1061] Step 6:

[1062] The device plays the music data in real time.

[1063] Specific behavior:

[1064] The music data received by the device is stored in a buffer and played in real time using Python's Pygame library, etc.

[1065] Users can relax and enjoy their meal while listening to music.

[1066] Input: Music data sent to the device.

[1067] Output: The played music.

[1068] (Application example 1)

[1069] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1070] In today's virtual stores and restaurants, unlike physical environments, users' behavior is left unmonitored while they eat or shop, making it difficult to adjust the pace of the meal or create a relaxing atmosphere. Furthermore, there is a lack of systems that can enhance the enjoyment of meals by providing appropriate music and promote healthy eating habits. Furthermore, the technology to dynamically adjust music for individual users is underdeveloped.

[1071] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1072] In this invention, the server includes means for detecting a user's behavior on the electronic device, the type of food, the eating speed, and sounds using sensors, means for analyzing the detected data and determining a music genre according to the type of food and a tempo according to the eating speed, means for generating the determined music data and playing it in real time, and means for dynamically changing the music data using a generative AI model to adjust the user's eating pace, thereby providing a relaxed atmosphere during the user's meal, allowing the user to naturally adjust the eating pace, and enriching the experience in the virtual store or virtual restaurant.

[1073] definition statement

[1074] "Sensor" is a general term for electronic devices that detect user behavior and environmental data in real time.

[1075] "Electronic device" refers to any electronic device that can connect to the internet and that a user uses while eating or shopping. Examples include smartphones, tablets, and personal computers.

[1076] "User behavior" refers to the actions and movements of users when eating or shopping, including hand movements, use of tableware, etc.

[1077] "Food type" refers to the specific type of food or dish a user is eating, such as pasta or sushi.

[1078] "Eating speed" refers to the speed at which a user brings food to their mouth when eating.

[1079] "Sound" refers to the sounds that occur while eating, including, for example, the sound of chewing food and the sound of dishes clashing.

[1080] "Means" refers to the methods or devices used to achieve a particular purpose.

[1081] "Analyzing data" refers to performing calculations and processing to extract information from collected data and find patterns and trends.

[1082] "Music genre" refers to a type or style of music, such as classical, jazz, or pop.

[1083] "Tempo" refers to the speed and rhythm of music or movement.

[1084] "Generating music data" refers to creating new music based on the analysis results.

[1085] "Playing in real time" means playing the data immediately after receiving it.

[1086] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to generate and analyze data.

[1087] "Dynamically changing music data" refers to changing the content of the music being played in real time based on new data or conditions along the way.

[1088] MODE FOR CARRYING OUT THE INVENTION

[1089] This invention is a system that monitors the user's behavior and eating pace in a virtual store or restaurant and provides appropriate music. This system uses sensors to collect user behavior data in real time and dynamically generates and changes music data based on the analysis results, enriching the user's dining experience.

[1090] System configuration and operation overview

[1091] The system has the following components:

[1092] 1. Device: An electronic device equipped with sensors such as a camera, microphone, and accelerometer. This device collects information on user behavior, eating speed, and environmental sounds in real time.

[1093] 2. Server: A central processing unit that analyzes data sent from the device and determines the music genre and tempo according to the type and speed of eating, and dynamically changes the music data using a generative AI model.

[1094] 3. User: A person who dine in a virtual environment. They listen to music from their device and enjoy a meal in a relaxed atmosphere.

[1095] How it works

[1096] Device data collection:

[1097] It uses a camera to capture footage of the user while they eat and analyzes the type of food they eat and the pace at which they eat.

[1098] It uses a microphone to collect sounds made by the user while eating, such as the clinking of dishes and chewing.

[1099] It uses an accelerometer to measure the movement of the user's hands and utensils to determine the speed at which they are eating.

[1100] Data analysis and music data generation:

[1101] The data collected by the device is sent to a server, which analyzes it and identifies the type of food and the speed at which it is eaten.

[1102] Based on the analyzed data, the server determines the appropriate music genre (for example, Italian music for Italian cuisine, Japanese music for Japanese cuisine) and tempo.

[1103] Furthermore, a generative AI model is used to dynamically generate and change music data based on the analysis results, providing a music environment that is optimal for the user's eating pace.

[1104] Real-time music playback:

[1105] The generated music data is sent to the device and played in real time, allowing users to naturally adjust the pace of their meal and enjoy it in a relaxed atmosphere.

[1106] Specific Examples

[1107] Example 1: Eating pasta at a virtual restaurant:

[1108] If a user is eating pasta in a virtual restaurant, the camera will recognize the pasta and find that they are eating quickly. The server will then analyze this data and generate Italian music at a slower tempo, encouraging the user to slow down and relax.

[1109] Example 2: Drinking coffee at a virtual cafe:

[1110] If a user is relaxing in a virtual cafe with a cup of coffee, the camera will recognize the coffee cup and detect a lack of movement. The server will then analyze this data and select acoustic music to create a more relaxing environment.

[1111] Prompt Sentence Examples

[1112] Below are some examples of specific prompts for implementing patents:

[1113] When a user eats pasta at a virtual restaurant, a camera and microphone capture their behavior and send the data to a server. The server analyzes the speed at which the user is eating and plays appropriate Italian music at a slightly slower tempo. The system uses Python's OpenCV, sounddevice, NumPy, and asyncio libraries.

[1114] This invention will improve the user experience in the virtual store, allowing them to enjoy their food and eat at a healthy pace.

[1115] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1116] Program processing steps

[1117] Step 1:

[1118] The device uses a camera, microphone, and accelerometer to collect real-time data on the user's behavior, ambient sounds, food type, and eating speed. During this data collection process, the camera captures images of the table and uses a recognition algorithm to identify the type of food (e.g., pasta, sushi, etc.). In addition, the microphone collects ambient sounds (e.g., the sound of dishes clattering, chewing, etc.), and the accelerometer measures the user's eating speed from the movement of their hands and dishes.

[1119] Input: Camera video data, audio data, acceleration data

[1120] Output: User behavior data (type of food, eating speed, environmental sounds)

[1121] Step 2:

[1122] The device sends the collected data to the server. In the data transmission process, the device uploads image data of the type of food recognized by the camera, audio data collected by the microphone, and eating speed data obtained by the accelerometer to the server.

[1123] Input: User behavior data (type of food, eating speed, environmental sounds)

[1124] Output: Raw data sent to the server

[1125] Step 3:

[1126] The server analyzes the received data to identify the type of food, eating speed, and environmental sounds. During the analysis process, an image recognition algorithm (using OpenCV) is used to identify the type of food, an audio analysis algorithm is used to classify the collected environmental sounds, and the speed of eating is calculated based on the acceleration data. This allows for specific analysis results to be obtained based on the characteristics of each data.

[1127] Input: Raw data sent to the server

[1128] Output: Analyzed data (food type, eating speed, environmental sound classification)

[1129] Step 4:

[1130] The server determines the appropriate music genre and tempo based on the analysis results. In the process of dynamically generating or changing music data using a generative AI model according to the analysis data, for example, if you eat quickly, it will select relaxing music with a slow tempo, and it will determine the appropriate music genre depending on the type of food (e.g., Italian music for pasta).

[1131] Input: Analyzed data (food type, eating speed, environmental sound classification)

[1132] Output: Determined music genre and tempo

[1133] Step 5:

[1134] The server sends the generated music data to the device, which then plays the music in real time. During the music playback process, the music data dynamically modified by the generative AI model is sent to the device, which stores the music in a buffer and then plays it through the speaker.

[1135] Input: Determined music genre and tempo

[1136] Output: Music data to be played

[1137] Step 6:

[1138] The user continues eating while listening to appropriate music. As a result, the user can naturally adjust the pace of their meal and enjoy it in a relaxed atmosphere. The device again monitors the user's reactions and behavior, and the entire system operates in a feedback loop.

[1139] Input: Music data to be played

[1140] Output: Relaxed eating environment, naturally adjusted eating pace

[1141] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1142] The present invention aims to provide appropriate music during meals to adjust the pace of the meal and create a relaxing atmosphere, and by combining it with an emotion engine that recognizes the user's emotions, it provides a more personalized music experience.

[1143] System configuration

[1144] The system consists of four main parts:

[1145] 1. Terminal: A device installed on the dining table that includes sensors (camera, microphone, accelerometer) and an emotion engine.

[1146] 2. Server: A central processing unit that analyzes data from the terminals and generates appropriate music data.

[1147] 3. Emotion engine: Recognizes the user's emotions and sends the data to the server.

[1148] 4. User: The person who eats the food.

[1149] Operational Overview

[1150] The system uses the device to collect environmental data during the meal, analyzes that data on the server, and takes into account data from the emotion engine to generate and play appropriate music. By repeating this cycle, the pace of the meal is naturally adjusted, enriching food culture.

[1151] How it works

[1152] 1. Sensor data collection by the device

[1153] Camera: The device's built-in camera takes pictures of the food on the table while the user is eating. Image recognition algorithms are used to identify the type of food. The device also analyzes the user's facial expressions and sends them to the emotion engine as emotional data.

[1154] Microphone: The device's microphone collects sounds made while eating, such as the sound of chopsticks and forks touching tableware and the tone of the user's voice, and sends these to the emotion engine.

[1155] Acceleration sensor: The device's built-in accelerometer detects the movement of the user's hands and tableware and measures the speed at which they eat.

[1156] 2. Data submission and analysis

[1157] Device: The device sends the image data, sound data, acceleration data collected, and emotion data analyzed by the emotion engine to the server.

[1158] Server: The server analyzes the data it receives. For example, it identifies the type of food from image data, the type of tableware and the sound of the movements from sound data, the speed of eating from acceleration data, and the user's current emotional state from emotion data.

[1159] 3. Generating music data

[1160] Server: Based on the data analyzed by the server, the music genre is determined according to the type of food and the tempo according to the eating speed, and the music is selected according to the user's emotional state. For example, if the user is feeling stressed, it will generate relaxing music, and if the user is in a happy mood, it will generate cheerful music.

[1161] Server: Generates music data in real time based on the determined music genre, tempo, and emotion.

[1162] 4. Playing Music

[1163] Device: The device stores the music data received from the server in a buffer and plays the music in real time. Users can listen to music that matches their emotions while eating, allowing them to naturally adjust the pace of their meal and enjoy a relaxed meal.

[1164] Specific examples

[1165] Example 1: When eating pasta

[1166] The user begins to eat pasta. The device's camera detects the pasta and sends the image data to the server. The device's microphone collects the sound of the fork and plate touching each other and sends the sound data to the server. The device's accelerometer measures the user's hand movements and sends the data to the server. The emotion engine then analyzes the user's facial expressions, determines that they are feeling stressed, and sends the emotion data to the server.

[1167] The server analyzes the data it receives, recognizes that the food is pasta and that the user is eating it quickly, and generates a relaxing Italian melody at a slow tempo because the user is feeling stressed. The music data is then sent to the device, which plays the music in real time, allowing the user to naturally adjust the pace of their meal and enjoy a relaxed meal.

[1168] Example 2: Eating sushi

[1169] The user begins to eat sushi. The device's camera detects the sushi and sends the image data to the server. The device's microphone collects the sound of the chopsticks hitting the plate and sends the sound data to the server. The device's accelerometer measures the user's hand movements and sends the data to the server. The emotion engine then analyzes the user's tone of voice, determines that they are enjoying the food, and sends the emotion data to the server.

[1170] The server analyzes the data it receives, recognizes that the food is sushi and that the user is eating at an appropriate pace, and because the user is enjoying themselves, generates a cheerful Japanese-style rhythm at the same tempo and sends the music data to the device. The device plays the music in real time, allowing the user to continue eating at an appropriate pace while enjoying the Japanese food atmosphere.

[1171] This invention allows users to naturally adjust the pace of their meals and enrich their food culture. It is also expected to promote healthy eating habits and increase the enjoyment of meals. Furthermore, by utilizing the emotion engine, it is possible to provide a personalized music experience that corresponds to the user's emotional state.

[1172] The processing flow will be explained below.

[1173] Step 1:

[1174] The device activates the camera, microphone, accelerometer, and emotion engine installed on the table.

[1175] Step 2:

[1176] The device uses a camera to capture images of food on the table and collects image data in real time, while the emotion engine simultaneously collects facial expression data from the user.

[1177] Step 3:

[1178] The device uses image recognition algorithms to identify the type of food from the collected image data, while an emotion engine analyzes facial expression data to identify the user's emotional state.

[1179] Step 4:

[1180] The device uses a microphone to collect sounds such as the clinking of dishes and other sounds made while eating, and the emotion engine also collects tone data from the user's voice.

[1181] Step 5:

[1182] The device uses an accelerometer to detect the movement of the user's hands and tableware and measure the speed at which they eat.

[1183] Step 6:

[1184] The image data, sound data, acceleration data collected by the terminal, and emotion data analyzed by the emotion engine are compiled into a single data packet and sent to the server.

[1185] Step 7:

[1186] The server receives the data packets and analyzes the image data to determine the type of food.

[1187] Step 8:

[1188] The server analyzes the sound data and identifies the type of tableware and the sound of its operation.

[1189] Step 9:

[1190] The server analyzes the acceleration data and calculates the user's eating speed.

[1191] Step 10:

[1192] The server analyzes the emotional data and recognizes the user's current emotional state.

[1193] Step 11:

[1194] Based on the analysis results, the server determines the music genre according to the type of food, the tempo according to the eating speed, and the characteristics of the music according to the emotional state.

[1195] Step 12:

[1196] The server uses a music generation algorithm to generate music data in real time according to the determined genre, tempo, and emotion.

[1197] Step 13:

[1198] The server transmits the generated music data to the terminal.

[1199] Step 14:

[1200] The terminal stores the music data received from the server in a buffer and plays it in real time.

[1201] Step 15:

[1202] The user continues eating while listening to the played music, adjusting the pace of the meal and emotional state.

[1203] Step 16:

[1204] The device continuously collects sensor data and emotion data and periodically transmits the data to a server.

[1205] Step 17:

[1206] The server analyzes the new data it receives and readjusts the tempo, genre and emotional characteristics of the music as needed.

[1207] Through this series of steps, the system can provide appropriate and personalized music while the user is eating, naturally pacing the meal and creating a relaxing atmosphere.

[1208] Example 2

[1209] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1210] Providing appropriate music during meals, naturally adjusting the pace of eating, and creating a relaxing atmosphere are challenges many people desire. However, existing systems have difficulty selecting music that reflects the user's eating situation and emotional state, and are limited to playing generic music. Furthermore, there are also limited systems that can reflect the type of food and eating speed in real time. Therefore, the present invention aims to solve these challenges.

[1211] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for detecting the type of food, eating speed, and sound using sensors, means for analyzing the detected data and selecting music according to the music genre corresponding to the type of food, the tempo corresponding to the eating speed, and the emotional state of the user, and means for generating the selected music data and playing it in real time. This makes it possible to provide a more personalized music experience based on environmental data during the meal and emotional data of the user.

[1212] text

[1213] A "sensor" is a device that detects physical changes and the surrounding environment and outputs that information as a signal.

[1214] "Food type" refers to the specific type of food consumed during a meal, primarily identified through image recognition.

[1215] "Eating speed" indicates the speed at which the user eats, and is measured using an acceleration sensor.

[1216] "Sound" refers to the sounds and noises generated during a meal and collected through a microphone.

[1217] "Detection" is the act of sensing specific information or data using sensors or other devices.

[1218] "Analysis" is the act of analyzing collected data to find meaning and patterns.

[1219] "Musical genre" is a term used to describe a category of music with a particular characteristic or style.

[1220] "Tempo" refers to the speed or rhythmic frequency of music.

[1221] "User's emotional state" refers to the emotions and moods the user is feeling at that time, as analyzed through the emotion engine.

[1222] "Selection" is the act of choosing the most suitable candidate from among multiple candidates.

[1223] "Generation" is the act of creating new content or results based on necessary data and information.

[1224] "Real-time" means that data is processed and output the instant it is collected.

[1225] "Music data" refers to digital data stored in a format that can be played as music.

[1226] text

[1227] The purpose of this invention is to adjust the pace of a meal and create a relaxing atmosphere by providing appropriate music during the meal. To achieve this, we will build a system that combines sensors and an emotion engine to collect user data, and a server analyzes the data and generates music. Specifically, the following components are required:

[1228] First, the device is equipped with a built-in camera, microphone, and accelerometer. These sensors collect environmental data while eating. The camera has high-resolution image capture capabilities and can identify the type of food and the user's facial expressions. The microphone collects audio, capturing the sound of dishes clashing and the tone of the user's voice. The accelerometer detects the movement of the user's hands and dishes and measures the speed of eating.

[1229] As a specific example, consider the case where a user is eating pasta. When the user starts to eat the pasta, the device's camera detects the pasta and sends the image data to the server. The microphone collects the sound of the fork and plate touching each other, and the accelerometer measures the user's hand movements. The emotion engine analyzes the user's facial expression and determines that they are feeling stressed.

[1230] The server receives and analyzes this data. Using a high-performance GPU, the server runs an image recognition algorithm to identify the food as pasta. It also measures the eating speed and recognizes that the user is eating quickly. Based on the data obtained from the emotion engine, it selects an Italian melody that will relax the user, who is feeling stressed, and generates that music data.

[1231] The generated music data is sent from the server to the device, which then stores it in a buffer and plays it in real time, allowing the user to naturally adjust the pace of their meal and enjoy a relaxed meal.

[1232] An example of a prompt sentence is, "Please explain the data processing flow of a system that automatically selects and plays appropriate music for a user eating pasta."

[1233] As described above, this system utilizes sensor data and emotional data to provide appropriate music to users, improving their dining experience.

[1234] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1235] text

[1236] Step 1:

[1237] (sensor data collection)

[1238] The device uses a camera, microphone, and accelerometer to collect environmental data while eating. The camera takes pictures of the meal and detects the type of food and the user's facial expression. The microphone collects the sound of dishes touching each other and the user's voice. The accelerometer detects the movement of the user's hands and the movement of dishes.

[1239] Input: Camera video, audio signal, acceleration data

[1240] Output: image data, audio data, acceleration data

[1241] Specific behavior:

[1242] The device takes a photo of the table every second while the user is eating and processes the images in real time. For example, it analyzes video of a user eating pasta to identify the type of pasta and the user's facial expression. At the same time, the microphone collects the sounds of dishes clashing and the tone of conversation, and the accelerometer captures data on the movement of the dishes.

[1243] Step 2:

[1244] (Data transmission)

[1245] The device sends the collected image data, voice data, acceleration data, and emotion data analyzed by the emotion engine to the server.

[1246] Input: image data, audio data, acceleration data, emotion data

[1247] Output: Compressed data packets

[1248] Specific behavior:

[1249] All data collected every second is compressed, packetized, and sent to a server over the Internet. Data is sent in a prioritized order to minimize data latency.

[1250] Step 3:

[1251] (Data Analysis)

[1252] The server analyzes the data sent from the device. It identifies the type of food and the user's facial expression from the image data, analyzes the sound of dishes clashing and the tone of the user's voice from the audio data, measures the eating speed from the acceleration data, and identifies the user's emotional state from the emotion data.

[1253] Input: Compressed data packet

[1254] Output: food type, eating rate, emotional state

[1255] Specific behavior:

[1256] The server uses a high-performance GPU to execute image recognition algorithms, such as detecting pasta from the received image data and identifying its type, while simultaneously measuring eating speed and analyzing emotional data from the sound of dishes clashing and the tone of the user's voice.

[1257] Step 4:

[1258] (Music data generation)

[1259] Based on the analysis results, the server determines the appropriate music genre and tempo, and generates music that matches the user's emotional state.

[1260] Input: food type, eating rate, emotional state

[1261] Output: Music data

[1262] Specific behavior:

[1263] The system determines the appropriate music genre and tempo based on the analysis results. For example, if it determines that a user is eating pasta quickly and feeling stressed, it will select a relaxing Italian melody and generate music data with a slow tempo.

[1264] Step 5:

[1265] (Music playing)

[1266] The device stores the music data received from the server in a buffer and plays the music in real time.

[1267] Input: Music data

[1268] Output: Playback music

[1269] Specific behavior:

[1270] When the device receives music data from the server, it immediately launches the music player and starts playing. This allows users to listen to music that matches their mood while eating. For example, if you are enjoying a relaxing meal of pasta, you can play a relaxing Italian melody.

[1271] (Application example 2)

[1272] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1273] Conventional dining environments lacked mechanisms to improve the user's dining experience, making it particularly difficult to respond in real time to the speed of eating and the user's emotions. Furthermore, the relaxing effect of personalized music was not fully realized. This resulted in a decline in meal quality and satisfaction, making it difficult to promote healthy eating habits.

[1274] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for detecting the type of food, eating speed, and sound using sensors, means for analyzing the detected data and determining a music genre according to the type of food and a tempo according to the eating speed, means for analyzing the user's emotions and personalizing music based on the analysis results, and means for generating the determined music data and playing it in real time. This makes it possible to provide personalized music in real time based on the captured data, improving the user's dining experience.

[1275] A "sensor" is a device that detects the environment or a specific situation and collects that information as electronic data.

[1276] "Food type" is a category for classifying specific foods in a meal, and is identified using image recognition or other methods.

[1277] "Eating speed" refers to the speed and rhythm of movements while eating, and is measured using an acceleration sensor or the like.

[1278] "Sound" refers to various acoustic information generated during a meal, which is collected using a microphone.

[1279] "Analysis" is the process of extracting and analyzing meaningful information based on data collected using sensors, etc.

[1280] A "music genre" is a category for classifying music, which is classified based on certain characteristics.

[1281] "Tempo" refers to the speed or beat of music and is the element that determines the speed of the rhythm.

[1282] "User emotion" refers to the emotional state determined from a person's facial expressions and voice as analyzed by the emotion engine.

[1283] "Personalization" means providing content that is optimized to suit the preferences and circumstances of each individual user.

[1284] "Real-time" means that processing and operations are carried out at the same speed as real time, and that they are reflected immediately without delay.

[1285] A "system" is a set of devices or programs that combine multiple components and functions and work together to achieve a specific purpose.

[1286] This invention is a system that uses sensors and an emotion engine to provide appropriate music in real time to improve the user's dining experience. The main hardware used is a terminal that includes a camera, microphone, acceleration sensor, and emotion engine, and a server that analyzes data and generates music.

[1287] System configuration

[1288] Terminal

[1289] Camera: Collects data to analyze the user's facial expressions and the type of food they eat.

[1290] Microphone: Collects sounds while eating, capturing the user's voice, the sound of dishes, etc.

[1291] Accelerometer: Measures the movement of the user's hands and the movement of the tableware to detect the speed at which the meal is being eaten.

[1292] Emotion engine: Analyzes the user's emotions based on data obtained from the camera and microphone.

[1293] server

[1294] Data analysis engine: Analyzes various data sent from the device and extracts food type, eating speed, and user emotions.

[1295] Music generation engine: Based on information from the data analysis engine, it determines the optimal music genre and tempo and generates music.

[1296] Real-time playback engine: The generated music is sent to the device in real time and provided to the user.

[1297] How it works

[1298] Camera: The camera captures the user's eating scene in real time and sends the image data to a server. Image recognition technology is used to analyze the type of food and the user's facial expression.

[1299] Microphone: The microphone collects sounds while you eat, capturing the tone of your voice and the sound of your tableware. The collected voice data is sent to a server and analyzed by the emotion engine.

[1300] Accelerometer: The accelerometer measures the movement of the user's hands and tableware to detect the speed at which the food is being eaten. This data is also sent to the server for analysis.

[1301] Emotion Engine: The emotion engine uses data from the camera and microphone to analyze the user's current emotional state. For example, if the user is feeling stressed, it can suggest relaxing music.

[1302] Server: The server receives and analyzes various data sent from the device. It determines the music genre according to the type of food, the tempo according to the eating speed, and also generates the optimal music based on the user's emotions.

[1303] Real-time playback: Music data generated on the server is sent to the device and played in real time, allowing users to naturally adjust the speed of their meal and enjoy a relaxed meal.

[1304] Specific examples

[1305] Example 1: When eating pasta

[1306] The user begins to eat pasta. The device's camera detects the pasta and sends the image data to the server. The device's microphone collects the sound of the fork and plate hitting each other and sends the sound data to the server. The device's accelerometer also measures the user's hand movements and sends the data to the server. The emotion engine then analyzes the user's facial expression, determines that the user is feeling stressed, and sends the emotion data to the server. The server analyzes the received data, recognizes that the pasta is being eaten quickly, and because the user is feeling stressed, generates relaxing music and sends the music data to the device. The device plays the music in real time, allowing the user to naturally adjust the pace of their meal and enjoy a relaxed meal.

[1307] Example 2: Eating sushi

[1308] The user begins to eat sushi. The device's camera detects the sushi and sends the image data to the server. The device's microphone collects the sound of the chopsticks hitting the plate and sends the sound data to the server. The device's accelerometer also measures the user's hand movements and sends the data to the server. The emotion engine then analyzes the user's tone of voice, determines that they are enjoying themselves, and sends the emotion data to the server. The server analyzes the received data, recognizes that the sushi is being eaten at an appropriate speed, generates upbeat music because the user is enjoying themselves, and sends the music data to the device. The device plays the music in real time, allowing the user to continue eating at an appropriate pace while enjoying the Japanese cuisine atmosphere.

[1309] Prompt Sentence Examples

[1310] "Emotion Engine": Analyzes the user's emotions from facial expressions.

[1311] "Meal Status Monitoring": The camera and microphone automatically detect the dining situation and play appropriate music.

[1312] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1313] Step 1:

[1314] The device captures the eating scene with a camera and collects image data. The camera captures the user's facial expressions and the type of food while eating, and sends the image data to a server in real time. The input is the image data acquired from the camera, and the output is the data sent to the server.

[1315] Step 2:

[1316] The device uses a microphone to collect voice data while eating. The microphone captures the tone of the user's voice and the sounds of tableware moving, and sends the voice data to a server. The input is the voice data acquired from the microphone, and the output is the data sent to the server.

[1317] Step 3:

[1318] The device uses an accelerometer to measure the movement of the user's hands and tableware. The accelerometer detects the speed of eating and sends that data to the server. The input is the movement data obtained from the accelerometer, and the output is the data sent to the server.

[1319] Step 4:

[1320] The server analyzes the received image data and identifies the type of food and the user's facial expression. Image recognition algorithms are used to identify the food classification and the user's emotional state. The input is the image data sent from the device, and the output is the classified food information and the user's emotional data.

[1321] Step 5:

[1322] The server analyzes the voice data and estimates the user's emotional state from the tone of their voice and the sounds of tableware movements. Using a voice analysis algorithm, emotions such as stress or joy are identified. The input is the voice data sent from the device, and the output is the estimated emotional state.

[1323] Step 6:

[1324] The server analyzes the acceleration data and identifies the speed at which the user is eating. Based on the data from the acceleration sensor, it determines how fast the user is eating. The input is the acceleration data sent from the device, and the output is the speed at which the user is eating.

[1325] Step 7:

[1326] The server determines the music genre and tempo based on the analyzed data. It selects the optimal music taking into account the type of ingredients, eating speed, and the user's emotional state. The input is food information, emotional data, and eating speed information, and the output is optimized music information.

[1327] Step 8:

[1328] The server generates music in real time based on the determined music information. Using a generative AI model, music data is created in real time and sent to the device. The input is optimized music information, and the output is generated music data.

[1329] Step 9:

[1330] The device stores the received music data in a buffer and plays the music in real time. Users can listen to appropriate music while eating, naturally adjusting the pace of their meal and enjoying a relaxed meal. The input is the music data sent from the server, and the output is the music being played.

[1331] Step 10:

[1332] The device continuously collects data and sends it to the server. By repeating this cycle, the music is adjusted appropriately according to the user's state. Input is data from the camera, microphone, and accelerometer, and output is data sent to the server.

[1333] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1334] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1335] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1336] [Fourth embodiment]

[1337] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1338] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1339] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1340] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1341] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1342] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1343] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1344] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1345] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1346] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1347] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1348] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1349] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1350] The present invention aims to provide appropriate music during meals, thereby adjusting the pace of the meal and creating a relaxed atmosphere. The specific configuration and operation of the system will be described below.

[1351] System configuration

[1352] The system consists of three main parts:

[1353] 1. Terminal: A device containing sensors (camera, microphone, accelerometer) that is installed on the dining table.

[1354] 2. Server: A central processing unit that analyzes data from the terminals and generates appropriate music data.

[1355] 3. User: The person who eats the food.

[1356] Operational Overview

[1357] The system uses devices to collect environmental data during meals, analyzes that data on a server, and generates and plays appropriate music. By repeating this cycle, the pace of meals is naturally adjusted, enriching food culture.

[1358] How it works

[1359] 1. Sensor data collection by the device

[1360] Camera: The device's built-in camera takes pictures of food on the table while you eat, and uses image recognition algorithms to identify the type of food.

[1361] Microphone: The device's microphone collects sounds made while eating, specifically the sounds of chopsticks and forks touching plates and the sounds of food being brought to the mouth.

[1362] Acceleration sensor: The device's built-in acceleration sensor detects the movement of the user's hands and tableware, and measures the speed at which the user eats.

[1363] 2. Data submission and analysis

[1364] Device: The device sends the sensor data (image data, sound data, acceleration data) collected to the server.

[1365] Server: The server analyzes the data it receives. For example, it identifies the type of food from image data, the type of tableware and the sound of the food being eaten from sound data, and the speed at which the food is eaten from acceleration data.

[1366] 3. Generating music data

[1367] Server: Based on the data analyzed by the server, it determines the appropriate music genre (for example, Italian for pasta) and tempo (according to the eating speed). It then generates music data in real time based on the determined music genre and tempo.

[1368] 4. Playing Music

[1369] Device: The device receives music data from the server, stores it in a buffer, and plays the music in real time. Users can listen to appropriate music while eating, and naturally adjust the pace of their meal.

[1370] Specific examples

[1371] Example 1: When eating pasta

[1372] The user begins to eat pasta. The device's camera detects the pasta and sends the image data to the server. The device's microphone collects the sound of the fork and plate touching each other and sends the sound data to the server. The device's accelerometer also measures the user's hand movements and sends the data to the server.

[1373] The server analyzes the data it receives and determines that the food is pasta and that the person is eating it quickly. The server then generates an Italian melody at a slightly slower tempo and sends the music data to the device. The device then plays the music in real time, allowing the user to naturally adjust the pace of their meal and enjoy a relaxed meal.

[1374] Example 2: Eating sushi

[1375] The user begins to eat sushi. The device's camera detects the sushi and sends the image data to the server. The device's microphone collects the sound of the chopsticks hitting the plate and sends the sound data to the server. The device's accelerometer also measures the user's hand movements and sends the data to the server.

[1376] The server analyzes the received data and determines that the food is sushi and that the user is eating at an appropriate speed. The server then generates a Japanese-style rhythm at the appropriate tempo and sends the music data to the device. The device plays the music in real time, allowing the user to continue eating at an appropriate pace while enjoying the Japanese cuisine.

[1377] This invention allows users to naturally adjust the pace of their meals, enriching food culture, and is expected to promote healthy eating habits and increase the enjoyment of meals.

[1378] The processing flow will be explained below.

[1379] Step 1:

[1380] The device activates the camera, microphone, and accelerometer installed on the table.

[1381] Step 2:

[1382] The device uses a camera to take pictures of food on the table and collect image data in real time.

[1383] Step 3:

[1384] The device uses image recognition algorithms to identify the type of food from the collected image data.

[1385] Step 4:

[1386] The device uses a microphone to collect sounds such as dishes clashing and other sounds made while eating.

[1387] Step 5:

[1388] The device uses an accelerometer to detect the movement of the user's hands and tableware and measure the speed at which they eat.

[1389] Step 6:

[1390] The image data, sound data, and acceleration data collected by the terminal are combined into a single data packet and transmitted to the server.

[1391] Step 7:

[1392] The server receives the data packets and analyzes the image data to determine the type of food.

[1393] Step 8:

[1394] The server analyzes the sound data and identifies the type of tableware and the sound of its operation.

[1395] Step 9:

[1396] The server analyzes the acceleration data and calculates the user's eating speed.

[1397] Step 10:

[1398] Based on the analysis results, the server determines the music genre according to the type of food and the tempo according to the eating speed.

[1399] Step 11:

[1400] The server uses a music generation algorithm to generate music data in real time according to the determined genre and tempo.

[1401] Step 12:

[1402] The server transmits the generated music data to the terminal.

[1403] Step 13:

[1404] The terminal stores the music data received from the server in a buffer and plays it in real time.

[1405] Step 14:

[1406] The user continues eating while listening to the played music and paces the meal.

[1407] Step 15:

[1408] The device continuously collects sensor data and periodically transmits the data to the server.

[1409] Step 16:

[1410] The server analyzes the new data it receives and readjusts the tempo and genre of the music as needed.

[1411] Through this series of steps, the system can provide appropriate music while the user is eating, naturally pacing the meal and creating a relaxed atmosphere.

[1412] Example 1

[1413] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1414] In traditional dining environments, there is a lack of ways to adjust the pace and atmosphere of the meal, making it difficult to promote healthy eating habits and improve the enjoyment of meals. While there are systems that provide music, there are no systems that dynamically generate music in real time according to the food and pace of the meal. This makes it difficult to maximize the enjoyment and relaxation of the meal.

[1415] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1416] In this invention, the server includes means for detecting the type of food, eating speed, and sound using sensors, means for transmitting the detected data to a cloud computing device, means for analyzing the detected data in the cloud computing device, means for determining a music genre according to the type of food and a tempo according to the eating speed based on the analysis results, means for generating the determined music data in real time using a generative AI model, means for transmitting the generated music data to a terminal, and means for playing the generated music data in real time. This makes it possible to dynamically adjust the environment during the meal and optimize the pace and atmosphere of the user's meal.

[1417] "Sensors" are devices for detecting food type, eating speed, and sound.

[1418] A "cloud computing device" is an external computer system provided over the Internet for performing data analysis and computational processing.

[1419] A "terminal" is a user-side device that contains sensors, collects and transmits data, and plays music.

[1420] "Image recognition" is a technology that analyzes image data captured by a camera to identify specific objects or types of food.

[1421] An "accelerometer" is a device that detects the user's movements and the movement of tableware, and is used to measure eating speed.

[1422] A "generative AI model" is an algorithm or program that uses artificial intelligence to generate music data in real time.

[1423] A "prompt sentence" is text data input into a generative AI model, and is a sentence that specifies the processing content and conditions to be performed by the generative AI model.

[1424] "Real-time" means that the processing or reaction occurs almost instantaneously or in a very short time.

[1425] MODE FOR CARRYING OUT THE INVENTION

[1426] This system aims to adjust the pace of a meal and create a relaxing atmosphere by providing appropriate music during the meal. This system is mainly composed of three elements: a terminal, a server, and a user.

[1427] Hardware and software used

[1428] Hardware:

[1429] Terminal: A device that includes sensors and is installed on the dining table. For example, a Raspberry Pi with its camera module, microphone module, and accelerometer module can be used.

[1430] Server: A high-performance central processing unit. Uses cloud services (e.g., AWS or Google Cloud).

[1431] software:

[1432] Terminal software: A custom Python script running on the Raspberry Pi, used to collect data from the sensors and send it to the server.

[1433] Server-side software: Runs on the cloud and performs data analysis and music generation. For example, data analysis algorithms written in Python and generative AI models for music generation (e.g., OpenAI GPT-3 and Jukedeck).

[1434] System Operation

[1435] This system uses devices to collect environmental data during meals, analyzes that data on a server, and generates and plays appropriate music. By repeating this cycle, the pace of meals is naturally adjusted, enriching food culture.

[1436] Specific operation example

[1437] Example 1: When eating pasta

[1438] 1. The device detects pasta on the table using a camera, analyzes the image data using OpenCV, and recognizes that it is pasta.

[1439] 2. The device's microphone collects the sound of the fork and plate touching each other, and Librosa is used to analyze the sound data.

[1440] 3. The device's accelerometer measures the user's hand movements, and a Python script analyzes the eating speed.

[1441] 4. The device sends this data to the server via an HTTP request.

[1442] 5. The server recognizes the pasta from the image data and confirms from the sound and acceleration data that the user is eating quickly.

[1443] 6. The server generates music with a slightly slower tempo and Italian melody using a generative AI model.

[1444] Example prompt:

[1445] "Recognize pasta from image data, and if the user is eating quickly from sound and acceleration data, generate music with a slightly slower tempo using an Italian melody."

[1446] 7. The server sends the generated music data to the device.

[1447] 8. The device plays music data in real time, allowing the user to relax and enjoy their meal.

[1448] Example 2: Eating sushi

[1449] 1. The device detects sushi on the table using a camera, analyzes the image data using OpenCV, and recognizes that it is sushi.

[1450] 2. The device's microphone collects the sound of the chopsticks and plate touching each other, and the sound data is analyzed using Librosa.

[1451] 3. The device's accelerometer measures the user's hand movements, and a Python script analyzes the eating speed.

[1452] 4. The device sends this data to the server via an HTTP request.

[1453] 5. The server recognizes the sushi from the image data and confirms that it is being eaten at the appropriate speed from the sound and acceleration data.

[1454] 6. The server generates music with an appropriate tempo and Japanese rhythm using an AI model.

[1455] Example prompt:

[1456] "Recognize sushi from image data, and generate Japanese-style rhythmic music if the user is eating at the appropriate speed based on sound and acceleration data."

[1457] 7. The server sends the generated music data to the device.

[1458] 8. The device plays music data in real time, allowing the user to enjoy their meal at a comfortable pace.

[1459] conclusion

[1460] This invention allows users to naturally adjust the pace of their meals, increasing the enjoyment of meals and is expected to promote healthy eating habits.

[1461] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1462] Step 1:

[1463] Sensor data is acquired by the device.

[1464] Specific behavior:

[1465] The camera takes a picture of the food on the table at a rate of one per second and collects image data.

[1466] A microphone continuously records the acoustic environment at the table, collecting the sounds of tableware and food as digital signals.

[1467] The acceleration sensor measures the movement of the user's hands and tableware in real time and collects acceleration data.

[1468] Input: Image data, sound data, and acceleration data from sensors installed on the dining table.

[1469] Output: Collected raw data (image data, sound data, acceleration data).

[1470] Step 2:

[1471] The device sends the collected sensor data to the server.

[1472] Specific behavior:

[1473] The device sends this data to the server as an HTTP request at regular intervals, for example, every 10 seconds.

[1474] Input: Raw data collected by the device (image data, sound data, acceleration data).

[1475] Output: The raw data sent to the server.

[1476] Step 3:

[1477] The server parses the data it receives.

[1478] Specific behavior:

[1479] The server analyzes the image data using computer vision algorithms (e.g., OpenCV or TensorFlow) to identify the type of food.

[1480] The server analyzes the sound data using an acoustic analysis library (e.g., Librosa) to identify the type of tableware and the sound of its operation.

[1481] The server analyzes the acceleration data and uses Python's NumPy library to measure the user's eating pace.

[1482] Input: Raw data (image data, sound data, acceleration data) sent to the server.

[1483] Output: Analyzed results (type of food, sound of dishes, eating pace).

[1484] Step 4:

[1485] The server generates music data based on the analysis results.

[1486] Specific behavior:

[1487] The server then uses the analysis results to determine the appropriate music genre and tempo. For example, if the food is pasta and the eating pace is fast, a slower Italian melody will be selected.

[1488] The server inputs prompt sentences into a generative AI model (e.g., OpenAI's GPT-3 or Jukedeck) and generates music data in real time.

[1489] Example prompt:

[1490] "Recognize pasta from image data, and if the user is eating quickly from sound and acceleration data, generate music with a slightly slower tempo using an Italian melody."

[1491] Input: Analyzed results (food type, dish sounds, eating pace).

[1492] Output: The generated music data.

[1493] Step 5:

[1494] The server transmits the generated music data to the terminal.

[1495] Specific behavior:

[1496] The music data generated by the server is sent to the terminal as an HTTP response.

[1497] Input: Generated music data.

[1498] Output: The music data sent to the device.

[1499] Step 6:

[1500] The device plays the music data in real time.

[1501] Specific behavior:

[1502] The music data received by the device is stored in a buffer and played in real time using Python's Pygame library, etc.

[1503] Users can relax and enjoy their meal while listening to music.

[1504] Input: Music data sent to the device.

[1505] Output: The played music.

[1506] (Application example 1)

[1507] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1508] In today's virtual stores and restaurants, unlike physical environments, users' behavior is left unmonitored while they eat or shop, making it difficult to adjust the pace of the meal or create a relaxing atmosphere. Furthermore, there is a lack of systems that can enhance the enjoyment of meals by providing appropriate music and promote healthy eating habits. Furthermore, the technology to dynamically adjust music for individual users is underdeveloped.

[1509] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1510] In this invention, the server includes means for detecting a user's behavior on the electronic device, the type of food, the eating speed, and sounds using sensors, means for analyzing the detected data and determining a music genre according to the type of food and a tempo according to the eating speed, means for generating the determined music data and playing it in real time, and means for dynamically changing the music data using a generative AI model to adjust the user's eating pace, thereby providing a relaxed atmosphere during the user's meal, allowing the user to naturally adjust the eating pace, and enriching the experience in the virtual store or virtual restaurant.

[1511] definition statement

[1512] "Sensor" is a general term for electronic devices that detect user behavior and environmental data in real time.

[1513] "Electronic device" refers to any electronic device that can connect to the internet and that a user uses while eating or shopping. Examples include smartphones, tablets, and personal computers.

[1514] "User behavior" refers to the actions and movements of users when eating or shopping, including hand movements, use of tableware, etc.

[1515] "Food type" refers to the specific type of food or dish a user is eating, such as pasta or sushi.

[1516] "Eating speed" refers to the speed at which a user brings food to their mouth when eating.

[1517] "Sound" refers to the sounds that occur while eating, including, for example, the sound of chewing food and the sound of dishes clashing.

[1518] "Means" refers to the methods or devices used to achieve a particular purpose.

[1519] "Analyzing data" refers to performing calculations and processing to extract information from collected data and find patterns and trends.

[1520] "Music genre" refers to a type or style of music, such as classical, jazz, or pop.

[1521] "Tempo" refers to the speed and rhythm of music or movement.

[1522] "Generating music data" refers to creating new music based on the analysis results.

[1523] "Playing in real time" means playing the data immediately after receiving it.

[1524] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to generate and analyze data.

[1525] "Dynamically changing music data" refers to changing the content of the music being played in real time based on new data or conditions along the way.

[1526] MODE FOR CARRYING OUT THE INVENTION

[1527] This invention is a system that monitors the user's behavior and eating pace in a virtual store or restaurant and provides appropriate music. This system uses sensors to collect user behavior data in real time and dynamically generates and changes music data based on the analysis results, enriching the user's dining experience.

[1528] System configuration and operation overview

[1529] The system has the following components:

[1530] 1. Device: An electronic device equipped with sensors such as a camera, microphone, and accelerometer. This device collects information on user behavior, eating speed, and environmental sounds in real time.

[1531] 2. Server: A central processing unit that analyzes data sent from the device and determines the music genre and tempo according to the type and speed of eating, and dynamically changes the music data using a generative AI model.

[1532] 3. User: A person who dine in a virtual environment. They listen to music from their device and enjoy a meal in a relaxed atmosphere.

[1533] How it works

[1534] Device data collection:

[1535] It uses a camera to capture footage of the user while they eat and analyzes the type of food they eat and the pace at which they eat.

[1536] It uses a microphone to collect sounds made by the user while eating, such as the clinking of dishes and chewing.

[1537] It uses an accelerometer to measure the movement of the user's hands and utensils to determine the speed at which they are eating.

[1538] Data analysis and music data generation:

[1539] The data collected by the device is sent to a server, which analyzes it and identifies the type of food and the speed at which it is eaten.

[1540] Based on the analyzed data, the server determines the appropriate music genre (for example, Italian music for Italian cuisine, Japanese music for Japanese cuisine) and tempo.

[1541] Furthermore, a generative AI model is used to dynamically generate and change music data based on the analysis results, providing a music environment that is optimal for the user's eating pace.

[1542] Real-time music playback:

[1543] The generated music data is sent to the device and played in real time, allowing users to naturally adjust the pace of their meal and enjoy it in a relaxed atmosphere.

[1544] Specific Examples

[1545] Example 1: Eating pasta at a virtual restaurant:

[1546] If a user is eating pasta in a virtual restaurant, the camera will recognize the pasta and find that they are eating quickly. The server will then analyze this data and generate Italian music at a slower tempo, encouraging the user to slow down and relax.

[1547] Example 2: Drinking coffee at a virtual cafe:

[1548] If a user is relaxing in a virtual cafe with a cup of coffee, the camera will recognize the coffee cup and detect a lack of movement. The server will then analyze this data and select acoustic music to create a more relaxing environment.

[1549] Prompt Sentence Examples

[1550] Below are some examples of specific prompts for implementing patents:

[1551] When a user eats pasta at a virtual restaurant, a camera and microphone capture their behavior and send the data to a server. The server analyzes the speed at which the user is eating and plays appropriate Italian music at a slightly slower tempo. The system uses Python's OpenCV, sounddevice, NumPy, and asyncio libraries.

[1552] This invention will improve the user experience in the virtual store, allowing them to enjoy their food and eat at a healthy pace.

[1553] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1554] Program processing steps

[1555] Step 1:

[1556] The device uses a camera, microphone, and accelerometer to collect real-time data on the user's behavior, ambient sounds, food type, and eating speed. During this data collection process, the camera captures images of the table and uses a recognition algorithm to identify the type of food (e.g., pasta, sushi, etc.). In addition, the microphone collects ambient sounds (e.g., the sound of dishes clattering, chewing, etc.), and the accelerometer measures the user's eating speed from the movement of their hands and dishes.

[1557] Input: Camera video data, audio data, acceleration data

[1558] Output: User behavior data (type of food, eating speed, environmental sounds)

[1559] Step 2:

[1560] The device sends the collected data to the server. In the data transmission process, the device uploads image data of the type of food recognized by the camera, audio data collected by the microphone, and eating speed data obtained by the accelerometer to the server.

[1561] Input: User behavior data (type of food, eating speed, environmental sounds)

[1562] Output: Raw data sent to the server

[1563] Step 3:

[1564] The server analyzes the received data to identify the type of food, eating speed, and environmental sounds. During the analysis process, an image recognition algorithm (using OpenCV) is used to identify the type of food, an audio analysis algorithm is used to classify the collected environmental sounds, and the speed of eating is calculated based on the acceleration data. This allows for specific analysis results to be obtained based on the characteristics of each data.

[1565] Input: Raw data sent to the server

[1566] Output: Analyzed data (food type, eating speed, environmental sound classification)

[1567] Step 4:

[1568] The server determines the appropriate music genre and tempo based on the analysis results. In the process of dynamically generating or changing music data using a generative AI model according to the analysis data, for example, if you eat quickly, it will select relaxing music with a slow tempo, and it will determine the appropriate music genre depending on the type of food (e.g., Italian music for pasta).

[1569] Input: Analyzed data (food type, eating speed, environmental sound classification)

[1570] Output: Determined music genre and tempo

[1571] Step 5:

[1572] The server sends the generated music data to the device, which then plays the music in real time. During the music playback process, the music data dynamically modified by the generative AI model is sent to the device, which stores the music in a buffer and then plays it through the speaker.

[1573] Input: Determined music genre and tempo

[1574] Output: Music data to be played

[1575] Step 6:

[1576] The user continues eating while listening to appropriate music. As a result, the user can naturally adjust the pace of their meal and enjoy it in a relaxed atmosphere. The device again monitors the user's reactions and behavior, and the entire system operates in a feedback loop.

[1577] Input: Music data to be played

[1578] Output: Relaxed eating environment, naturally adjusted eating pace

[1579] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1580] The present invention aims to provide appropriate music during meals to adjust the pace of the meal and create a relaxing atmosphere, and by combining it with an emotion engine that recognizes the user's emotions, it provides a more personalized music experience.

[1581] System configuration

[1582] The system consists of four main parts:

[1583] 1. Terminal: A device installed on the dining table that includes sensors (camera, microphone, accelerometer) and an emotion engine.

[1584] 2. Server: A central processing unit that analyzes data from the terminals and generates appropriate music data.

[1585] 3. Emotion engine: Recognizes the user's emotions and sends the data to the server.

[1586] 4. User: The person who eats the food.

[1587] Operational Overview

[1588] The system uses the device to collect environmental data during the meal, analyzes that data on the server, and takes into account data from the emotion engine to generate and play appropriate music. By repeating this cycle, the pace of the meal is naturally adjusted, enriching food culture.

[1589] How it works

[1590] 1. Sensor data collection by the device

[1591] Camera: The device's built-in camera takes pictures of the food on the table while the user is eating. Image recognition algorithms are used to identify the type of food. The device also analyzes the user's facial expressions and sends them to the emotion engine as emotional data.

[1592] Microphone: The device's microphone collects sounds made while eating, such as the sound of chopsticks and forks touching tableware and the tone of the user's voice, and sends these to the emotion engine.

[1593] Acceleration sensor: The device's built-in accelerometer detects the movement of the user's hands and tableware and measures the speed at which they eat.

[1594] 2. Data submission and analysis

[1595] Device: The device sends the image data, sound data, acceleration data collected, and emotion data analyzed by the emotion engine to the server.

[1596] Server: The server analyzes the data it receives. For example, it identifies the type of food from image data, the type of tableware and the sound of the movements from sound data, the speed of eating from acceleration data, and the user's current emotional state from emotion data.

[1597] 3. Generating music data

[1598] Server: Based on the data analyzed by the server, the music genre is determined according to the type of food and the tempo according to the eating speed, and the music is selected according to the user's emotional state. For example, if the user is feeling stressed, it will generate relaxing music, and if the user is in a happy mood, it will generate cheerful music.

[1599] Server: Generates music data in real time based on the determined music genre, tempo, and emotion.

[1600] 4. Playing Music

[1601] Device: The device stores the music data received from the server in a buffer and plays the music in real time. Users can listen to music that matches their emotions while eating, allowing them to naturally adjust the pace of their meal and enjoy a relaxed meal.

[1602] Specific examples

[1603] Example 1: When eating pasta

[1604] The user begins to eat pasta. The device's camera detects the pasta and sends the image data to the server. The device's microphone collects the sound of the fork and plate touching each other and sends the sound data to the server. The device's accelerometer measures the user's hand movements and sends the data to the server. The emotion engine then analyzes the user's facial expressions, determines that they are feeling stressed, and sends the emotion data to the server.

[1605] The server analyzes the data it receives, recognizes that the food is pasta and that the user is eating it quickly, and generates a relaxing Italian melody at a slow tempo because the user is feeling stressed. The music data is then sent to the device, which plays the music in real time, allowing the user to naturally adjust the pace of their meal and enjoy a relaxed meal.

[1606] Example 2: Eating sushi

[1607] The user begins to eat sushi. The device's camera detects the sushi and sends the image data to the server. The device's microphone collects the sound of the chopsticks hitting the plate and sends the sound data to the server. The device's accelerometer measures the user's hand movements and sends the data to the server. The emotion engine then analyzes the user's tone of voice, determines that they are enjoying the food, and sends the emotion data to the server.

[1608] The server analyzes the data it receives, recognizes that the food is sushi and that the user is eating at an appropriate pace, and because the user is enjoying themselves, generates a cheerful Japanese-style rhythm at the same tempo and sends the music data to the device. The device plays the music in real time, allowing the user to continue eating at an appropriate pace while enjoying the Japanese food atmosphere.

[1609] This invention allows users to naturally adjust the pace of their meals and enrich their food culture. It is also expected to promote healthy eating habits and increase the enjoyment of meals. Furthermore, by utilizing the emotion engine, it is possible to provide a personalized music experience that corresponds to the user's emotional state.

[1610] The processing flow will be explained below.

[1611] Step 1:

[1612] The device activates the camera, microphone, accelerometer, and emotion engine installed on the table.

[1613] Step 2:

[1614] The device uses a camera to capture images of food on the table and collects image data in real time, while the emotion engine simultaneously collects facial expression data from the user.

[1615] Step 3:

[1616] The device uses image recognition algorithms to identify the type of food from the collected image data, while an emotion engine analyzes facial expression data to identify the user's emotional state.

[1617] Step 4:

[1618] The device uses a microphone to collect sounds such as the clinking of dishes and other sounds made while eating, and the emotion engine also collects tone data from the user's voice.

[1619] Step 5:

[1620] The device uses an accelerometer to detect the movement of the user's hands and tableware and measure the speed at which they eat.

[1621] Step 6:

[1622] The image data, sound data, acceleration data collected by the terminal, and emotion data analyzed by the emotion engine are compiled into a single data packet and sent to the server.

[1623] Step 7:

[1624] The server receives the data packets and analyzes the image data to determine the type of food.

[1625] Step 8:

[1626] The server analyzes the sound data and identifies the type of tableware and the sound of its operation.

[1627] Step 9:

[1628] The server analyzes the acceleration data and calculates the user's eating speed.

[1629] Step 10:

[1630] The server analyzes the emotional data and recognizes the user's current emotional state.

[1631] Step 11:

[1632] Based on the analysis results, the server determines the music genre according to the type of food, the tempo according to the eating speed, and the characteristics of the music according to the emotional state.

[1633] Step 12:

[1634] The server uses a music generation algorithm to generate music data in real time according to the determined genre, tempo, and emotion.

[1635] Step 13:

[1636] The server transmits the generated music data to the terminal.

[1637] Step 14:

[1638] The terminal stores the music data received from the server in a buffer and plays it in real time.

[1639] Step 15:

[1640] The user continues eating while listening to the played music, adjusting the pace of the meal and emotional state.

[1641] Step 16:

[1642] The device continuously collects sensor data and emotion data and periodically transmits the data to a server.

[1643] Step 17:

[1644] The server analyzes the new data it receives and readjusts the tempo, genre and emotional characteristics of the music as needed.

[1645] Through this series of steps, the system can provide appropriate and personalized music while the user is eating, naturally pacing the meal and creating a relaxing atmosphere.

[1646] Example 2

[1647] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1648] Providing appropriate music during meals, naturally adjusting the pace of eating, and creating a relaxing atmosphere are challenges many people desire. However, existing systems have difficulty selecting music that reflects the user's eating situation and emotional state, and are limited to playing generic music. Furthermore, there are also limited systems that can reflect the type of food and eating speed in real time. Therefore, the present invention aims to solve these challenges.

[1649] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for detecting the type of food, eating speed, and sound using sensors, means for analyzing the detected data and selecting music according to the music genre corresponding to the type of food, the tempo corresponding to the eating speed, and the emotional state of the user, and means for generating the selected music data and playing it in real time. This makes it possible to provide a more personalized music experience based on environmental data during the meal and emotional data of the user.

[1650] text

[1651] A "sensor" is a device that detects physical changes and the surrounding environment and outputs that information as a signal.

[1652] "Food type" refers to the specific type of food consumed during a meal, primarily identified through image recognition.

[1653] "Eating speed" indicates the speed at which the user eats, and is measured using an acceleration sensor.

[1654] "Sound" refers to the sounds and noises generated during a meal and collected through a microphone.

[1655] "Detection" is the act of sensing specific information or data using sensors or other devices.

[1656] "Analysis" is the act of analyzing collected data to find meaning and patterns.

[1657] "Musical genre" is a term used to describe a category of music with a particular characteristic or style.

[1658] "Tempo" refers to the speed or rhythmic frequency of music.

[1659] "User's emotional state" refers to the emotions and moods the user is feeling at that time, as analyzed through the emotion engine.

[1660] "Selection" is the act of choosing the most suitable candidate from among multiple candidates.

[1661] "Generation" is the act of creating new content or results based on necessary data and information.

[1662] "Real-time" means that data is processed and output the instant it is collected.

[1663] "Music data" refers to digital data stored in a format that can be played as music.

[1664] text

[1665] The purpose of this invention is to adjust the pace of a meal and create a relaxing atmosphere by providing appropriate music during the meal. To achieve this, we will build a system that combines sensors and an emotion engine to collect user data, and a server analyzes the data and generates music. Specifically, the following components are required:

[1666] First, the device is equipped with a built-in camera, microphone, and accelerometer. These sensors collect environmental data while eating. The camera has high-resolution image capture capabilities and can identify the type of food and the user's facial expressions. The microphone collects audio, capturing the sound of dishes clashing and the tone of the user's voice. The accelerometer detects the movement of the user's hands and dishes and measures the speed of eating.

[1667] As a specific example, consider the case where a user is eating pasta. When the user starts to eat the pasta, the device's camera detects the pasta and sends the image data to the server. The microphone collects the sound of the fork and plate touching each other, and the accelerometer measures the user's hand movements. The emotion engine analyzes the user's facial expression and determines that they are feeling stressed.

[1668] The server receives and analyzes this data. Using a high-performance GPU, the server runs an image recognition algorithm to identify the food as pasta. It also measures the eating speed and recognizes that the user is eating quickly. Based on the data obtained from the emotion engine, it selects an Italian melody that will relax the user, who is feeling stressed, and generates that music data.

[1669] The generated music data is sent from the server to the device, which then stores it in a buffer and plays it in real time, allowing the user to naturally adjust the pace of their meal and enjoy a relaxed meal.

[1670] An example of a prompt sentence is, "Please explain the data processing flow of a system that automatically selects and plays appropriate music for a user eating pasta."

[1671] As described above, this system utilizes sensor data and emotional data to provide appropriate music to users, improving their dining experience.

[1672] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1673] text

[1674] Step 1:

[1675] (sensor data collection)

[1676] The device uses a camera, microphone, and accelerometer to collect environmental data while eating. The camera takes pictures of the meal and detects the type of food and the user's facial expression. The microphone collects the sound of dishes touching each other and the user's voice. The accelerometer detects the movement of the user's hands and the movement of dishes.

[1677] Input: Camera video, audio signal, acceleration data

[1678] Output: image data, audio data, acceleration data

[1679] Specific behavior:

[1680] The device takes a photo of the table every second while the user is eating and processes the images in real time. For example, it analyzes video of a user eating pasta to identify the type of pasta and the user's facial expression. At the same time, the microphone collects the sounds of dishes clashing and the tone of conversation, and the accelerometer captures data on the movement of the dishes.

[1681] Step 2:

[1682] (Data transmission)

[1683] The device sends the collected image data, voice data, acceleration data, and emotion data analyzed by the emotion engine to the server.

[1684] Input: image data, audio data, acceleration data, emotion data

[1685] Output: Compressed data packets

[1686] Specific behavior:

[1687] All data collected every second is compressed, packetized, and sent to a server over the Internet. Data is sent in a prioritized order to minimize data latency.

[1688] Step 3:

[1689] (Data Analysis)

[1690] The server analyzes the data sent from the device. It identifies the type of food and the user's facial expression from the image data, analyzes the sound of dishes clashing and the tone of the user's voice from the audio data, measures the eating speed from the acceleration data, and identifies the user's emotional state from the emotion data.

[1691] Input: Compressed data packet

[1692] Output: food type, eating rate, emotional state

[1693] Specific behavior:

[1694] The server uses a high-performance GPU to execute image recognition algorithms, such as detecting pasta from the received image data and identifying its type, while simultaneously measuring eating speed and analyzing emotional data from the sound of dishes clashing and the tone of the user's voice.

[1695] Step 4:

[1696] (Music data generation)

[1697] Based on the analysis results, the server determines the appropriate music genre and tempo, and generates music that matches the user's emotional state.

[1698] Input: food type, eating rate, emotional state

[1699] Output: Music data

[1700] Specific behavior:

[1701] The system determines the appropriate music genre and tempo based on the analysis results. For example, if it determines that a user is eating pasta quickly and feeling stressed, it will select a relaxing Italian melody and generate music data with a slow tempo.

[1702] Step 5:

[1703] (Music playing)

[1704] The device stores the music data received from the server in a buffer and plays the music in real time.

[1705] Input: Music data

[1706] Output: Playback music

[1707] Specific behavior:

[1708] When the device receives music data from the server, it immediately launches the music player and starts playing. This allows users to listen to music that matches their mood while eating. For example, if you are enjoying a relaxing meal of pasta, you can play a relaxing Italian melody.

[1709] (Application example 2)

[1710] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1711] Conventional dining environments lacked mechanisms to improve the user's dining experience, making it particularly difficult to respond in real time to the speed of eating and the user's emotions. Furthermore, the relaxing effect of personalized music was not fully realized. This resulted in a decline in meal quality and satisfaction, making it difficult to promote healthy eating habits.

[1712] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for detecting the type of food, eating speed, and sound using sensors, means for analyzing the detected data and determining a music genre according to the type of food and a tempo according to the eating speed, means for analyzing the user's emotions and personalizing music based on the analysis results, and means for generating the determined music data and playing it in real time. This makes it possible to provide personalized music in real time based on the captured data, improving the user's dining experience.

[1713] A "sensor" is a device that detects the environment or a specific situation and collects that information as electronic data.

[1714] "Food type" is a category for classifying specific foods in a meal, and is identified using image recognition or other methods.

[1715] "Eating speed" refers to the speed and rhythm of movements while eating, and is measured using an acceleration sensor or the like.

[1716] "Sound" refers to various acoustic information generated during a meal, which is collected using a microphone.

[1717] "Analysis" is the process of extracting and analyzing meaningful information based on data collected using sensors, etc.

[1718] A "music genre" is a category for classifying music, which is classified based on certain characteristics.

[1719] "Tempo" refers to the speed or beat of music and is the element that determines the speed of the rhythm.

[1720] "User emotion" refers to the emotional state determined from a person's facial expressions and voice as analyzed by the emotion engine.

[1721] "Personalization" means providing content that is optimized to suit the preferences and circumstances of each individual user.

[1722] "Real-time" means that processing and operations are carried out at the same speed as real time, and that they are reflected immediately without delay.

[1723] A "system" is a set of devices or programs that combine multiple components and functions and work together to achieve a specific purpose.

[1724] This invention is a system that uses sensors and an emotion engine to provide appropriate music in real time to improve the user's dining experience. The main hardware used is a terminal that includes a camera, microphone, acceleration sensor, and emotion engine, and a server that analyzes data and generates music.

[1725] System configuration

[1726] Terminal

[1727] Camera: Collects data to analyze the user's facial expressions and the type of food they eat.

[1728] Microphone: Collects sounds while eating, capturing the user's voice, the sound of dishes, etc.

[1729] Accelerometer: Measures the movement of the user's hands and the movement of the tableware to detect the speed at which the meal is being eaten.

[1730] Emotion engine: Analyzes the user's emotions based on data obtained from the camera and microphone.

[1731] server

[1732] Data analysis engine: Analyzes various data sent from the device and extracts food type, eating speed, and user emotions.

[1733] Music generation engine: Based on information from the data analysis engine, it determines the optimal music genre and tempo and generates music.

[1734] Real-time playback engine: The generated music is sent to the device in real time and provided to the user.

[1735] How it works

[1736] Camera: The camera captures the user's eating scene in real time and sends the image data to a server. Image recognition technology is used to analyze the type of food and the user's facial expression.

[1737] Microphone: The microphone collects sounds while you eat, capturing the tone of your voice and the sound of your tableware. The collected voice data is sent to a server and analyzed by the emotion engine.

[1738] Accelerometer: The accelerometer measures the movement of the user's hands and tableware to detect the speed at which the food is being eaten. This data is also sent to the server for analysis.

[1739] Emotion Engine: The emotion engine uses data from the camera and microphone to analyze the user's current emotional state. For example, if the user is feeling stressed, it can suggest relaxing music.

[1740] Server: The server receives and analyzes various data sent from the device. It determines the music genre according to the type of food, the tempo according to the eating speed, and also generates the optimal music based on the user's emotions.

[1741] Real-time playback: Music data generated on the server is sent to the device and played in real time, allowing users to naturally adjust the speed of their meal and enjoy a relaxed meal.

[1742] Specific examples

[1743] Example 1: When eating pasta

[1744] The user begins to eat pasta. The device's camera detects the pasta and sends the image data to the server. The device's microphone collects the sound of the fork and plate hitting each other and sends the sound data to the server. The device's accelerometer also measures the user's hand movements and sends the data to the server. The emotion engine then analyzes the user's facial expression, determines that the user is feeling stressed, and sends the emotion data to the server. The server analyzes the received data, recognizes that the pasta is being eaten quickly, and because the user is feeling stressed, generates relaxing music and sends the music data to the device. The device plays the music in real time, allowing the user to naturally adjust the pace of their meal and enjoy a relaxed meal.

[1745] Example 2: Eating sushi

[1746] The user begins to eat sushi. The device's camera detects the sushi and sends the image data to the server. The device's microphone collects the sound of the chopsticks hitting the plate and sends the sound data to the server. The device's accelerometer also measures the user's hand movements and sends the data to the server. The emotion engine then analyzes the user's tone of voice, determines that they are enjoying themselves, and sends the emotion data to the server. The server analyzes the received data, recognizes that the sushi is being eaten at an appropriate speed, generates upbeat music because the user is enjoying themselves, and sends the music data to the device. The device plays the music in real time, allowing the user to continue eating at an appropriate pace while enjoying the Japanese cuisine atmosphere.

[1747] Prompt Sentence Examples

[1748] "Emotion Engine": Analyzes the user's emotions from facial expressions.

[1749] "Meal Status Monitoring": The camera and microphone automatically detect the dining situation and play appropriate music.

[1750] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1751] Step 1:

[1752] The device captures the eating scene with a camera and collects image data. The camera captures the user's facial expressions and the type of food while eating, and sends the image data to a server in real time. The input is the image data acquired from the camera, and the output is the data sent to the server.

[1753] Step 2:

[1754] The device uses a microphone to collect voice data while eating. The microphone captures the tone of the user's voice and the sounds of tableware moving, and sends the voice data to a server. The input is the voice data acquired from the microphone, and the output is the data sent to the server.

[1755] Step 3:

[1756] The device uses an accelerometer to measure the movement of the user's hands and tableware. The accelerometer detects the speed of eating and sends that data to the server. The input is the movement data obtained from the accelerometer, and the output is the data sent to the server.

[1757] Step 4:

[1758] The server analyzes the received image data and identifies the type of food and the user's facial expression. Image recognition algorithms are used to identify the food classification and the user's emotional state. The input is the image data sent from the device, and the output is the classified food information and the user's emotional data.

[1759] Step 5:

[1760] The server analyzes the voice data and estimates the user's emotional state from the tone of their voice and the sounds of tableware movements. Using a voice analysis algorithm, emotions such as stress or joy are identified. The input is the voice data sent from the device, and the output is the estimated emotional state.

[1761] Step 6:

[1762] The server analyzes the acceleration data and identifies the speed at which the user is eating. Based on the data from the acceleration sensor, it determines how fast the user is eating. The input is the acceleration data sent from the device, and the output is the speed at which the user is eating.

[1763] Step 7:

[1764] The server determines the music genre and tempo based on the analyzed data. It selects the optimal music taking into account the type of ingredients, eating speed, and the user's emotional state. The input is food information, emotional data, and eating speed information, and the output is optimized music information.

[1765] Step 8:

[1766] The server generates music in real time based on the determined music information. Using a generative AI model, music data is created in real time and sent to the device. The input is optimized music information, and the output is generated music data.

[1767] Step 9:

[1768] The device stores the received music data in a buffer and plays the music in real time. Users can listen to appropriate music while eating, naturally adjusting the pace of their meal and enjoying a relaxed meal. The input is the music data sent from the server, and the output is the music being played.

[1769] Step 10:

[1770] The device continuously collects data and sends it to the server. By repeating this cycle, the music is adjusted appropriately according to the user's state. Input is data from the camera, microphone, and accelerometer, and output is data sent to the server.

[1771] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1772] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1773] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1774] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1775] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1776] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1777] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1778] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1779] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1780] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1781] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1782] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1783] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1784] 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.

[1785] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1786] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1787] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1788] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1789] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1790] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1791] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1792] The following is further disclosed regarding the above embodiment.

[1793] (Claim 1)

[1794] means for detecting food type, eating rate, and sound using sensors;

[1795] a means for analyzing the detected data and determining a music genre according to the type of food and a tempo according to the eating speed;

[1796] means for generating the determined music data and playing it back in real time;

[1797] A system including:

[1798] (Claim 2)

[1799] 2. The system according to claim 1, wherein the type of food is identified by image recognition.

[1800] (Claim 3)

[1801] 10. The system of claim 1, wherein the eating speed is detected by an acceleration sensor.

[1802] "Example 1"

[1803] (Claim 1)

[1804] means for detecting food type, eating rate, and sound using sensors;

[1805] means for transmitting the detected data to a cloud computing device;

[1806] means for analyzing the detected data in the cloud computing device;

[1807] A means for determining a music genre according to the type of food and a tempo according to the eating speed based on the analysis result;

[1808] A means for generating the determined music data in real time using a generative AI model;

[1809] means for transmitting the generated music data to a terminal;

[1810] a means for playing back the generated music data in real time;

[1811] A system including:

[1812] (Claim 2)

[1813] 2. The system according to claim 1, wherein the type of food is identified by image recognition.

[1814] (Claim 3)

[1815] 10. The system of claim 1, wherein the eating speed is detected by an acceleration sensor.

[1816] "Application Example 1"

[1817] (Claim 1)

[1818] means for detecting user actions, food types, eating speed, and sounds on the electronic device using sensors;

[1819] a means for analyzing the detected data and determining a music genre according to the type of food and a tempo according to the eating speed;

[1820] means for generating the determined music data and playing it back in real time;

[1821] a means for dynamically modifying the music data using a generative AI model to pace the user's eating;

[1822] A system including:

[1823] (Claim 2)

[1824] 2. The system according to claim 1, wherein the type of food is identified by image recognition.

[1825] (Claim 3)

[1826] 10. The system of claim 1, wherein the eating speed is detected by an acceleration sensor.

[1827] "Example 2: Combining Emotion Engines"

[1828] text

[1829] (Claim 1)

[1830] means for detecting food type, eating rate, and sound using sensors;

[1831] means for analyzing the detected data and selecting a music genre according to the type of food, a tempo according to the eating speed, and music according to the emotional state of the user;

[1832] means for generating the selected music data and playing it in real time;

[1833] A system including:

[1834] (Claim 2)

[1835] 2. The system according to claim 1, wherein the type of food is identified by image recognition and the emotional state of the user is analyzed by an emotion engine.

[1836] (Claim 3)

[1837] 10. The system of claim 1, wherein the eating speed is detected by an acceleration sensor and the tone of the user's voice is collected by a microphone.

[1838] "Application example 2 when combining emotion engines"

[1839] (Claim 1)

[1840] means for detecting food type, eating rate, and sound using sensors;

[1841] a means for analyzing the detected data and determining a music genre according to the type of food and a tempo according to the eating speed;

[1842] means for analyzing a user's emotions and personalizing music based on the analysis results;

[1843] means for generating the determined music data and playing it back in real time;

[1844] A system including:

[1845] (Claim 2)

[1846] The system of claim 1, wherein the system identifies the type of food by image recognition.

[1847] (Claim 3)

[1848] The system of claim 1, wherein the speed of eating is detected by an acceleration sensor. [Explanation of symbols]

[1849] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for detecting food type, eating rate, and sound using sensors; a means for analyzing the detected data and determining a music genre according to the type of food and a tempo according to the eating speed; means for generating the determined music data and playing it back in real time; A system including:

2. The system according to claim 1, wherein the type of food is identified by image recognition.

3. 2. The system according to claim 1, wherein the eating speed is detected by an acceleration sensor.

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