system

Abstract

Provide a system. The method includes: collecting biometric and environmental data via a wearable device or sensor; means for analyzing the collected data and determining the user's stress level; A means for using a generation AI to generate a relaxation program suitable for the user based on the judgment result; means for providing the generated relaxation program to a user; and means for adjusting the room environment and bedding settings based on the environmental data to optimize the user's sleep environment. Including system.

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, many people are exposed to various stresses, making it difficult to ensure high-quality sleep. Furthermore, because the conditions and environments of individuals susceptible to stress fluctuate daily, effective stress care and sleep improvement require tailored responses tailored to each individual user's condition. However, conventional stress care and sleep improvement methods and devices are limited in effectiveness because they are limited to general approaches that cannot adequately adapt to the individual user's condition. Therefore, there is a need for a system that can grasp each user's condition in real time, provide optimal relaxation programs tailored to that condition, and even adjust the indoor environment and bedding settings. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides a system having the following features: a means for collecting biometric data and environmental data using a wearable device or sensor, a means for analyzing the collected data and determining a user's stress level, a means for using a generation AI to generate a relaxation program suitable for the user based on the determination results, a means for providing the generated relaxation program to the user, and a means for adjusting the indoor environment and bedding settings based on the environmental data to optimize the user's sleep environment. This system enables detailed responses tailored to the user's individual condition, thereby reducing stress and improving sleep quality.

[0006] A "wearable device" is an electronic device that can be worn by the user and measures biometric data such as heart rate, body temperature, and activity level in real time.

[0007] A "sensor" is a device for collecting biological and environmental data, and is used to measure temperature, humidity, brightness, etc.

[0008] "Biometric data" is information related to the user's body, including heart rate, body temperature, activity level, and the like.

[0009] "Environmental data" is information about the user's surrounding environment, such as temperature, humidity, and brightness.

[0010] "Analyzing data" refers to performing a process to evaluate the user's condition based on the collected biometric data and environmental data.

[0011] "Determining stress level" refers to evaluating the user's stress state from the analyzed data and indicating it as a specific level.

[0012] A "relaxation program" is a set of instructions or activities provided to a user to reduce stress, and may include guided meditation audio, guided breathing exercises, relaxation music, etc.

[0013] "Generative AI" refers to software or a device that uses artificial intelligence technology to generate a relaxation program tailored to the user's condition.

[0014] "Adjusting indoor environment settings" refers to appropriately changing indoor environment conditions such as temperature, humidity, and brightness so that the user can be comfortable.

[0015] "Sleep environment" refers to the ambient and bedding conditions necessary for a user to get quality sleep.

[0016] "Adjusting bedding settings" refers to optimizing bedding conditions such as pillow height and mattress hardness to help the user sleep comfortably. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0025] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0038] The present invention relates to a system for reducing stress in a user and providing an optimal sleeping environment. Specific embodiments of the system will be described below.

[0039] System Overview

[0040] The system uses wearable devices and environmental sensors to collect biometric and environmental data from users, analyzes their stress levels based on that data, and uses generative AI to provide optimal relaxation programs for users based on the analysis results. It also optimizes the indoor environment and bedding settings to improve the quality of users' sleep.

[0041] Data collection and analysis

[0042] The device (wearable device or sensor) collects real-time data such as the user's heart rate, body temperature, activity level, room temperature, humidity, and brightness. This data is periodically sent to a server, which analyzes the data and determines the user's stress level. For example, if the heart rate is higher than normal or the activity level is low, it is determined to be a state of high stress.

[0043] Creation and provision of relaxation programs

[0044] Based on the analysis results, the server generates a relaxation program tailored to the user's stress level. The relaxation program created using the generative AI includes audio guided meditation, breathing exercise guides, relaxation music, etc. This program is sent to the device, which notifies the user of the program and encourages them to execute it.

[0045] Adjusting the indoor environment and bedding

[0046] The server optimizes the indoor environment (temperature, humidity, and brightness) based on past and current environmental data to optimize the user's sleeping environment. For example, it sends instructions to a smart thermostat to check whether the temperature is set to the optimal level for the user when they go to bed. It also adjusts the firmness and height of bedding based on the user's body pressure distribution data to provide a more comfortable sleeping environment.

[0047] Sleep tracking and feedback

[0048] The device continuously measures the user's sleep state (time to fall asleep, deep sleep, light sleep, wake-up time, etc.) and sends this data to a server. The server analyzes this data and evaluates the user's sleep quality. Based on the analysis results, it provides the user with feedback on the next day's activities and relaxation methods.

[0049] Specific examples

[0050] A concrete example of the daily routine

[0051] 1. Morning:

[0052] The device measures the user's heart rate and activity level and sends the data to a server.

[0053] The server analyzes the data and determines that the user is in a high stress state.

[0054] 2. Afternoon:

[0055] The server uses a generation AI to generate a relaxation program suitable for the user (e.g., a 10-minute breathing guide) and sends it to the device.

[0056] The terminal notifies the user of the program and prompts the user to execute the program.

[0057] 3. Night:

[0058] The device collects the user's sleep preparation data (heart rate, indoor environment) and sends it to the server.

[0059] The server calculates the recommended room temperature and humidity, and issues instructions to the smart device to adjust the environment.

[0060] 4. Next day morning:

[0061] The terminal collects the user's sleep data and transmits it to the server.

[0062] The server analyzes the user's sleep state and generates feedback.

[0063] The terminal notifies the user of relaxation suggestions for the next day.

[0064] In this way, the system carries out a series of processes, including data collection, analysis, provision of relaxation programs, environmental adjustments, and feedback, to reduce stress and improve the quality of sleep for users.

[0065] The processing flow will be explained below.

[0066] Step 1:

[0067] The terminal (wearable device or sensor) collects data such as the user's heart rate, body temperature, activity level, indoor temperature, humidity, and brightness in real time.

[0068] Step 2:

[0069] The device sends the collected data to the server at regular intervals (e.g., every minute).

[0070] Step 3:

[0071] The server receives the transmitted data and stores it in a database.

[0072] Step 4:

[0073] The server analyzes the stored data and determines the user's stress level.

[0074] For example, if your heart rate is higher than normal, your body temperature is high, or you are less active.

[0075] Step 5:

[0076] Based on the analysis results, the server uses a generation AI to generate a relaxation program suitable for the user.

[0077] Examples include guided meditation audio, guided breathing exercises, relaxation music, etc.

[0078] Step 6:

[0079] The server transmits the generated relaxation program to the terminal.

[0080] Step 7:

[0081] The terminal notifies the user of the received relaxation program and prompts the user to start the program.

[0082] Step 8:

[0083] The terminal provides the content of the relaxation program to the user.

[0084] Examples include playing audio guided breathing exercises, providing guided meditations, playing relaxation music, etc.

[0085] Step 9:

[0086] The server applies machine learning models based on past data and analyzes current data to optimize the user's sleep environment.

[0087] Example: Calculating the optimum room temperature, humidity, brightness, etc.

[0088] Step 10:

[0089] The server generates appropriate configuration instructions for smart devices (e.g., smart thermostats, smart lighting).

[0090] Step 11:

[0091] The terminal receives instructions from the server and sends environment adjustment commands to the associated smart devices.

[0092] Step 12:

[0093] The device continuously records the user's sleep state (time to fall asleep, deep sleep, light sleep, wake-up time, etc.).

[0094] Step 13:

[0095] The device transmits the collected sleep data to the server at regular intervals.

[0096] Step 14:

[0097] The server analyzes the received sleep data and evaluates the quality of the user's sleep.

[0098] Step 15:

[0099] Based on the analysis results, the server generates feedback to the user regarding the next day's activities and relaxation methods.

[0100] Step 16:

[0101] The server transmits the generated feedback to the terminal.

[0102] Step 17:

[0103] The device provides feedback to the user and displays suggestions for the next day's activities and relaxation.

[0104] In this way, the system performs a series of processes to reduce stress for the user and provide an optimal sleeping environment.

[0105] Example 1

[0106] 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."

[0107] In modern society, increased stress and poor sleep environments are causing health problems for many users. Furthermore, there is no integrated system for effectively managing stress and providing an optimal sleep environment. There is a need for a system that collects and analyzes users' biometric and environmental data in real time, and then provides appropriate relaxation programs and environmental adjustments.

[0108] 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.

[0109] In this invention, the server includes a means for collecting biological data and environmental data using a wearable device or a sensor, a means for periodically transmitting the collected data to the server, and a means for analyzing the data received by the server and determining the user's stress level. This allows the user's stress level to be specifically understood, enabling effective stress management.

[0110] The server includes a means for generating a relaxation program suitable for the user using a generative AI model based on the judgment result, and a means for transmitting the generated relaxation program to the terminal and notifying the user. This allows the user to relax at an appropriate time and reduce stress.

[0111] The server includes a means for adjusting the room environment and bedding settings based on past and current data to optimize the user's sleep environment, and a means for measuring the user's sleep state, transmitting the data to the server for analysis, thereby improving the user's sleep quality. The server also includes a means for providing the user with feedback based on the analysis results, allowing the user to take optimal actions for the next day.

[0112] A "wearable device" is an electronic device that can be worn on the body and can measure and collect the user's biometric data in real time.

[0113] A "sensor" is a device that detects and collects environmental and biological data.

[0114] "Biometric data" refers to data that indicates the physiological state of the user, such as heart rate, body temperature, and activity level.

[0115] "Environmental data" refers to data that indicates the state of the physical environment, such as the temperature, humidity, and brightness of the room.

[0116] The "server" is a computer system that oversees data collection, analysis, creation of relaxation programs, and feedback to users.

[0117] A "generative AI model" is a model used to create programs and analyses that are suitable for users using artificial intelligence.

[0118] "Relaxation programs" are programs such as guided meditation audio, breathing exercise guides, and relaxation music aimed at relieving stress in users.

[0119] A "terminal" is a device that a user directly operates or uses to receive information.

[0120] "Sleep environment" refers to the surrounding physical environment (temperature, humidity, brightness, etc.) that allows a user to sleep comfortably.

[0121] "Analysis" is the process of evaluating collected data using statistical or computational methods to derive useful information.

[0122] "Feedback" refers to advice or information provided to the user based on the analysis results.

[0123] System Overview

[0124] This invention is a system for reducing stress and providing an optimal sleep environment for users. The system uses wearable devices and environmental sensors to collect biometric and environmental data, and analyzes stress levels based on that data. Based on the analysis results, a generative AI model is used to provide the user with an optimal relaxation program, and the system further improves the user's sleep quality by optimizing the indoor environment and bedding settings.

[0125] Hardware and software used

[0126] The system's hardware includes wearable devices (e.g., Fitbit, smartwatches) and environmental sensors (e.g., smart thermostats, smart lights). The software uses generative AI models (e.g., OpenAI's GPT) for data analysis and relaxation program generation.

[0127] Data collection and analysis

[0128] Terminals (wearable devices and sensors) collect data in real time, such as the user's heart rate, body temperature, activity level, room temperature, humidity, and brightness. For example, a Fitbit measures the user's heart rate and activity level, while a smart thermostat measures the room temperature. This data is sent to a server at regular intervals, which analyzes it and determines the user's stress level. If the heart rate is higher than normal and the activity level is low, it is determined to be in a state of high stress.

[0129] Creation and provision of relaxation programs

[0130] The server uses a generative AI model to generate a relaxation program based on the analysis results. For example, if the user's stress level is high, a relaxation program including a 10-minute breathing exercise guide is generated. The generated program is sent to the device, which then notifies the user. For example, a notification may appear on the smartphone saying, "A 10-minute breathing exercise guide has been prepared. Start now."

[0131] Adjusting the indoor environment and bedding

[0132] The server optimizes the indoor environment (temperature, humidity, brightness) based on past and current data to optimize the user's sleeping environment. For example, to set the optimal room temperature (22 degrees) before bed, it instructs the smart thermostat to adjust it to 22 degrees. It also provides a more comfortable sleeping environment by adjusting the firmness and height of the smart bed based on the user's body pressure distribution data.

[0133] Sleep tracking and feedback

[0134] The device continuously measures the user's sleep status (time to fall asleep, deep sleep, light sleep, wake-up time, etc.) and sends this data to the server. The server analyzes the data and evaluates the quality of the user's sleep. For example, if the user had little time to get deep sleep, the server generates feedback about the next day's activities and relaxation methods and sends it to the device. The device then notifies the user of the feedback.

[0135] Examples of prompts:

[0136] "Explain how to create an appropriate relaxation program based on a user's heart rate and activity data."

[0137] "How can I use smart devices to optimize my users' sleep environment?"

[0138] keyword

[0139] Generative AI model, prompt sentence

[0140] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0141] System program processing flow

[0142] Step 1: Collect data

[0143] Terminals (wearable devices and environmental sensors) collect biometric and environmental data in real time, such as the user's heart rate, body temperature, activity level, room temperature, humidity, and brightness. The input is the user's biometric and environmental status, and the output is the collected data. For example, a user wearing a smartwatch measures their heart rate every minute, and a smart thermostat measures the room temperature every hour.

[0144] Step 2: Sending data

[0145] The terminal sends the collected data to the server at regular intervals. The input is the data collected by the terminal, and the output is the data sent to the server. For example, it is possible to set the collected data to be sent to the server every morning at 8:00 and evening at 8:00. This transmission ensures that the data is available for the next analysis step.

[0146] Step 3: Analyze the data

[0147] The server analyzes the received data and determines the user's stress level and sleep quality. The input is the biometric and environmental data sent to the server, and the output is the stress level assessment result. For example, it may analyze the data using statistical or computational methods using a Python data analysis library (e.g., Pandas) and determine that a user is in a high-stress state if their heart rate is higher than normal and their activity level is low.

[0148] Step 4: Create a relaxation program

[0149] The server uses a generative AI model based on the analysis results to generate a relaxation program suitable for the user. The input is the stress level assessment result, and the output is the generated relaxation program. For example, the operation includes inputting a prompt such as "Please create a 10-minute breathing exercise guide suitable for when the user is in a high-stress state" into the generative AI model and obtaining the generated program.

[0150] Step 5: Inform and implement a relaxation program

[0151] The server sends the generated relaxation program to the device, and the device notifies the user. The input is the generated relaxation program, and the output is the program notified to the user. For example, this includes an action to display a notification on the smartphone saying, "A 10-minute breathing exercise guide is ready. Start now." The user confirms the notification and runs the program.

[0152] Step 6: Adjust your indoor environment and bedding

[0153] The server issues instructions to optimize the indoor environment (temperature, humidity, brightness) based on past and current data to optimize the user's sleeping environment. The input is past and current data, and the output is instructions for the optimized environment settings. For example, to set the optimal room temperature (22 degrees) before bed, this includes issuing an instruction to the smart thermostat to adjust it to 22 degrees. It also adjusts the firmness and height of the smart bed to provide a more comfortable sleeping environment.

[0154] Step 7: Sleep tracking and feedback

[0155] The device continuously measures the user's sleep state (time to fall asleep, deep sleep, light sleep, wake-up time, etc.) and sends this data to the server. The server analyzes the sent data and evaluates the quality of the user's sleep. The input is the measured sleep data, and the output is an evaluation and feedback based on the analysis results. For example, feedback such as "You got one hour of deep sleep today. To relax even more, try the following meditation technique" is generated and sent to the device. The device notifies the user of the feedback.

[0156] (Application example 1)

[0157] 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."

[0158] Conventional systems have limitations in comprehensively analyzing users' stress levels and sleep quality and providing appropriate relaxation methods. Furthermore, they lack specific solutions for improving customer experience in brick-and-mortar stores. As a result, users lack the means to appropriately reduce stress in their daily lives and in stores, and are unable to create an optimal sleeping environment. This is the issue that needs to be resolved.

[0159] 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.

[0160] In this invention, the server includes: a means for collecting biometric data and environmental data by a wearable device or a sensor;

[0161] means for analyzing the collected data and determining the user's stress level;

[0162] A means for using a generation AI to generate a relaxation program suitable for the user based on the judgment result;

[0163] means for providing the generated relaxation program to a user;

[0164] A means to suggest relaxation services and recommended products according to the user's stress level,

[0165] means for informing the user of proposed relaxation services and products through the smartphone application in order to enhance the user's experience;

[0166] and means for adjusting room climate and bedding settings based on the environmental data to optimize the user's sleep environment.

[0167] This allows users to effectively reduce stress and enjoy an optimal sleeping environment, while also improving the customer experience in physical stores and increasing customer satisfaction within the store.

[0168] A "wearable device" is a device that can collect biometric data when worn by a user.

[0169] A "sensor" is a device for collecting environmental data in real time.

[0170] "Biometric data" refers to data that indicates the user's physical condition, such as heart rate, body temperature, and activity level.

[0171] "Environmental data" refers to data that indicates the state of the environment in which the user is located, such as the temperature, humidity, and brightness of the room.

[0172] "Stress level" is an indicator that indicates the user's current level of stress.

[0173] "Generative AI" is a system that uses artificial intelligence technology to generate optimal suggestions and programs based on specific input data.

[0174] "Relaxation programs" are means such as guided meditation audio, guided breathing exercises, and relaxation music provided to help users reduce stress.

[0175] A "smartphone application" is software that runs on a smartphone and provides notifications and suggestions to the user.

[0176] "Notification" is a means by which the system notifies the user of information or offers.

[0177] The "sleeping environment" refers to the state of the environment that allows the user to sleep comfortably.

[0178] The "indoor environment" refers to the environmental conditions such as temperature, humidity, and brightness in the room where the user spends time.

[0179] "Bedding" refers to items such as beds, mattresses, and pillows that users use when sleeping.

[0180] To implement this invention, it is first necessary to collect biometric data and environmental data of a user using a wearable device or sensor. The biometric data includes heart rate, body temperature, and activity level, and the environmental data includes indoor temperature, humidity, and brightness. This data is collected in real time and transmitted to a server.

[0181] The server then receives and analyzes the biometric and environmental data. Specifically, it analyzes the user's heart rate and activity level to determine the user's stress level. For example, if the heart rate is higher than normal or the activity level is low, it is determined that the user is in a high-stress state.

[0182] After determining the stress level, the server uses a generative AI to generate a relaxation program suited to the user. This relaxation program may include audio guided meditations, breathing exercises, relaxation music, etc. The generated relaxation program is notified to the user via a smartphone application.

[0183] Furthermore, the system suggests optimal relaxation services and recommended products to users based on their stress level. For example, a user who is judged to be in a high stress state may be offered a relaxing experience in a specific area (music, aromatherapy, etc.) or a discount coupon for a specific product. These suggestions are also notified via a smartphone application.

[0184] Before going to sleep, the system adjusts the user's sleep environment to optimize it. Specifically, the room temperature and humidity are adjusted to optimal conditions based on environmental data, and the firmness and height of the bedding are also adjusted based on the user's body pressure distribution data.

[0185] The hardware used includes a smartphone, a heart rate sensor, and an activity tracker, while the software includes a generative AI model using Python and a data collection library (sensor data acquisition module).

[0186] As a specific example, the following case can be considered.

[0187] Examples:

[0188] 1. At 2:00 PM, a customer enters the store wearing a heart rate sensor.

[0189] 2. The application detects that the heart rate is above 90 and determines that the person is in a state of high stress.

[0190] 3. The generative AI generates a 10-minute guided meditation audio and notifies the user via a smartphone app.

[0191] 4. The client follows a guided meditation to achieve relaxation.

[0192] 5. Furthermore, secondary relaxation can be provided by directing people to specific areas with relaxing music.

[0193] Example prompt sentence:

[0194] "The client's heart rate is over 90 and their activity level is low, indicating a high stress state. Please generate an appropriate relaxation program to reduce the client's stress."

[0195] "Generate a relaxing 10-minute guided meditation audio for clients with high stress levels."

[0196] As described above, this system reduces stress and improves the quality of sleep for users, improving the customer experience in physical stores and increasing customer satisfaction within the stores.

[0197] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0198] Step 1:

[0199] Data collection

[0200] Wearable devices worn by users and installed sensors collect biometric and environmental data such as heart rate, body temperature, activity level, indoor temperature, humidity, and brightness.

[0201] Input: Biometric and environmental data from wearable devices and sensors

[0202] Output: Collected biometric and environmental data

[0203] Specific operation: The sensor measures data in real time and transmits the data to the device via Bluetooth, Wi-Fi, etc.

[0204] Step 2:

[0205] Data transmission and analysis

[0206] The device sends the collected data to a server, which analyzes the data and determines the user's stress level.

[0207] Input: Biometric and environmental data sent from the device to the server

[0208] Output: Analyzed stress level judgment result

[0209] Specific operation: The server inputs the received data into an analysis algorithm to detect abnormal values ​​in heart rate and activity level.

[0210] Step 3:

[0211] Creation of relaxation programs

[0212] Based on the stress level assessment results, the server uses a generation AI to generate a relaxation program suitable for the user.

[0213] Input: Stress level judgment result

[0214] Output: Generated relaxation program

[0215] Specific operation: A prompt statement (e.g., "The customer's heart rate is over 90 and their activity level is low, indicating a high level of stress. Please generate an appropriate relaxation program to reduce the customer's stress.") is input into the generative AI model, and a program is generated.

[0216] Step 4:

[0217] Program Offerings and Notifications

[0218] The generated relaxation program is sent to the terminal (smartphone application) and notified to the user.

[0219] Input: Generated relaxation program

[0220] Output: Relaxation program notified to the user

[0221] Specific operation: The server sends program data to the device, and the application displays a push notification.

[0222] Step 5:

[0223] Relaxation services and product proposals

[0224] Depending on the user's stress level, the system will suggest optimal relaxation services and recommended products (such as discount coupons).

[0225] Input: Relaxation program generated by AI, user's current stress level

[0226] Output: Suggested relaxation services and products

[0227] Specific operation: The application displays appropriate service and product suggestions based on the user's stress level.

[0228] Step 6:

[0229] Adjusting the indoor environment and bedding

[0230] While the user is performing the relaxation program or afterwards, the server optimally adjusts the room environment and bedding settings based on the environmental data.

[0231] Input: Historical and current environmental data

[0232] Output: Optimized room environment and bedding settings

[0233] Specific operation: The server analyzes environmental data and sends instructions to the smart thermostat or smart bed to make adjustments.

[0234] Step 7:

[0235] Feedback and analysis results

[0236] The next morning, the device collects the user's sleep data and sends it to the server, which analyzes the data, evaluates the user's sleep quality, generates feedback, and notifies the user.

[0237] Input: Sleep data sent from the device

[0238] Output: Analysis results and feedback

[0239] Specific operation: The server analyzes the data, compiles feedback, sends it to the device, and the application displays it.

[0240] This allows users to receive appropriate relaxation programs, optimize their environment to ensure high quality sleep, and improve their experience in physical stores.

[0241] 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.

[0242] The present invention relates to a system that reduces stress and provides an optimal sleeping environment for users. Furthermore, the system incorporates an emotion engine that recognizes the user's emotions, and performs relaxation programs and environmental adjustments based on the emotions.

[0243] System Overview

[0244] The system uses wearable devices and environmental sensors to collect biometric and environmental data from the user, and also collects emotional data from the user using an emotion engine. The data is then analyzed to determine the user's stress level. Based on the analysis results, generative AI is used to provide the user with an optimal relaxation program, and the system also optimizes the indoor environment and bedding settings to improve the user's sleep quality.

[0245] Data collection and analysis

[0246] The terminal (wearable device or sensor) collects data such as the user's heart rate, body temperature, activity level, room temperature, humidity, and brightness in real time. In addition, an emotion engine is used to collect emotional data in real time from the user's voice and facial expressions. This data is periodically sent to a server, which analyzes the data and determines the user's stress level. For example, if the heart rate is higher than normal or the activity level is low, it is determined to be in a state of high stress. Emotional data is also used to assist in determining the stress level.

[0247] Creation and provision of relaxation programs

[0248] Based on the analysis results, the server generates a relaxation program that takes into account the user's stress level and emotional data. The relaxation program created using generative AI includes audio guided meditation, breathing exercise guides, relaxation music, etc. This program is sent to the device, which notifies the user of the program and encourages them to execute it.

[0249] Adjusting the indoor environment and bedding

[0250] The server optimizes the indoor environment (temperature, humidity, and brightness) based on past data, current environmental data, and emotional data to optimize the user's sleeping environment. For example, it sends instructions to a smart thermostat to check whether the temperature is set to the optimum level for the user when they go to bed. It also adjusts the firmness and height of bedding based on the user's body pressure distribution data to provide a more comfortable sleeping environment.

[0251] Sleep tracking and feedback

[0252] The device continuously measures the user's sleep status (time to fall asleep, deep sleep, light sleep, wake-up time, etc.) and sends this data to the server. The server analyzes this data and evaluates the user's sleep quality. Based on the analysis results, it provides the user with feedback on the next day's activities and relaxation methods.

[0253] Specific examples

[0254] A concrete example of the daily routine

[0255] 1. Morning:

[0256] The device measures the user's heart rate and activity level, collects the user's emotional data using an emotion engine, and sends the data to a server.

[0257] The server analyzes the data and determines that the user is in a high stress state.

[0258] 2. Afternoon:

[0259] The server uses generative AI to generate a relaxation program (e.g., a 10-minute breathing guide) based on the user's stress level and emotional data, and sends it to the device.

[0260] The terminal notifies the user of the program and prompts him to run it.

[0261] 3. Night:

[0262] The device collects the user's sleep preparation data (heart rate, indoor environment, and emotional data) and sends it to the server.

[0263] The server calculates the recommended room temperature and humidity, and issues instructions to the smart device to adjust the environment.

[0264] 4. Next day morning:

[0265] The device collects the user's sleep data and sends it to the server.

[0266] The server analyzes the user's sleep state and generates feedback.

[0267] The device notifies the user of relaxation suggestions for the next day.

[0268] In this way, the system performs a series of processes, from data collection and analysis to providing a relaxation program, adjusting the environment, and providing feedback, thereby reducing stress and improving the quality of sleep for users. At the same time, by utilizing emotional data from the emotion engine, it becomes possible to more accurately assess stress and provide relaxation programs.

[0269] The processing flow will be explained below.

[0270] Step 1:

[0271] The terminal (wearable device or sensor) collects data such as the user's heart rate, body temperature, activity level, indoor temperature, humidity, and brightness in real time.

[0272] Step 2:

[0273] The emotion engine collects emotional data from the user's voice and facial expressions in real time.

[0274] Step 3:

[0275] The device transmits the collected biometric data, environmental data, and emotional data to the server at regular intervals (e.g., every minute).

[0276] Step 4:

[0277] The server receives the transmitted data and stores it in a database.

[0278] Step 5:

[0279] The server analyzes the stored data and determines the user's stress level.

[0280] For example, if your heart rate is higher than normal, your body temperature is high, or your activity level is low, you are considered to be in a high stress state.

[0281] Emotion data from the emotion engine is used as auxiliary information to improve the accuracy of stress judgment.

[0282] Step 6:

[0283] Based on the analysis results, the server uses a generation AI to generate a relaxation program suitable for the user.

[0284] Examples include guided meditation audio, guided breathing exercises, relaxation music, etc.

[0285] Step 7:

[0286] The server transmits the generated relaxation program to the terminal.

[0287] Step 8:

[0288] The terminal notifies the user of the received relaxation program and prompts the user to start the program.

[0289] Step 9:

[0290] The terminal provides the content of the relaxation program to the user.

[0291] Examples include playing audio guided breathing exercises, providing guided meditations, playing relaxation music, etc.

[0292] Step 10:

[0293] The server applies machine learning models based on past data and analyzes current data to optimize the user's sleep environment.

[0294] Example: Calculating the optimum room temperature, humidity, brightness, etc.

[0295] Step 11:

[0296] The server generates appropriate configuration instructions for smart devices (e.g., smart thermostats, smart lighting).

[0297] Step 12:

[0298] The terminal receives instructions from the server and sends environment adjustment commands to the associated smart devices.

[0299] Step 13:

[0300] The device continuously measures the user's sleep state (time to fall asleep, deep sleep, light sleep, wake-up time, etc.).

[0301] Step 14:

[0302] The device transmits the collected sleep data to the server at regular intervals.

[0303] Step 15:

[0304] The server analyzes the received sleep data and evaluates the quality of the user's sleep.

[0305] Step 16:

[0306] Based on the analysis results, the server generates feedback to the user regarding the next day's activities and relaxation methods.

[0307] Step 17:

[0308] The server transmits the generated feedback to the terminal.

[0309] Step 18:

[0310] The device provides feedback to the user and displays suggestions for the next day's activities and relaxation.

[0311] In this way, the system performs a series of processes to reduce the user's stress and provide an optimal sleeping environment. By utilizing emotion data from the emotion engine, it becomes possible to more accurately assess stress and provide relaxation.

[0312] Example 2

[0313] 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."

[0314] In modern society, many people suffer from health problems due to daily stress and inadequate sleep environments. Exposure to stress in particular can impair physical and mental health and reduce quality of life. To solve this problem, it is necessary to accurately grasp users' stress levels, provide appropriate relaxation programs, and optimize their sleep environments. However, there is currently no system that can comprehensively achieve these goals.

[0315] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0316] In this invention, the server includes means for collecting biometric data and environmental data using a wearable device or sensor, means for collecting user emotion data using an emotion engine, means for periodically transmitting the collected data to the server, means for analyzing the transmitted data and determining the user's stress level, means for using a generation AI to generate a relaxation program suitable for the user based on the determination result, means for providing the generated relaxation program to the user, means for adjusting the indoor environment and bedding settings based on the environmental data to optimize the user's sleep environment, means for measuring the user's sleep data and transmitting it to the server, and means for providing feedback to the user based on the analysis result, thereby enabling the user to reduce stress and improve the quality of their sleep.

[0317] A "wearable device" is an electronic device that can be worn by a user and collects biometric data such as heart rate, body temperature, and activity level.

[0318] A "sensor" is a device that measures environmental data and collects data such as temperature, humidity, and brightness.

[0319] The "emotion engine" is a system that analyzes emotions from the user's voice and facial expressions and collects that data.

[0320] "Biometric data" refers to data relating to the user's physical condition, such as the user's heart rate, body temperature, and activity level.

[0321] "Environmental data" refers to data relating to the user's surrounding environment, such as the temperature, humidity, and brightness of the room.

[0322] The "means for determining stress level" refers to an algorithm or program that the server uses to analyze the collected biometric data and emotional data and determine the user's stress state.

[0323] "Generative AI" is an artificial intelligence model that generates a relaxation program suitable for the user based on collected data.

[0324] "Relaxation programs" are means such as guided meditation audio, guided breathing exercises, and relaxation music provided to help users reduce stress.

[0325] A "means for optimizing the sleep environment" is a system for adjusting the room temperature, humidity, lighting, and bedding settings based on environmental data.

[0326] "Sleep data" refers to data relating to the user's sleep state, such as the time it takes for the user to fall asleep, deep sleep, light sleep, and wake-up time.

[0327] The "means for providing feedback" is a system that notifies the user of advice about the next day's activities and relaxation methods based on the analysis results.

[0328] This invention is a system for reducing user stress and providing an optimal sleeping environment. Specifically, it uses wearable devices and environmental sensors to collect biometric and environmental data from the user, and also combines it with an emotion engine to collect the user's emotional data. This data is analyzed to determine the user's stress level, and a generative AI model is used to generate a relaxation program, and the indoor environment and bedding are adjusted accordingly.

[0329] Data collection and analysis

[0330] First, the device (wearable device or sensor) collects data in real time, such as the user's heart rate, body temperature, activity level, room temperature, humidity, and brightness. Furthermore, an emotion engine is used to collect emotional data from the user's voice and facial expressions. This data is continuously sent to a server, which analyzes it and determines the user's stress level. This analysis is carried out using programming languages ​​such as Python and machine learning algorithms.

[0331] Examples:

[0332] While the user is wearing the wearable device at 15:00, the device measures their heart rate and body temperature every minute, and periodically sends the data to a server.

[0333] Example prompt sentence:

[0334] Collect the user's morning heart rate and activity data, as well as emotional data, to determine their stress level.

[0335] Creation and provision of relaxation programs

[0336] The server uses the analysis results, taking into account the user's stress level and emotional data, to generate a relaxation program using a generative AI. The relaxation program generated by the generative AI includes audio guided meditation, breathing guides, relaxation music, etc. This program is sent to the device, which notifies the user and encourages them to carry it out.

[0337] Examples:

[0338] For users who are judged by the server to be in a high stress state, a generative AI model is used to generate a 10-minute audio guided meditation and send it to the user's device.

[0339] Example prompt sentence:

[0340] Generate and notify relaxation programs according to the user's high stress state.

[0341] Adjusting the indoor environment and bedding

[0342] The server adjusts the indoor environment based on the collected environmental and emotional data to optimize the user's sleeping environment. For example, it can set the appropriate room temperature and humidity for the smart thermostat and adjust the brightness of the smart lights. It can also adjust the firmness and height of bedding based on the user's body pressure distribution data to provide a comfortable sleeping environment.

[0343] Examples:

[0344] One hour before the user goes to bed, the server instructs the smart thermostat to set the room temperature to 22 degrees and the humidifier to adjust the humidity to 50%.

[0345] Example prompt sentence:

[0346] Calculate the optimal room temperature and humidity based on the user's pre-sleep data and provide instructions to adjust the indoor environment.

[0347] Sleep data measurement and analysis

[0348] The device measures the user's sleep status (time to fall asleep, deep sleep, light sleep, wake-up time, etc.) and sends the data to a server. The server analyzes this data and evaluates the user's sleep quality. Based on this evaluation, the device provides feedback on the next day's activities and relaxation methods.

[0349] Examples:

[0350] When the user goes to sleep, the device automatically switches to sleep mode and measures their heart rate and body temperature. When they wake up, the data is sent to a server and feedback is generated to assess the quality of their sleep.

[0351] Example prompt sentence:

[0352] Analyze the user's sleep data and generate and notify relaxation suggestions for the next day.

[0353] In this way, the system performs a series of processes, from collecting data and analyzing it to providing a relaxation program, adjusting the environment, and providing feedback, thereby reducing stress and improving the quality of sleep for the user. Furthermore, by utilizing the emotion engine, it is possible to provide more personalized stress assessments and relaxation programs.

[0354] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0355] Step 1:

[0356] Terminals (wearable devices and sensors) collect biometric and environmental data.

[0357] Specific operation: The device collects the user's heart rate, body temperature, activity level, indoor temperature, humidity, and brightness in real time.

[0358] Input: Biometric and environmental data obtained from the user.

[0359] Output: A set of collected data (e.g. heart rate, body temperature, temperature, humidity, etc.).

[0360] Step 2:

[0361] The terminal uses an emotion engine to collect emotion data of the user.

[0362] Specific operation: The device uses its built-in microphone and camera to analyze the user's voice and facial expressions and obtain emotional data.

[0363] Input: User's voice and facial expressions.

[0364] Output: Emotional data (e.g., emotional state or tendency).

[0365] Step 3:

[0366] The terminal periodically transmits the collected data to the server.

[0367] Specific operation: The device uses Wi-Fi or Bluetooth to send data to the server at regular intervals. If the transmission is successful, it proceeds to the next data collection, but if it fails, it retries.

[0368] Input: Collected biometric, environmental, and emotional data.

[0369] Output: The data sent to the server.

[0370] Step 4:

[0371] The server analyzes the received data and determines the user's stress level.

[0372] How it works: The server uses programming languages ​​such as Python to analyze data with machine learning algorithms and evaluate stress levels. For example, if your heart rate is high, your activity level is low, or there is a lot of negative emotional data, it will determine that you are under high stress.

[0373] Input: The dataset sent from the device.

[0374] Output: The user's stress level as a result of the analysis (e.g. high stress, low stress).

[0375] Step 5:

[0376] The server generates a relaxation program using a generative AI model based on the judgment results.

[0377] Specific operation: The server uses the generative AI model to generate a relaxation program (e.g., meditation audio guide, breathing exercise guide) that takes into account the user's stress level and emotional data.

[0378] Input: Stress level analysis results and emotion data.

[0379] Output: Relaxation program (e.g. 10-minute guided meditation).

[0380] Step 6:

[0381] The server transmits the generated relaxation program to the terminal, and the terminal notifies the user and prompts them to execute it.

[0382] Specific operation: The server sends the relaxation program to the terminal, and the terminal displays a notification of the program to the user and a message prompting the user to run it.

[0383] Input: The generated relaxation program.

[0384] Output: The relaxation program notified to the user.

[0385] Step 7:

[0386] The server issues instructions to adjust the room climate and bedding settings based on the environmental data.

[0387] Specific operation: Based on current indoor environment data, the server issues appropriate temperature, humidity, and brightness instructions to smart thermostats and smart lights.

[0388] Input: User's environmental data and sleep preparation data.

[0389] Output: Calibration instructions sent to the smart device.

[0390] Step 8:

[0391] The device measures the user's sleep state and sends the data to a server.

[0392] How it works: The device measures the user's heart rate, body temperature, and activity level from the time they fall asleep until they wake up, and sends this data to a server in the morning.

[0393] Input: User's biometric data while sleeping.

[0394] Output: Measured sleep data.

[0395] Step 9:

[0396] The server analyzes the sleep data and provides feedback to the user.

[0397] Specific operation: The server analyzes the acquired sleep data and evaluates the user's sleep quality. Based on the results, it generates feedback on the user's behavior and relaxation methods for the next day and notifies the user via the device.

[0398] Input: Sleep data.

[0399] Output: The generated feedback and notifications based on it.

[0400] (Application example 2)

[0401] 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."

[0402] Currently, the working environment for staff in brick-and-mortar stores is often stressful, making it difficult to improve staff efficiency and reduce their mental burden. Furthermore, in order to provide comfortable service to customers, it is necessary to properly manage staff health and emotional states. Current technology does not adequately analyze real-time biometric and emotional data, and provide effective relaxation programs based on that data. Therefore, a system that solves these issues is needed.

[0403] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0404] In this invention, the server includes means for collecting biometric data and environmental data using a wearable device or sensor, means for analyzing the collected data and determining the user's stress level, means for using a generation AI to generate a relaxation program suitable for the user based on the determination result, means for providing the generated relaxation program to the user, means for adjusting the facility environment based on the environmental data to optimize the user's working environment, means for analyzing the emotion data of the user and facility users using an emotion engine, and notification means for providing a relaxation program in real time according to the stress level, thereby reducing staff stress, improving work efficiency, and providing comfortable service to customers.

[0405] A "wearable device" is a measuring device that can be worn by a user and has the function of collecting biological data and environmental data.

[0406] A "sensor" is a device that detects physical quantities, and is particularly used to collect environmental data (temperature, humidity, brightness, etc.) and biological data (heart rate, body temperature, etc.).

[0407] "Biometric data" refers to data relating to the user's physical condition, including, for example, heart rate, body temperature, and the like.

[0408] "Environmental data" refers to data relating to the environment surrounding the user, including, for example, temperature, humidity, brightness, and the like.

[0409] "Stress level" is an indicator of the user's psychological and physical stress state.

[0410] "Generative AI" is a system that uses an artificial intelligence model to generate the optimal relaxation program for each user.

[0411] A "relaxation program" is a series of guides, music, and other content designed to promote relaxation in users.

[0412] "Working environment" refers to the place and conditions in which staff work, including in particular factors relating to the comfort and safety of that environment.

[0413] "Facility environment" refers to the entire environment of a physical store or service location, including physical conditions such as temperature, humidity, and brightness.

[0414] The "emotion engine" is a system for analyzing the emotional state of users and facility visitors from their facial expressions and voices.

[0415] "Notification means" refers to a mechanism for conveying information and instructions to the user in real time, and specifically includes the use of smart glasses or a smartphone.

[0416] This invention mainly uses wearable devices, sensors, emotion engines, generative AI, servers, and notification means to create a system that reduces staff stress and optimizes the working environment in physical stores.

[0417] System Overview

[0418] Wearable devices and sensors collect real-time biometric data from staff and store environment data, including heart rate, body temperature, ambient temperature, humidity, and brightness, and periodically transmit this data to a server.

[0419] The server analyzes the collected data and uses generative AI to determine the stress level of staff. If the stress level is high, the server generates an appropriate relaxation program and provides it to the relevant staff member. It also uses an emotion engine to analyze the emotional data of staff and customers, making even more accurate stress assessments.

[0420] Optimizing your environment

[0421] The server adjusts the facility environment based on environmental data to optimize the staff's working environment. For example, if the room temperature is too high, the server will automatically issue instructions to lower the temperature. If the light level is low, the server will adjust the lighting.

[0422] Real-time notifications

[0423] Smart glasses or smartphones are used as notification devices to provide real-time relaxation programs based on stress levels, allowing staff to receive the programs on the spot.

[0424] Specific examples

[0425] For example, imagine a brick-and-mortar store staff wearing smart glasses, which could enable the following daily routine:

[0426] 1. Morning:

[0427] The smart glasses measure the heart rate and body temperature of staff, and an emotion engine analyzes the staff's emotional data, which is then sent to a server.

[0428] Example prompt sentence:

[0429] "Data will now be sent to the server: including staff heart rate, body temperature, store temperature, humidity, and lighting intensity."

[0430] 2. Afternoon:

[0431] The server analyzes the data and determines that the staff member's stress level is high. Using generative AI, it generates a relaxation program suitable for the staff member and notifies the smart glasses.

[0432] Example prompt sentence:

[0433] "We have determined that staff members have high stress levels. We will now play a 10-minute guided breathing program."

[0434] 3. Evening:

[0435] If the environment inside the facility is not comfortable, the server will send instructions to smart devices to adjust the temperature and lighting.

[0436] Example prompt sentence:

[0437] "The temperature is high, please adjust the set temperature to 22 degrees."

[0438] These processes ensure that the working environment for staff is always optimized, reducing stress and enabling the provision of comfortable service to customers.

[0439] Such systems and methods will enable stress management and environmental adjustment in physical stores.

[0440] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0441] Step 1: Data collection

[0442] The terminal uses wearable devices and sensors to collect staff biometric data (heart rate, body temperature, etc.) and store environmental data (temperature, humidity, brightness, etc.) in real time. This data is periodically aggregated in the terminal. The input is various data from the wearable devices and sensors, and the output is a set of collected data.

[0443] Step 2: Send data

[0444] The terminal sends the collected biometric and environmental data to the server. The input is the set of data collected in step 1, and the output is the data received at the server side.

[0445] Step 3: Data analysis

[0446] The server analyzes the received biometric and environmental data. This analysis uses a generative AI model to determine the staff member's stress level from each data point. It also uses an emotion engine to extract and analyze emotional data from the facial expressions and voices of staff and customers. The input is the transmitted data set, and the output is the staff member's stress level and emotional state.

[0447] Step 4: Create a relaxation program

[0448] The server generates a relaxation program suited to each staff member based on the analysis results. In this process, it uses a generative AI to create a program (e.g., meditation audio guide, breathing exercise guide, relaxation music, etc.) that is optimal for each staff member's stress level and emotional state. The input is the analysis results, and the output is the generated relaxation program.

[0449] Step 5: Notification of relaxation program

[0450] The server sends the generated relaxation program to the terminal, which then notifies the staff via smart glasses or a smartphone. The input is the relaxation program, and the output is the program notification received by the staff. Specifically, the program is displayed on the smart glasses or instructions are played back via voice.

[0451] Step 6: Adjusting the environment

[0452] Based on the collected environmental data, the server sends instructions to smart devices to optimize the environment within the facility. For example, if the room temperature is too high, lower the temperature, or if the lights are too dim, increase the brightness. These instructions are transmitted to smart thermostats and smart lights. The input is environmental data, and the output is the adjusted environmental conditions.

[0453] Step 7: Reassessment and feedback

[0454] The terminals continuously collect biometric and environmental data from staff. The server then analyzes this data again, monitors changes in stress levels and emotional states, and provides feedback as needed. The input is a newly collected set of data, and the output is feedback information. Specifically, the system notifies staff of relaxation suggestions and work environment advice for the following day.

[0455] 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.

[0456] 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.

[0457] 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.

[0458] [Second embodiment]

[0459] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0460] 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.

[0461] 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).

[0462] 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.

[0463] 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.

[0464] 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).

[0465] 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.

[0466] 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.

[0467] 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.

[0468] 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.

[0469] 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.

[0470] 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."

[0471] The present invention relates to a system for reducing stress in a user and providing an optimal sleeping environment. Specific embodiments of the system will be described below.

[0472] System Overview

[0473] The system uses wearable devices and environmental sensors to collect biometric and environmental data from users, analyzes their stress levels based on that data, and uses generative AI to provide optimal relaxation programs for users based on the analysis results. It also optimizes the indoor environment and bedding settings to improve the quality of users' sleep.

[0474] Data collection and analysis

[0475] The device (wearable device or sensor) collects real-time data such as the user's heart rate, body temperature, activity level, room temperature, humidity, and brightness. This data is periodically sent to a server, which analyzes the data and determines the user's stress level. For example, if the heart rate is higher than normal or the activity level is low, it is determined to be a state of high stress.

[0476] Creation and provision of relaxation programs

[0477] Based on the analysis results, the server generates a relaxation program tailored to the user's stress level. The relaxation program created using the generative AI includes audio guided meditation, breathing exercise guides, relaxation music, etc. This program is sent to the device, which notifies the user of the program and encourages them to execute it.

[0478] Adjusting the indoor environment and bedding

[0479] The server optimizes the indoor environment (temperature, humidity, and brightness) based on past and current environmental data to optimize the user's sleeping environment. For example, it sends instructions to a smart thermostat to check whether the temperature is set to the optimal level for the user when they go to bed. It also adjusts the firmness and height of bedding based on the user's body pressure distribution data to provide a more comfortable sleeping environment.

[0480] Sleep tracking and feedback

[0481] The device continuously measures the user's sleep state (time to fall asleep, deep sleep, light sleep, wake-up time, etc.) and sends this data to a server. The server analyzes this data and evaluates the user's sleep quality. Based on the analysis results, it provides the user with feedback on the next day's activities and relaxation methods.

[0482] Specific examples

[0483] A concrete example of the daily routine

[0484] 1. Morning:

[0485] The device measures the user's heart rate and activity level and sends the data to a server.

[0486] The server analyzes the data and determines that the user is in a high stress state.

[0487] 2. Afternoon:

[0488] The server uses a generation AI to generate a relaxation program suitable for the user (e.g., a 10-minute breathing guide) and sends it to the device.

[0489] The terminal notifies the user of the program and prompts the user to execute the program.

[0490] 3. Night:

[0491] The device collects the user's sleep preparation data (heart rate, indoor environment) and sends it to the server.

[0492] The server calculates the recommended room temperature and humidity, and issues instructions to the smart device to adjust the environment.

[0493] 4. Next day morning:

[0494] The terminal collects the user's sleep data and transmits it to the server.

[0495] The server analyzes the user's sleep state and generates feedback.

[0496] The terminal notifies the user of relaxation suggestions for the next day.

[0497] In this way, the system carries out a series of processes, including data collection, analysis, provision of relaxation programs, environmental adjustments, and feedback, to reduce stress and improve the quality of sleep for users.

[0498] The processing flow will be explained below.

[0499] Step 1:

[0500] The terminal (wearable device or sensor) collects data such as the user's heart rate, body temperature, activity level, indoor temperature, humidity, and brightness in real time.

[0501] Step 2:

[0502] The device sends the collected data to the server at regular intervals (e.g., every minute).

[0503] Step 3:

[0504] The server receives the transmitted data and stores it in a database.

[0505] Step 4:

[0506] The server analyzes the stored data and determines the user's stress level.

[0507] For example, if your heart rate is higher than normal, your body temperature is high, or you are less active.

[0508] Step 5:

[0509] Based on the analysis results, the server uses a generation AI to generate a relaxation program suitable for the user.

[0510] Examples include guided meditation audio, guided breathing exercises, relaxation music, etc.

[0511] Step 6:

[0512] The server transmits the generated relaxation program to the terminal.

[0513] Step 7:

[0514] The terminal notifies the user of the received relaxation program and prompts the user to start the program.

[0515] Step 8:

[0516] The terminal provides the content of the relaxation program to the user.

[0517] Examples include playing audio guided breathing exercises, providing guided meditations, playing relaxation music, etc.

[0518] Step 9:

[0519] The server applies machine learning models based on past data and analyzes current data to optimize the user's sleep environment.

[0520] Example: Calculating the optimum room temperature, humidity, brightness, etc.

[0521] Step 10:

[0522] The server generates appropriate configuration instructions for smart devices (e.g., smart thermostats, smart lighting).

[0523] Step 11:

[0524] The terminal receives instructions from the server and sends environment adjustment commands to the associated smart devices.

[0525] Step 12:

[0526] The device continuously records the user's sleep state (time to fall asleep, deep sleep, light sleep, wake-up time, etc.).

[0527] Step 13:

[0528] The device transmits the collected sleep data to the server at regular intervals.

[0529] Step 14:

[0530] The server analyzes the received sleep data and evaluates the quality of the user's sleep.

[0531] Step 15:

[0532] Based on the analysis results, the server generates feedback to the user regarding the next day's activities and relaxation methods.

[0533] Step 16:

[0534] The server transmits the generated feedback to the terminal.

[0535] Step 17:

[0536] The device provides feedback to the user and displays suggestions for the next day's activities and relaxation.

[0537] In this way, the system performs a series of processes to reduce stress for the user and provide an optimal sleeping environment.

[0538] Example 1

[0539] 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."

[0540] In modern society, increased stress and poor sleep environments are causing health problems for many users. Furthermore, there is no integrated system for effectively managing stress and providing an optimal sleep environment. There is a need for a system that collects and analyzes users' biometric and environmental data in real time, and then provides appropriate relaxation programs and environmental adjustments.

[0541] 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.

[0542] In this invention, the server includes a means for collecting biological data and environmental data using a wearable device or a sensor, a means for periodically transmitting the collected data to the server, and a means for analyzing the data received by the server and determining the user's stress level. This allows the user's stress level to be specifically understood, enabling effective stress management.

[0543] The server includes a means for generating a relaxation program suitable for the user using a generative AI model based on the judgment result, and a means for transmitting the generated relaxation program to the terminal and notifying the user. This allows the user to relax at an appropriate time and reduce stress.

[0544] The server includes a means for adjusting the room environment and bedding settings based on past and current data to optimize the user's sleep environment, and a means for measuring the user's sleep state, transmitting the data to the server for analysis, thereby improving the user's sleep quality. The server also includes a means for providing the user with feedback based on the analysis results, allowing the user to take optimal actions for the next day.

[0545] A "wearable device" is an electronic device that can be worn on the body and can measure and collect the user's biometric data in real time.

[0546] A "sensor" is a device that detects and collects environmental and biological data.

[0547] "Biometric data" refers to data that indicates the physiological state of the user, such as heart rate, body temperature, and activity level.

[0548] "Environmental data" refers to data that indicates the state of the physical environment, such as the temperature, humidity, and brightness of the room.

[0549] The "server" is a computer system that oversees data collection, analysis, creation of relaxation programs, and feedback to users.

[0550] A "generative AI model" is a model used to create programs and analyses that are suitable for users using artificial intelligence.

[0551] "Relaxation programs" are programs such as guided meditation audio, breathing exercise guides, and relaxation music aimed at relieving stress in users.

[0552] A "terminal" is a device that a user directly operates or uses to receive information.

[0553] "Sleep environment" refers to the surrounding physical environment (temperature, humidity, brightness, etc.) that allows a user to sleep comfortably.

[0554] "Analysis" is the process of evaluating collected data using statistical or computational methods to derive useful information.

[0555] "Feedback" refers to advice or information provided to the user based on the analysis results.

[0556] System Overview

[0557] This invention is a system for reducing stress and providing an optimal sleep environment for users. The system uses wearable devices and environmental sensors to collect biometric and environmental data, and analyzes stress levels based on that data. Based on the analysis results, a generative AI model is used to provide the user with an optimal relaxation program, and the system further improves the user's sleep quality by optimizing the indoor environment and bedding settings.

[0558] Hardware and software used

[0559] The system's hardware includes wearable devices (e.g., Fitbit, smartwatches) and environmental sensors (e.g., smart thermostats, smart lights). The software uses generative AI models (e.g., OpenAI's GPT) for data analysis and relaxation program generation.

[0560] Data collection and analysis

[0561] Terminals (wearable devices and sensors) collect data in real time, such as the user's heart rate, body temperature, activity level, room temperature, humidity, and brightness. For example, a Fitbit measures the user's heart rate and activity level, while a smart thermostat measures the room temperature. This data is sent to a server at regular intervals, which analyzes it and determines the user's stress level. If the heart rate is higher than normal and the activity level is low, it is determined to be in a state of high stress.

[0562] Creation and provision of relaxation programs

[0563] The server uses a generative AI model to generate a relaxation program based on the analysis results. For example, if the user's stress level is high, a relaxation program including a 10-minute breathing exercise guide is generated. The generated program is sent to the device, which then notifies the user. For example, a notification may appear on the smartphone saying, "A 10-minute breathing exercise guide has been prepared. Start now."

[0564] Adjusting the indoor environment and bedding

[0565] The server optimizes the indoor environment (temperature, humidity, brightness) based on past and current data to optimize the user's sleeping environment. For example, to set the optimal room temperature (22 degrees) before bed, it instructs the smart thermostat to adjust it to 22 degrees. It also provides a more comfortable sleeping environment by adjusting the firmness and height of the smart bed based on the user's body pressure distribution data.

[0566] Sleep tracking and feedback

[0567] The device continuously measures the user's sleep status (time to fall asleep, deep sleep, light sleep, wake-up time, etc.) and sends this data to the server. The server analyzes the data and evaluates the quality of the user's sleep. For example, if the user had little time to get deep sleep, the server generates feedback about the next day's activities and relaxation methods and sends it to the device. The device then notifies the user of the feedback.

[0568] Examples of prompts:

[0569] "Explain how to create an appropriate relaxation program based on a user's heart rate and activity data."

[0570] "How can I use smart devices to optimize my users' sleep environment?"

[0571] keyword

[0572] Generative AI model, prompt sentence

[0573] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0574] System program processing flow

[0575] Step 1: Collect data

[0576] Terminals (wearable devices and environmental sensors) collect biometric and environmental data in real time, such as the user's heart rate, body temperature, activity level, room temperature, humidity, and brightness. The input is the user's biometric and environmental status, and the output is the collected data. For example, a user wearing a smartwatch measures their heart rate every minute, and a smart thermostat measures the room temperature every hour.

[0577] Step 2: Sending data

[0578] The terminal sends the collected data to the server at regular intervals. The input is the data collected by the terminal, and the output is the data sent to the server. For example, it is possible to set the collected data to be sent to the server every morning at 8:00 and evening at 8:00. This transmission ensures that the data is available for the next analysis step.

[0579] Step 3: Analyze the data

[0580] The server analyzes the received data and determines the user's stress level and sleep quality. The input is the biometric and environmental data sent to the server, and the output is the stress level assessment result. For example, it may analyze the data using statistical or computational methods using a Python data analysis library (e.g., Pandas) and determine that a user is in a high-stress state if their heart rate is higher than normal and their activity level is low.

[0581] Step 4: Create a relaxation program

[0582] The server uses a generative AI model based on the analysis results to generate a relaxation program suitable for the user. The input is the stress level assessment result, and the output is the generated relaxation program. For example, the operation includes inputting a prompt such as "Please create a 10-minute breathing exercise guide suitable for when the user is in a high-stress state" into the generative AI model and obtaining the generated program.

[0583] Step 5: Inform and implement a relaxation program

[0584] The server sends the generated relaxation program to the device, and the device notifies the user. The input is the generated relaxation program, and the output is the program notified to the user. For example, this includes an action to display a notification on the smartphone saying, "A 10-minute breathing exercise guide is ready. Start now." The user confirms the notification and runs the program.

[0585] Step 6: Adjust your indoor environment and bedding

[0586] The server issues instructions to optimize the indoor environment (temperature, humidity, brightness) based on past and current data to optimize the user's sleeping environment. The input is past and current data, and the output is instructions for the optimized environment settings. For example, to set the optimal room temperature (22 degrees) before bed, this includes issuing an instruction to the smart thermostat to adjust it to 22 degrees. It also adjusts the firmness and height of the smart bed to provide a more comfortable sleeping environment.

[0587] Step 7: Sleep tracking and feedback

[0588] The device continuously measures the user's sleep state (time to fall asleep, deep sleep, light sleep, wake-up time, etc.) and sends this data to the server. The server analyzes the sent data and evaluates the quality of the user's sleep. The input is the measured sleep data, and the output is an evaluation and feedback based on the analysis results. For example, feedback such as "You got one hour of deep sleep today. To relax even more, try the following meditation technique" is generated and sent to the device. The device notifies the user of the feedback.

[0589] (Application example 1)

[0590] 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."

[0591] Conventional systems have limitations in comprehensively analyzing users' stress levels and sleep quality and providing appropriate relaxation methods. Furthermore, they lack specific solutions for improving customer experience in brick-and-mortar stores. As a result, users lack the means to appropriately reduce stress in their daily lives and in stores, and are unable to create an optimal sleeping environment. This is the issue that needs to be resolved.

[0592] 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.

[0593] In this invention, the server includes: a means for collecting biometric data and environmental data by a wearable device or a sensor;

[0594] means for analyzing the collected data and determining the user's stress level;

[0595] A means for using a generation AI to generate a relaxation program suitable for the user based on the judgment result;

[0596] means for providing the generated relaxation program to a user;

[0597] A means to suggest relaxation services and recommended products according to the user's stress level,

[0598] means for informing the user of proposed relaxation services and products through the smartphone application in order to enhance the user's experience;

[0599] and means for adjusting room climate and bedding settings based on the environmental data to optimize the user's sleep environment.

[0600] This allows users to effectively reduce stress and enjoy an optimal sleeping environment, while also improving the customer experience in physical stores and increasing customer satisfaction within the store.

[0601] A "wearable device" is a device that can collect biometric data when worn by a user.

[0602] A "sensor" is a device for collecting environmental data in real time.

[0603] "Biometric data" refers to data that indicates the user's physical condition, such as heart rate, body temperature, and activity level.

[0604] "Environmental data" refers to data that indicates the state of the environment in which the user is located, such as the temperature, humidity, and brightness of the room.

[0605] "Stress level" is an indicator that indicates the user's current level of stress.

[0606] "Generative AI" is a system that uses artificial intelligence technology to generate optimal suggestions and programs based on specific input data.

[0607] "Relaxation programs" are means such as guided meditation audio, guided breathing exercises, and relaxation music provided to help users reduce stress.

[0608] A "smartphone application" is software that runs on a smartphone and provides notifications and suggestions to the user.

[0609] "Notification" is a means by which the system notifies the user of information or offers.

[0610] The "sleeping environment" refers to the state of the environment that allows the user to sleep comfortably.

[0611] The "indoor environment" refers to the environmental conditions such as temperature, humidity, and brightness in the room where the user spends time.

[0612] "Bedding" refers to items such as beds, mattresses, and pillows that users use when sleeping.

[0613] To implement this invention, it is first necessary to collect biometric data and environmental data of a user using a wearable device or sensor. The biometric data includes heart rate, body temperature, and activity level, and the environmental data includes indoor temperature, humidity, and brightness. This data is collected in real time and transmitted to a server.

[0614] The server then receives and analyzes the biometric and environmental data. Specifically, it analyzes the user's heart rate and activity level to determine the user's stress level. For example, if the heart rate is higher than normal or the activity level is low, it is determined that the user is in a high-stress state.

[0615] After determining the stress level, the server uses a generative AI to generate a relaxation program suited to the user. This relaxation program may include audio guided meditations, breathing exercises, relaxation music, etc. The generated relaxation program is notified to the user via a smartphone application.

[0616] Furthermore, the system suggests optimal relaxation services and recommended products to users based on their stress level. For example, a user who is judged to be in a high stress state may be offered a relaxing experience in a specific area (music, aromatherapy, etc.) or a discount coupon for a specific product. These suggestions are also notified via a smartphone application.

[0617] Before going to sleep, the system adjusts the user's sleep environment to optimize it. Specifically, the room temperature and humidity are adjusted to optimal conditions based on environmental data, and the firmness and height of the bedding are also adjusted based on the user's body pressure distribution data.

[0618] The hardware used includes a smartphone, a heart rate sensor, and an activity tracker, while the software includes a generative AI model using Python and a data collection library (sensor data acquisition module).

[0619] As a specific example, the following case can be considered.

[0620] Examples:

[0621] 1. At 2:00 PM, a customer enters the store wearing a heart rate sensor.

[0622] 2. The application detects that the heart rate is above 90 and determines that the person is in a state of high stress.

[0623] 3. The generative AI generates a 10-minute guided meditation audio and notifies the user via a smartphone app.

[0624] 4. The client follows a guided meditation to achieve relaxation.

[0625] 5. Furthermore, secondary relaxation can be provided by directing people to specific areas with relaxing music.

[0626] Example prompt sentence:

[0627] "The client's heart rate is over 90 and their activity level is low, indicating a high stress state. Please generate an appropriate relaxation program to reduce the client's stress."

[0628] "Generate a relaxing 10-minute guided meditation audio for clients with high stress levels."

[0629] As described above, this system reduces stress and improves the quality of sleep for users, improving the customer experience in physical stores and increasing customer satisfaction within the stores.

[0630] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0631] Step 1:

[0632] Data collection

[0633] Wearable devices worn by users and installed sensors collect biometric and environmental data such as heart rate, body temperature, activity level, indoor temperature, humidity, and brightness.

[0634] Input: Biometric and environmental data from wearable devices and sensors

[0635] Output: Collected biometric and environmental data

[0636] Specific operation: The sensor measures data in real time and transmits the data to the device via Bluetooth, Wi-Fi, etc.

[0637] Step 2:

[0638] Data transmission and analysis

[0639] The device sends the collected data to a server, which analyzes the data and determines the user's stress level.

[0640] Input: Biometric and environmental data sent from the device to the server

[0641] Output: Analyzed stress level judgment result

[0642] Specific operation: The server inputs the received data into an analysis algorithm to detect abnormal values ​​in heart rate and activity level.

[0643] Step 3:

[0644] Creation of relaxation programs

[0645] Based on the stress level assessment results, the server uses a generation AI to generate a relaxation program suitable for the user.

[0646] Input: Stress level judgment result

[0647] Output: Generated relaxation program

[0648] Specific operation: A prompt statement (e.g., "The customer's heart rate is over 90 and their activity level is low, indicating a high level of stress. Please generate an appropriate relaxation program to reduce the customer's stress.") is input into the generative AI model, and a program is generated.

[0649] Step 4:

[0650] Program Offerings and Notifications

[0651] The generated relaxation program is sent to the terminal (smartphone application) and notified to the user.

[0652] Input: Generated relaxation program

[0653] Output: Relaxation program notified to the user

[0654] Specific operation: The server sends program data to the device, and the application displays a push notification.

[0655] Step 5:

[0656] Relaxation services and product proposals

[0657] Depending on the user's stress level, the system will suggest optimal relaxation services and recommended products (such as discount coupons).

[0658] Input: Relaxation program generated by AI, user's current stress level

[0659] Output: Suggested relaxation services and products

[0660] Specific operation: The application displays appropriate service and product suggestions based on the user's stress level.

[0661] Step 6:

[0662] Adjusting the indoor environment and bedding

[0663] While the user is performing the relaxation program or afterwards, the server optimally adjusts the room environment and bedding settings based on the environmental data.

[0664] Input: Historical and current environmental data

[0665] Output: Optimized room environment and bedding settings

[0666] Specific operation: The server analyzes environmental data and sends instructions to the smart thermostat or smart bed to make adjustments.

[0667] Step 7:

[0668] Feedback and analysis results

[0669] The next morning, the device collects the user's sleep data and sends it to the server, which analyzes the data, evaluates the user's sleep quality, generates feedback, and notifies the user.

[0670] Input: Sleep data sent from the device

[0671] Output: Analysis results and feedback

[0672] Specific operation: The server analyzes the data, compiles feedback, sends it to the device, and the application displays it.

[0673] This allows users to receive appropriate relaxation programs, optimize their environment to ensure high quality sleep, and improve their experience in physical stores.

[0674] 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.

[0675] The present invention relates to a system that reduces stress and provides an optimal sleeping environment for users. Furthermore, the system incorporates an emotion engine that recognizes the user's emotions, and performs relaxation programs and environmental adjustments based on the emotions.

[0676] System Overview

[0677] The system uses wearable devices and environmental sensors to collect biometric and environmental data from the user, and also collects emotional data from the user using an emotion engine. The data is then analyzed to determine the user's stress level. Based on the analysis results, generative AI is used to provide the user with an optimal relaxation program, and the system also optimizes the indoor environment and bedding settings to improve the user's sleep quality.

[0678] Data collection and analysis

[0679] The terminal (wearable device or sensor) collects data such as the user's heart rate, body temperature, activity level, room temperature, humidity, and brightness in real time. In addition, an emotion engine is used to collect emotional data in real time from the user's voice and facial expressions. This data is periodically sent to a server, which analyzes the data and determines the user's stress level. For example, if the heart rate is higher than normal or the activity level is low, it is determined to be in a state of high stress. Emotional data is also used to assist in determining the stress level.

[0680] Creation and provision of relaxation programs

[0681] Based on the analysis results, the server generates a relaxation program that takes into account the user's stress level and emotional data. The relaxation program created using generative AI includes audio guided meditation, breathing exercise guides, relaxation music, etc. This program is sent to the device, which notifies the user of the program and encourages them to execute it.

[0682] Adjusting the indoor environment and bedding

[0683] The server optimizes the indoor environment (temperature, humidity, and brightness) based on past data, current environmental data, and emotional data to optimize the user's sleeping environment. For example, it sends instructions to a smart thermostat to check whether the temperature is set to the optimum level for the user when they go to bed. It also adjusts the firmness and height of bedding based on the user's body pressure distribution data to provide a more comfortable sleeping environment.

[0684] Sleep tracking and feedback

[0685] The device continuously measures the user's sleep status (time to fall asleep, deep sleep, light sleep, wake-up time, etc.) and sends this data to the server. The server analyzes this data and evaluates the user's sleep quality. Based on the analysis results, it provides the user with feedback on the next day's activities and relaxation methods.

[0686] Specific examples

[0687] A concrete example of the daily routine

[0688] 1. Morning:

[0689] The device measures the user's heart rate and activity level, collects the user's emotional data using an emotion engine, and sends the data to a server.

[0690] The server analyzes the data and determines that the user is in a high stress state.

[0691] 2. Afternoon:

[0692] The server uses generative AI to generate a relaxation program (e.g., a 10-minute breathing guide) based on the user's stress level and emotional data, and sends it to the device.

[0693] The terminal notifies the user of the program and prompts him to run it.

[0694] 3. Night:

[0695] The device collects the user's sleep preparation data (heart rate, indoor environment, and emotional data) and sends it to the server.

[0696] The server calculates the recommended room temperature and humidity, and issues instructions to the smart device to adjust the environment.

[0697] 4. Next day morning:

[0698] The device collects the user's sleep data and sends it to the server.

[0699] The server analyzes the user's sleep state and generates feedback.

[0700] The device notifies the user of relaxation suggestions for the next day.

[0701] In this way, the system performs a series of processes, from data collection and analysis to providing a relaxation program, adjusting the environment, and providing feedback, thereby reducing stress and improving the quality of sleep for users. At the same time, by utilizing emotional data from the emotion engine, it becomes possible to more accurately assess stress and provide relaxation programs.

[0702] The processing flow will be explained below.

[0703] Step 1:

[0704] The terminal (wearable device or sensor) collects data such as the user's heart rate, body temperature, activity level, indoor temperature, humidity, and brightness in real time.

[0705] Step 2:

[0706] The emotion engine collects emotional data from the user's voice and facial expressions in real time.

[0707] Step 3:

[0708] The device transmits the collected biometric data, environmental data, and emotional data to the server at regular intervals (e.g., every minute).

[0709] Step 4:

[0710] The server receives the transmitted data and stores it in a database.

[0711] Step 5:

[0712] The server analyzes the stored data and determines the user's stress level.

[0713] For example, if your heart rate is higher than normal, your body temperature is high, or your activity level is low, you are considered to be in a high stress state.

[0714] Emotion data from the emotion engine is used as auxiliary information to improve the accuracy of stress judgment.

[0715] Step 6:

[0716] Based on the analysis results, the server uses a generation AI to generate a relaxation program suitable for the user.

[0717] Examples include guided meditation audio, guided breathing exercises, relaxation music, etc.

[0718] Step 7:

[0719] The server transmits the generated relaxation program to the terminal.

[0720] Step 8:

[0721] The terminal notifies the user of the received relaxation program and prompts the user to start the program.

[0722] Step 9:

[0723] The terminal provides the content of the relaxation program to the user.

[0724] Examples include playing audio guided breathing exercises, providing guided meditations, playing relaxation music, etc.

[0725] Step 10:

[0726] The server applies machine learning models based on past data and analyzes current data to optimize the user's sleep environment.

[0727] Example: Calculating the optimum room temperature, humidity, brightness, etc.

[0728] Step 11:

[0729] The server generates appropriate configuration instructions for smart devices (e.g., smart thermostats, smart lighting).

[0730] Step 12:

[0731] The terminal receives instructions from the server and sends environment adjustment commands to the associated smart devices.

[0732] Step 13:

[0733] The device continuously measures the user's sleep state (time to fall asleep, deep sleep, light sleep, wake-up time, etc.).

[0734] Step 14:

[0735] The device transmits the collected sleep data to the server at regular intervals.

[0736] Step 15:

[0737] The server analyzes the received sleep data and evaluates the quality of the user's sleep.

[0738] Step 16:

[0739] Based on the analysis results, the server generates feedback to the user regarding the next day's activities and relaxation methods.

[0740] Step 17:

[0741] The server transmits the generated feedback to the terminal.

[0742] Step 18:

[0743] The device provides feedback to the user and displays suggestions for the next day's activities and relaxation.

[0744] In this way, the system performs a series of processes to reduce the user's stress and provide an optimal sleeping environment. By utilizing emotion data from the emotion engine, it becomes possible to more accurately assess stress and provide relaxation.

[0745] Example 2

[0746] 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."

[0747] In modern society, many people suffer from health problems due to daily stress and inadequate sleep environments. Exposure to stress in particular can impair physical and mental health and reduce quality of life. To solve this problem, it is necessary to accurately grasp users' stress levels, provide appropriate relaxation programs, and optimize their sleep environments. However, there is currently no system that can comprehensively achieve these goals.

[0748] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0749] In this invention, the server includes means for collecting biometric data and environmental data using a wearable device or sensor, means for collecting user emotion data using an emotion engine, means for periodically transmitting the collected data to the server, means for analyzing the transmitted data and determining the user's stress level, means for using a generation AI to generate a relaxation program suitable for the user based on the determination result, means for providing the generated relaxation program to the user, means for adjusting the indoor environment and bedding settings based on the environmental data to optimize the user's sleep environment, means for measuring the user's sleep data and transmitting it to the server, and means for providing feedback to the user based on the analysis result, thereby enabling the user to reduce stress and improve the quality of their sleep.

[0750] A "wearable device" is an electronic device that can be worn by a user and collects biometric data such as heart rate, body temperature, and activity level.

[0751] A "sensor" is a device that measures environmental data and collects data such as temperature, humidity, and brightness.

[0752] The "emotion engine" is a system that analyzes emotions from the user's voice and facial expressions and collects that data.

[0753] "Biometric data" refers to data relating to the user's physical condition, such as the user's heart rate, body temperature, and activity level.

[0754] "Environmental data" refers to data relating to the user's surrounding environment, such as the temperature, humidity, and brightness of the room.

[0755] The "means for determining stress level" refers to an algorithm or program that the server uses to analyze the collected biometric data and emotional data and determine the user's stress state.

[0756] "Generative AI" is an artificial intelligence model that generates a relaxation program suitable for the user based on collected data.

[0757] "Relaxation programs" are means such as guided meditation audio, guided breathing exercises, and relaxation music provided to help users reduce stress.

[0758] A "means for optimizing the sleep environment" is a system for adjusting the room temperature, humidity, lighting, and bedding settings based on environmental data.

[0759] "Sleep data" refers to data relating to the user's sleep state, such as the time it takes for the user to fall asleep, deep sleep, light sleep, and wake-up time.

[0760] The "means for providing feedback" is a system that notifies the user of advice about the next day's activities and relaxation methods based on the analysis results.

[0761] This invention is a system for reducing user stress and providing an optimal sleeping environment. Specifically, it uses wearable devices and environmental sensors to collect biometric and environmental data from the user, and also combines it with an emotion engine to collect the user's emotional data. This data is analyzed to determine the user's stress level, and a generative AI model is used to generate a relaxation program, and the indoor environment and bedding are adjusted accordingly.

[0762] Data collection and analysis

[0763] First, the device (wearable device or sensor) collects data in real time, such as the user's heart rate, body temperature, activity level, room temperature, humidity, and brightness. Furthermore, an emotion engine is used to collect emotional data from the user's voice and facial expressions. This data is continuously sent to a server, which analyzes it and determines the user's stress level. This analysis is carried out using programming languages ​​such as Python and machine learning algorithms.

[0764] Examples:

[0765] While the user is wearing the wearable device at 15:00, the device measures their heart rate and body temperature every minute, and periodically sends the data to a server.

[0766] Example prompt sentence:

[0767] Collect the user's morning heart rate and activity data, as well as emotional data, to determine their stress level.

[0768] Creation and provision of relaxation programs

[0769] The server uses the analysis results, taking into account the user's stress level and emotional data, to generate a relaxation program using a generative AI. The relaxation program generated by the generative AI includes audio guided meditation, breathing guides, relaxation music, etc. This program is sent to the device, which notifies the user and encourages them to carry it out.

[0770] Examples:

[0771] For users who are judged by the server to be in a high stress state, a generative AI model is used to generate a 10-minute audio guided meditation and send it to the user's device.

[0772] Example prompt sentence:

[0773] Generate and notify relaxation programs according to the user's high stress state.

[0774] Adjusting the indoor environment and bedding

[0775] The server adjusts the indoor environment based on the collected environmental and emotional data to optimize the user's sleeping environment. For example, it can set the appropriate room temperature and humidity for the smart thermostat and adjust the brightness of the smart lights. It can also adjust the firmness and height of bedding based on the user's body pressure distribution data to provide a comfortable sleeping environment.

[0776] Examples:

[0777] One hour before the user goes to bed, the server instructs the smart thermostat to set the room temperature to 22 degrees and the humidifier to adjust the humidity to 50%.

[0778] Example prompt sentence:

[0779] Calculate the optimal room temperature and humidity based on the user's pre-sleep data and provide instructions to adjust the indoor environment.

[0780] Sleep data measurement and analysis

[0781] The device measures the user's sleep status (time to fall asleep, deep sleep, light sleep, wake-up time, etc.) and sends the data to a server. The server analyzes this data and evaluates the user's sleep quality. Based on this evaluation, the device provides feedback on the next day's activities and relaxation methods.

[0782] Examples:

[0783] When the user goes to sleep, the device automatically switches to sleep mode and measures their heart rate and body temperature. When they wake up, the data is sent to a server and feedback is generated to assess the quality of their sleep.

[0784] Example prompt sentence:

[0785] Analyze the user's sleep data and generate and notify relaxation suggestions for the next day.

[0786] In this way, the system performs a series of processes, from collecting data and analyzing it to providing a relaxation program, adjusting the environment, and providing feedback, thereby reducing stress and improving the quality of sleep for the user. Furthermore, by utilizing the emotion engine, it is possible to provide more personalized stress assessments and relaxation programs.

[0787] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0788] Step 1:

[0789] Terminals (wearable devices and sensors) collect biometric and environmental data.

[0790] Specific operation: The device collects the user's heart rate, body temperature, activity level, indoor temperature, humidity, and brightness in real time.

[0791] Input: Biometric and environmental data obtained from the user.

[0792] Output: A set of collected data (e.g. heart rate, body temperature, temperature, humidity, etc.).

[0793] Step 2:

[0794] The terminal uses an emotion engine to collect emotion data of the user.

[0795] Specific operation: The device uses its built-in microphone and camera to analyze the user's voice and facial expressions and obtain emotional data.

[0796] Input: User's voice and facial expressions.

[0797] Output: Emotional data (e.g., emotional state or tendency).

[0798] Step 3:

[0799] The terminal periodically transmits the collected data to the server.

[0800] Specific operation: The device uses Wi-Fi or Bluetooth to send data to the server at regular intervals. If the transmission is successful, it proceeds to the next data collection, but if it fails, it retries.

[0801] Input: Collected biometric, environmental, and emotional data.

[0802] Output: The data sent to the server.

[0803] Step 4:

[0804] The server analyzes the received data and determines the user's stress level.

[0805] How it works: The server uses programming languages ​​such as Python to analyze data with machine learning algorithms and evaluate stress levels. For example, if your heart rate is high, your activity level is low, or there is a lot of negative emotional data, it will determine that you are under high stress.

[0806] Input: The dataset sent from the device.

[0807] Output: The user's stress level as a result of the analysis (e.g. high stress, low stress).

[0808] Step 5:

[0809] The server generates a relaxation program using a generative AI model based on the judgment results.

[0810] Specific operation: The server uses the generative AI model to generate a relaxation program (e.g., meditation audio guide, breathing exercise guide) that takes into account the user's stress level and emotional data.

[0811] Input: Stress level analysis results and emotion data.

[0812] Output: Relaxation program (e.g. 10-minute guided meditation).

[0813] Step 6:

[0814] The server transmits the generated relaxation program to the terminal, and the terminal notifies the user and prompts them to execute it.

[0815] Specific operation: The server sends the relaxation program to the terminal, and the terminal displays a notification of the program to the user and a message prompting the user to run it.

[0816] Input: The generated relaxation program.

[0817] Output: The relaxation program notified to the user.

[0818] Step 7:

[0819] The server issues instructions to adjust the room climate and bedding settings based on the environmental data.

[0820] Specific operation: Based on current indoor environment data, the server issues appropriate temperature, humidity, and brightness instructions to smart thermostats and smart lights.

[0821] Input: User's environmental data and sleep preparation data.

[0822] Output: Calibration instructions sent to the smart device.

[0823] Step 8:

[0824] The device measures the user's sleep state and sends the data to a server.

[0825] How it works: The device measures the user's heart rate, body temperature, and activity level from the time they fall asleep until they wake up, and sends this data to a server in the morning.

[0826] Input: User's biometric data while sleeping.

[0827] Output: Measured sleep data.

[0828] Step 9:

[0829] The server analyzes the sleep data and provides feedback to the user.

[0830] Specific operation: The server analyzes the acquired sleep data and evaluates the user's sleep quality. Based on the results, it generates feedback on the user's behavior and relaxation methods for the next day and notifies the user via the device.

[0831] Input: Sleep data.

[0832] Output: The generated feedback and notifications based on it.

[0833] (Application example 2)

[0834] 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."

[0835] Currently, the working environment for staff in brick-and-mortar stores is often stressful, making it difficult to improve staff efficiency and reduce their mental burden. Furthermore, in order to provide comfortable service to customers, it is necessary to properly manage staff health and emotional states. Current technology does not adequately analyze real-time biometric and emotional data, and provide effective relaxation programs based on that data. Therefore, a system that solves these issues is needed.

[0836] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0837] In this invention, the server includes means for collecting biometric data and environmental data using a wearable device or sensor, means for analyzing the collected data and determining the user's stress level, means for using a generation AI to generate a relaxation program suitable for the user based on the determination result, means for providing the generated relaxation program to the user, means for adjusting the facility environment based on the environmental data to optimize the user's working environment, means for analyzing the emotion data of the user and facility users using an emotion engine, and notification means for providing a relaxation program in real time according to the stress level, thereby reducing staff stress, improving work efficiency, and providing comfortable service to customers.

[0838] A "wearable device" is a measuring device that can be worn by a user and has the function of collecting biological data and environmental data.

[0839] A "sensor" is a device that detects physical quantities, and is particularly used to collect environmental data (temperature, humidity, brightness, etc.) and biological data (heart rate, body temperature, etc.).

[0840] "Biometric data" refers to data relating to the user's physical condition, including, for example, heart rate, body temperature, and the like.

[0841] "Environmental data" refers to data relating to the environment surrounding the user, including, for example, temperature, humidity, brightness, and the like.

[0842] "Stress level" is an indicator of the user's psychological and physical stress state.

[0843] "Generative AI" is a system that uses an artificial intelligence model to generate the optimal relaxation program for each user.

[0844] A "relaxation program" is a series of guides, music, and other content designed to promote relaxation in users.

[0845] "Working environment" refers to the place and conditions in which staff work, including in particular factors relating to the comfort and safety of that environment.

[0846] "Facility environment" refers to the entire environment of a physical store or service location, including physical conditions such as temperature, humidity, and brightness.

[0847] The "emotion engine" is a system for analyzing the emotional state of users and facility visitors from their facial expressions and voices.

[0848] "Notification means" refers to a mechanism for conveying information and instructions to the user in real time, and specifically includes the use of smart glasses or a smartphone.

[0849] This invention mainly uses wearable devices, sensors, emotion engines, generative AI, servers, and notification means to create a system that reduces staff stress and optimizes the working environment in physical stores.

[0850] System Overview

[0851] Wearable devices and sensors collect real-time biometric data from staff and store environment data, including heart rate, body temperature, ambient temperature, humidity, and brightness, and periodically transmit this data to a server.

[0852] The server analyzes the collected data and uses generative AI to determine the stress level of staff. If the stress level is high, the server generates an appropriate relaxation program and provides it to the relevant staff member. It also uses an emotion engine to analyze the emotional data of staff and customers, making even more accurate stress assessments.

[0853] Optimizing your environment

[0854] The server adjusts the facility environment based on environmental data to optimize the staff's working environment. For example, if the room temperature is too high, the server will automatically issue instructions to lower the temperature. If the light level is low, the server will adjust the lighting.

[0855] Real-time notifications

[0856] Smart glasses or smartphones are used as notification devices to provide real-time relaxation programs based on stress levels, allowing staff to receive the programs on the spot.

[0857] Specific examples

[0858] For example, imagine a brick-and-mortar store staff wearing smart glasses, which could enable the following daily routine:

[0859] 1. Morning:

[0860] The smart glasses measure the heart rate and body temperature of staff, and an emotion engine analyzes the staff's emotional data, which is then sent to a server.

[0861] Example prompt sentence:

[0862] "Data will now be sent to the server: including staff heart rate, body temperature, store temperature, humidity, and lighting intensity."

[0863] 2. Afternoon:

[0864] The server analyzes the data and determines that the staff member's stress level is high. Using generative AI, it generates a relaxation program suitable for the staff member and notifies the smart glasses.

[0865] Example prompt sentence:

[0866] "We have determined that staff members have high stress levels. We will now play a 10-minute guided breathing program."

[0867] 3. Evening:

[0868] If the environment inside the facility is not comfortable, the server will send instructions to smart devices to adjust the temperature and lighting.

[0869] Example prompt sentence:

[0870] "The temperature is high, please adjust the set temperature to 22 degrees."

[0871] These processes ensure that the working environment for staff is always optimized, reducing stress and enabling the provision of comfortable service to customers.

[0872] Such systems and methods will enable stress management and environmental adjustment in physical stores.

[0873] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0874] Step 1: Data collection

[0875] The terminal uses wearable devices and sensors to collect staff biometric data (heart rate, body temperature, etc.) and store environmental data (temperature, humidity, brightness, etc.) in real time. This data is periodically aggregated in the terminal. The input is various data from the wearable devices and sensors, and the output is a set of collected data.

[0876] Step 2: Send data

[0877] The terminal sends the collected biometric and environmental data to the server. The input is the set of data collected in step 1, and the output is the data received at the server side.

[0878] Step 3: Data analysis

[0879] The server analyzes the received biometric and environmental data. This analysis uses a generative AI model to determine the staff member's stress level from each data point. It also uses an emotion engine to extract and analyze emotional data from the facial expressions and voices of staff and customers. The input is the transmitted data set, and the output is the staff member's stress level and emotional state.

[0880] Step 4: Create a relaxation program

[0881] The server generates a relaxation program suited to each staff member based on the analysis results. In this process, it uses a generative AI to create a program (e.g., meditation audio guide, breathing exercise guide, relaxation music, etc.) that is optimal for each staff member's stress level and emotional state. The input is the analysis results, and the output is the generated relaxation program.

[0882] Step 5: Notification of relaxation program

[0883] The server sends the generated relaxation program to the terminal, which then notifies the staff via smart glasses or a smartphone. The input is the relaxation program, and the output is the program notification received by the staff. Specifically, the program is displayed on the smart glasses or instructions are played back via voice.

[0884] Step 6: Adjusting the environment

[0885] Based on the collected environmental data, the server sends instructions to smart devices to optimize the environment within the facility. For example, if the room temperature is too high, lower the temperature, or if the lights are too dim, increase the brightness. These instructions are transmitted to smart thermostats and smart lights. The input is environmental data, and the output is the adjusted environmental conditions.

[0886] Step 7: Reassessment and feedback

[0887] The terminals continuously collect biometric and environmental data from staff. The server then analyzes this data again, monitors changes in stress levels and emotional states, and provides feedback as needed. The input is a newly collected set of data, and the output is feedback information. Specifically, the system notifies staff of relaxation suggestions and work environment advice for the following day.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] [Third embodiment]

[0892] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0893] 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.

[0894] 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).

[0895] 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.

[0896] 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.

[0897] 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).

[0898] 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.

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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.

[0903] 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."

[0904] The present invention relates to a system for reducing stress in a user and providing an optimal sleeping environment. Specific embodiments of the system will be described below.

[0905] System Overview

[0906] The system uses wearable devices and environmental sensors to collect biometric and environmental data from users, analyzes their stress levels based on that data, and uses generative AI to provide optimal relaxation programs for users based on the analysis results. It also optimizes the indoor environment and bedding settings to improve the quality of users' sleep.

[0907] Data collection and analysis

[0908] The device (wearable device or sensor) collects real-time data such as the user's heart rate, body temperature, activity level, room temperature, humidity, and brightness. This data is periodically sent to a server, which analyzes the data and determines the user's stress level. For example, if the heart rate is higher than normal or the activity level is low, it is determined to be a state of high stress.

[0909] Creation and provision of relaxation programs

[0910] Based on the analysis results, the server generates a relaxation program tailored to the user's stress level. The relaxation program created using the generative AI includes audio guided meditation, breathing exercise guides, relaxation music, etc. This program is sent to the device, which notifies the user of the program and encourages them to execute it.

[0911] Adjusting the indoor environment and bedding

[0912] The server optimizes the indoor environment (temperature, humidity, and brightness) based on past and current environmental data to optimize the user's sleeping environment. For example, it sends instructions to a smart thermostat to check whether the temperature is set to the optimal level for the user when they go to bed. It also adjusts the firmness and height of bedding based on the user's body pressure distribution data to provide a more comfortable sleeping environment.

[0913] Sleep tracking and feedback

[0914] The device continuously measures the user's sleep state (time to fall asleep, deep sleep, light sleep, wake-up time, etc.) and sends this data to a server. The server analyzes this data and evaluates the user's sleep quality. Based on the analysis results, it provides the user with feedback on the next day's activities and relaxation methods.

[0915] Specific examples

[0916] A concrete example of the daily routine

[0917] 1. Morning:

[0918] The device measures the user's heart rate and activity level and sends the data to a server.

[0919] The server analyzes the data and determines that the user is in a high stress state.

[0920] 2. Afternoon:

[0921] The server uses a generation AI to generate a relaxation program suitable for the user (e.g., a 10-minute breathing guide) and sends it to the device.

[0922] The terminal notifies the user of the program and prompts the user to execute the program.

[0923] 3. Night:

[0924] The device collects the user's sleep preparation data (heart rate, indoor environment) and sends it to the server.

[0925] The server calculates the recommended room temperature and humidity, and issues instructions to the smart device to adjust the environment.

[0926] 4. Next day morning:

[0927] The terminal collects the user's sleep data and transmits it to the server.

[0928] The server analyzes the user's sleep state and generates feedback.

[0929] The terminal notifies the user of relaxation suggestions for the next day.

[0930] In this way, the system carries out a series of processes, including data collection, analysis, provision of relaxation programs, environmental adjustments, and feedback, to reduce stress and improve the quality of sleep for users.

[0931] The processing flow will be explained below.

[0932] Step 1:

[0933] The terminal (wearable device or sensor) collects data such as the user's heart rate, body temperature, activity level, indoor temperature, humidity, and brightness in real time.

[0934] Step 2:

[0935] The device sends the collected data to the server at regular intervals (e.g., every minute).

[0936] Step 3:

[0937] The server receives the transmitted data and stores it in a database.

[0938] Step 4:

[0939] The server analyzes the stored data and determines the user's stress level.

[0940] For example, if your heart rate is higher than normal, your body temperature is high, or you are less active.

[0941] Step 5:

[0942] Based on the analysis results, the server uses a generation AI to generate a relaxation program suitable for the user.

[0943] Examples include guided meditation audio, guided breathing exercises, relaxation music, etc.

[0944] Step 6:

[0945] The server transmits the generated relaxation program to the terminal.

[0946] Step 7:

[0947] The terminal notifies the user of the received relaxation program and prompts the user to start the program.

[0948] Step 8:

[0949] The terminal provides the content of the relaxation program to the user.

[0950] Examples include playing audio guided breathing exercises, providing guided meditations, playing relaxation music, etc.

[0951] Step 9:

[0952] The server applies machine learning models based on past data and analyzes current data to optimize the user's sleep environment.

[0953] Example: Calculating the optimum room temperature, humidity, brightness, etc.

[0954] Step 10:

[0955] The server generates appropriate configuration instructions for smart devices (e.g., smart thermostats, smart lighting).

[0956] Step 11:

[0957] The terminal receives instructions from the server and sends environment adjustment commands to the associated smart devices.

[0958] Step 12:

[0959] The device continuously records the user's sleep state (time to fall asleep, deep sleep, light sleep, wake-up time, etc.).

[0960] Step 13:

[0961] The device transmits the collected sleep data to the server at regular intervals.

[0962] Step 14:

[0963] The server analyzes the received sleep data and evaluates the quality of the user's sleep.

[0964] Step 15:

[0965] Based on the analysis results, the server generates feedback to the user regarding the next day's activities and relaxation methods.

[0966] Step 16:

[0967] The server transmits the generated feedback to the terminal.

[0968] Step 17:

[0969] The device provides feedback to the user and displays suggestions for the next day's activities and relaxation.

[0970] In this way, the system performs a series of processes to reduce stress for the user and provide an optimal sleeping environment.

[0971] Example 1

[0972] 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."

[0973] In modern society, increased stress and poor sleep environments are causing health problems for many users. Furthermore, there is no integrated system for effectively managing stress and providing an optimal sleep environment. There is a need for a system that collects and analyzes users' biometric and environmental data in real time, and then provides appropriate relaxation programs and environmental adjustments.

[0974] 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.

[0975] In this invention, the server includes a means for collecting biological data and environmental data using a wearable device or a sensor, a means for periodically transmitting the collected data to the server, and a means for analyzing the data received by the server and determining the user's stress level. This allows the user's stress level to be specifically understood, enabling effective stress management.

[0976] The server includes a means for generating a relaxation program suitable for the user using a generative AI model based on the judgment result, and a means for transmitting the generated relaxation program to the terminal and notifying the user. This allows the user to relax at an appropriate time and reduce stress.

[0977] The server includes a means for adjusting the room environment and bedding settings based on past and current data to optimize the user's sleep environment, and a means for measuring the user's sleep state, transmitting the data to the server for analysis, thereby improving the user's sleep quality. The server also includes a means for providing the user with feedback based on the analysis results, allowing the user to take optimal actions for the next day.

[0978] A "wearable device" is an electronic device that can be worn on the body and can measure and collect the user's biometric data in real time.

[0979] A "sensor" is a device that detects and collects environmental and biological data.

[0980] "Biometric data" refers to data that indicates the physiological state of the user, such as heart rate, body temperature, and activity level.

[0981] "Environmental data" refers to data that indicates the state of the physical environment, such as the temperature, humidity, and brightness of the room.

[0982] The "server" is a computer system that oversees data collection, analysis, creation of relaxation programs, and feedback to users.

[0983] A "generative AI model" is a model used to create programs and analyses that are suitable for users using artificial intelligence.

[0984] "Relaxation programs" are programs such as guided meditation audio, breathing exercise guides, and relaxation music aimed at relieving stress in users.

[0985] A "terminal" is a device that a user directly operates or uses to receive information.

[0986] "Sleep environment" refers to the surrounding physical environment (temperature, humidity, brightness, etc.) that allows a user to sleep comfortably.

[0987] "Analysis" is the process of evaluating collected data using statistical or computational methods to derive useful information.

[0988] "Feedback" refers to advice or information provided to the user based on the analysis results.

[0989] System Overview

[0990] This invention is a system for reducing stress and providing an optimal sleep environment for users. The system uses wearable devices and environmental sensors to collect biometric and environmental data, and analyzes stress levels based on that data. Based on the analysis results, a generative AI model is used to provide the user with an optimal relaxation program, and the system further improves the user's sleep quality by optimizing the indoor environment and bedding settings.

[0991] Hardware and software used

[0992] The system's hardware includes wearable devices (e.g., Fitbit, smartwatches) and environmental sensors (e.g., smart thermostats, smart lights). The software uses generative AI models (e.g., OpenAI's GPT) for data analysis and relaxation program generation.

[0993] Data collection and analysis

[0994] Terminals (wearable devices and sensors) collect data in real time, such as the user's heart rate, body temperature, activity level, room temperature, humidity, and brightness. For example, a Fitbit measures the user's heart rate and activity level, while a smart thermostat measures the room temperature. This data is sent to a server at regular intervals, which analyzes it and determines the user's stress level. If the heart rate is higher than normal and the activity level is low, it is determined to be in a state of high stress.

[0995] Creation and provision of relaxation programs

[0996] The server uses a generative AI model to generate a relaxation program based on the analysis results. For example, if the user's stress level is high, a relaxation program including a 10-minute breathing exercise guide is generated. The generated program is sent to the device, which then notifies the user. For example, a notification may appear on the smartphone saying, "A 10-minute breathing exercise guide has been prepared. Start now."

[0997] Adjusting the indoor environment and bedding

[0998] The server optimizes the indoor environment (temperature, humidity, brightness) based on past and current data to optimize the user's sleeping environment. For example, to set the optimal room temperature (22 degrees) before bed, it instructs the smart thermostat to adjust it to 22 degrees. It also provides a more comfortable sleeping environment by adjusting the firmness and height of the smart bed based on the user's body pressure distribution data.

[0999] Sleep tracking and feedback

[1000] The device continuously measures the user's sleep status (time to fall asleep, deep sleep, light sleep, wake-up time, etc.) and sends this data to the server. The server analyzes the data and evaluates the quality of the user's sleep. For example, if the user had little time to get deep sleep, the server generates feedback about the next day's activities and relaxation methods and sends it to the device. The device then notifies the user of the feedback.

[1001] Examples of prompts:

[1002] "Explain how to create an appropriate relaxation program based on a user's heart rate and activity data."

[1003] "How can I use smart devices to optimize my users' sleep environment?"

[1004] keyword

[1005] Generative AI model, prompt sentence

[1006] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1007] System program processing flow

[1008] Step 1: Collect data

[1009] Terminals (wearable devices and environmental sensors) collect biometric and environmental data in real time, such as the user's heart rate, body temperature, activity level, room temperature, humidity, and brightness. The input is the user's biometric and environmental status, and the output is the collected data. For example, a user wearing a smartwatch measures their heart rate every minute, and a smart thermostat measures the room temperature every hour.

[1010] Step 2: Sending data

[1011] The terminal sends the collected data to the server at regular intervals. The input is the data collected by the terminal, and the output is the data sent to the server. For example, it is possible to set the collected data to be sent to the server every morning at 8:00 and evening at 8:00. This transmission ensures that the data is available for the next analysis step.

[1012] Step 3: Analyze the data

[1013] The server analyzes the received data and determines the user's stress level and sleep quality. The input is the biometric and environmental data sent to the server, and the output is the stress level assessment result. For example, it may analyze the data using statistical or computational methods using a Python data analysis library (e.g., Pandas) and determine that a user is in a high-stress state if their heart rate is higher than normal and their activity level is low.

[1014] Step 4: Create a relaxation program

[1015] The server uses a generative AI model based on the analysis results to generate a relaxation program suitable for the user. The input is the stress level assessment result, and the output is the generated relaxation program. For example, the operation includes inputting a prompt such as "Please create a 10-minute breathing exercise guide suitable for when the user is in a high-stress state" into the generative AI model and obtaining the generated program.

[1016] Step 5: Inform and implement a relaxation program

[1017] The server sends the generated relaxation program to the device, and the device notifies the user. The input is the generated relaxation program, and the output is the program notified to the user. For example, this includes an action to display a notification on the smartphone saying, "A 10-minute breathing exercise guide is ready. Start now." The user confirms the notification and runs the program.

[1018] Step 6: Adjust your indoor environment and bedding

[1019] The server issues instructions to optimize the indoor environment (temperature, humidity, brightness) based on past and current data to optimize the user's sleeping environment. The input is past and current data, and the output is instructions for the optimized environment settings. For example, to set the optimal room temperature (22 degrees) before bed, this includes issuing an instruction to the smart thermostat to adjust it to 22 degrees. It also adjusts the firmness and height of the smart bed to provide a more comfortable sleeping environment.

[1020] Step 7: Sleep tracking and feedback

[1021] The device continuously measures the user's sleep state (time to fall asleep, deep sleep, light sleep, wake-up time, etc.) and sends this data to the server. The server analyzes the sent data and evaluates the quality of the user's sleep. The input is the measured sleep data, and the output is an evaluation and feedback based on the analysis results. For example, feedback such as "You got one hour of deep sleep today. To relax even more, try the following meditation technique" is generated and sent to the device. The device notifies the user of the feedback.

[1022] (Application example 1)

[1023] 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."

[1024] Conventional systems have limitations in comprehensively analyzing users' stress levels and sleep quality and providing appropriate relaxation methods. Furthermore, they lack specific solutions for improving customer experience in brick-and-mortar stores. As a result, users lack the means to appropriately reduce stress in their daily lives and in stores, and are unable to create an optimal sleeping environment. This is the issue that needs to be resolved.

[1025] 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.

[1026] In this invention, the server includes: a means for collecting biometric data and environmental data by a wearable device or a sensor;

[1027] means for analyzing the collected data and determining the user's stress level;

[1028] A means for using a generation AI to generate a relaxation program suitable for the user based on the judgment result;

[1029] means for providing the generated relaxation program to a user;

[1030] A means to suggest relaxation services and recommended products according to the user's stress level,

[1031] means for informing the user of proposed relaxation services and products through the smartphone application in order to enhance the user's experience;

[1032] and means for adjusting room climate and bedding settings based on the environmental data to optimize the user's sleep environment.

[1033] This allows users to effectively reduce stress and enjoy an optimal sleeping environment, while also improving the customer experience in physical stores and increasing customer satisfaction within the store.

[1034] A "wearable device" is a device that can collect biometric data when worn by a user.

[1035] A "sensor" is a device for collecting environmental data in real time.

[1036] "Biometric data" refers to data that indicates the user's physical condition, such as heart rate, body temperature, and activity level.

[1037] "Environmental data" refers to data that indicates the state of the environment in which the user is located, such as the temperature, humidity, and brightness of the room.

[1038] "Stress level" is an indicator that indicates the user's current level of stress.

[1039] "Generative AI" is a system that uses artificial intelligence technology to generate optimal suggestions and programs based on specific input data.

[1040] "Relaxation programs" are means such as guided meditation audio, guided breathing exercises, and relaxation music provided to help users reduce stress.

[1041] A "smartphone application" is software that runs on a smartphone and provides notifications and suggestions to the user.

[1042] "Notification" is a means by which the system notifies the user of information or offers.

[1043] The "sleeping environment" refers to the state of the environment that allows the user to sleep comfortably.

[1044] The "indoor environment" refers to the environmental conditions such as temperature, humidity, and brightness in the room where the user spends time.

[1045] "Bedding" refers to items such as beds, mattresses, and pillows that users use when sleeping.

[1046] To implement this invention, it is first necessary to collect biometric data and environmental data of a user using a wearable device or sensor. The biometric data includes heart rate, body temperature, and activity level, and the environmental data includes indoor temperature, humidity, and brightness. This data is collected in real time and transmitted to a server.

[1047] The server then receives and analyzes the biometric and environmental data. Specifically, it analyzes the user's heart rate and activity level to determine the user's stress level. For example, if the heart rate is higher than normal or the activity level is low, it is determined that the user is in a high-stress state.

[1048] After determining the stress level, the server uses a generative AI to generate a relaxation program suited to the user. This relaxation program may include audio guided meditations, breathing exercises, relaxation music, etc. The generated relaxation program is notified to the user via a smartphone application.

[1049] Furthermore, the system suggests optimal relaxation services and recommended products to users based on their stress level. For example, a user who is judged to be in a high stress state may be offered a relaxing experience in a specific area (music, aromatherapy, etc.) or a discount coupon for a specific product. These suggestions are also notified via a smartphone application.

[1050] Before going to sleep, the system adjusts the user's sleep environment to optimize it. Specifically, the room temperature and humidity are adjusted to optimal conditions based on environmental data, and the firmness and height of the bedding are also adjusted based on the user's body pressure distribution data.

[1051] The hardware used includes a smartphone, a heart rate sensor, and an activity tracker, while the software includes a generative AI model using Python and a data collection library (sensor data acquisition module).

[1052] As a specific example, the following case can be considered.

[1053] Examples:

[1054] 1. At 2:00 PM, a customer enters the store wearing a heart rate sensor.

[1055] 2. The application detects that the heart rate is above 90 and determines that the person is in a state of high stress.

[1056] 3. The generative AI generates a 10-minute guided meditation audio and notifies the user via a smartphone app.

[1057] 4. The client follows a guided meditation to achieve relaxation.

[1058] 5. Furthermore, secondary relaxation can be provided by directing people to specific areas with relaxing music.

[1059] Example prompt sentence:

[1060] "The client's heart rate is over 90 and their activity level is low, indicating a high stress state. Please generate an appropriate relaxation program to reduce the client's stress."

[1061] "Generate a relaxing 10-minute guided meditation audio for clients with high stress levels."

[1062] As described above, this system reduces stress and improves the quality of sleep for users, improving the customer experience in physical stores and increasing customer satisfaction within the stores.

[1063] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1064] Step 1:

[1065] Data collection

[1066] Wearable devices worn by users and installed sensors collect biometric and environmental data such as heart rate, body temperature, activity level, indoor temperature, humidity, and brightness.

[1067] Input: Biometric and environmental data from wearable devices and sensors

[1068] Output: Collected biometric and environmental data

[1069] Specific operation: The sensor measures data in real time and transmits the data to the device via Bluetooth, Wi-Fi, etc.

[1070] Step 2:

[1071] Data transmission and analysis

[1072] The device sends the collected data to a server, which analyzes the data and determines the user's stress level.

[1073] Input: Biometric and environmental data sent from the device to the server

[1074] Output: Analyzed stress level judgment result

[1075] Specific operation: The server inputs the received data into an analysis algorithm to detect abnormal values ​​in heart rate and activity level.

[1076] Step 3:

[1077] Creation of relaxation programs

[1078] Based on the stress level assessment results, the server uses a generation AI to generate a relaxation program suitable for the user.

[1079] Input: Stress level judgment result

[1080] Output: Generated relaxation program

[1081] Specific operation: A prompt statement (e.g., "The customer's heart rate is over 90 and their activity level is low, indicating a high level of stress. Please generate an appropriate relaxation program to reduce the customer's stress.") is input into the generative AI model, and a program is generated.

[1082] Step 4:

[1083] Program Offerings and Notifications

[1084] The generated relaxation program is sent to the terminal (smartphone application) and notified to the user.

[1085] Input: Generated relaxation program

[1086] Output: Relaxation program notified to the user

[1087] Specific operation: The server sends program data to the device, and the application displays a push notification.

[1088] Step 5:

[1089] Relaxation services and product proposals

[1090] Depending on the user's stress level, the system will suggest optimal relaxation services and recommended products (such as discount coupons).

[1091] Input: Relaxation program generated by AI, user's current stress level

[1092] Output: Suggested relaxation services and products

[1093] Specific operation: The application displays appropriate service and product suggestions based on the user's stress level.

[1094] Step 6:

[1095] Adjusting the indoor environment and bedding

[1096] While the user is performing the relaxation program or afterwards, the server optimally adjusts the room environment and bedding settings based on the environmental data.

[1097] Input: Historical and current environmental data

[1098] Output: Optimized room environment and bedding settings

[1099] Specific operation: The server analyzes environmental data and sends instructions to the smart thermostat or smart bed to make adjustments.

[1100] Step 7:

[1101] Feedback and analysis results

[1102] The next morning, the device collects the user's sleep data and sends it to the server, which analyzes the data, evaluates the user's sleep quality, generates feedback, and notifies the user.

[1103] Input: Sleep data sent from the device

[1104] Output: Analysis results and feedback

[1105] Specific operation: The server analyzes the data, compiles feedback, sends it to the device, and the application displays it.

[1106] This allows users to receive appropriate relaxation programs, optimize their environment to ensure high quality sleep, and improve their experience in physical stores.

[1107] 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.

[1108] The present invention relates to a system that reduces stress and provides an optimal sleeping environment for users. Furthermore, the system incorporates an emotion engine that recognizes the user's emotions, and performs relaxation programs and environmental adjustments based on the emotions.

[1109] System Overview

[1110] The system uses wearable devices and environmental sensors to collect biometric and environmental data from the user, and also collects emotional data from the user using an emotion engine. The data is then analyzed to determine the user's stress level. Based on the analysis results, generative AI is used to provide the user with an optimal relaxation program, and the system also optimizes the indoor environment and bedding settings to improve the user's sleep quality.

[1111] Data collection and analysis

[1112] The terminal (wearable device or sensor) collects data such as the user's heart rate, body temperature, activity level, room temperature, humidity, and brightness in real time. In addition, an emotion engine is used to collect emotional data in real time from the user's voice and facial expressions. This data is periodically sent to a server, which analyzes the data and determines the user's stress level. For example, if the heart rate is higher than normal or the activity level is low, it is determined to be in a state of high stress. Emotional data is also used to assist in determining the stress level.

[1113] Creation and provision of relaxation programs

[1114] Based on the analysis results, the server generates a relaxation program that takes into account the user's stress level and emotional data. The relaxation program created using generative AI includes audio guided meditation, breathing exercise guides, relaxation music, etc. This program is sent to the device, which notifies the user of the program and encourages them to execute it.

[1115] Adjusting the indoor environment and bedding

[1116] The server optimizes the indoor environment (temperature, humidity, and brightness) based on past data, current environmental data, and emotional data to optimize the user's sleeping environment. For example, it sends instructions to a smart thermostat to check whether the temperature is set to the optimum level for the user when they go to bed. It also adjusts the firmness and height of bedding based on the user's body pressure distribution data to provide a more comfortable sleeping environment.

[1117] Sleep tracking and feedback

[1118] The device continuously measures the user's sleep status (time to fall asleep, deep sleep, light sleep, wake-up time, etc.) and sends this data to the server. The server analyzes this data and evaluates the user's sleep quality. Based on the analysis results, it provides the user with feedback on the next day's activities and relaxation methods.

[1119] Specific examples

[1120] A concrete example of the daily routine

[1121] 1. Morning:

[1122] The device measures the user's heart rate and activity level, collects the user's emotional data using an emotion engine, and sends the data to a server.

[1123] The server analyzes the data and determines that the user is in a high stress state.

[1124] 2. Afternoon:

[1125] The server uses generative AI to generate a relaxation program (e.g., a 10-minute breathing guide) based on the user's stress level and emotional data, and sends it to the device.

[1126] The terminal notifies the user of the program and prompts him to run it.

[1127] 3. Night:

[1128] The device collects the user's sleep preparation data (heart rate, indoor environment, and emotional data) and sends it to the server.

[1129] The server calculates the recommended room temperature and humidity, and issues instructions to the smart device to adjust the environment.

[1130] 4. Next day morning:

[1131] The device collects the user's sleep data and sends it to the server.

[1132] The server analyzes the user's sleep state and generates feedback.

[1133] The device notifies the user of relaxation suggestions for the next day.

[1134] In this way, the system performs a series of processes, from data collection and analysis to providing a relaxation program, adjusting the environment, and providing feedback, thereby reducing stress and improving the quality of sleep for users. At the same time, by utilizing emotional data from the emotion engine, it becomes possible to more accurately assess stress and provide relaxation programs.

[1135] The processing flow will be explained below.

[1136] Step 1:

[1137] The terminal (wearable device or sensor) collects data such as the user's heart rate, body temperature, activity level, indoor temperature, humidity, and brightness in real time.

[1138] Step 2:

[1139] The emotion engine collects emotional data from the user's voice and facial expressions in real time.

[1140] Step 3:

[1141] The device transmits the collected biometric data, environmental data, and emotional data to the server at regular intervals (e.g., every minute).

[1142] Step 4:

[1143] The server receives the transmitted data and stores it in a database.

[1144] Step 5:

[1145] The server analyzes the stored data and determines the user's stress level.

[1146] For example, if your heart rate is higher than normal, your body temperature is high, or your activity level is low, you are considered to be in a high stress state.

[1147] Emotion data from the emotion engine is used as auxiliary information to improve the accuracy of stress judgment.

[1148] Step 6:

[1149] Based on the analysis results, the server uses a generation AI to generate a relaxation program suitable for the user.

[1150] Examples include guided meditation audio, guided breathing exercises, relaxation music, etc.

[1151] Step 7:

[1152] The server transmits the generated relaxation program to the terminal.

[1153] Step 8:

[1154] The terminal notifies the user of the received relaxation program and prompts the user to start the program.

[1155] Step 9:

[1156] The terminal provides the content of the relaxation program to the user.

[1157] Examples include playing audio guided breathing exercises, providing guided meditations, playing relaxation music, etc.

[1158] Step 10:

[1159] The server applies machine learning models based on past data and analyzes current data to optimize the user's sleep environment.

[1160] Example: Calculating the optimum room temperature, humidity, brightness, etc.

[1161] Step 11:

[1162] The server generates appropriate configuration instructions for smart devices (e.g., smart thermostats, smart lighting).

[1163] Step 12:

[1164] The terminal receives instructions from the server and sends environment adjustment commands to the associated smart devices.

[1165] Step 13:

[1166] The device continuously measures the user's sleep state (time to fall asleep, deep sleep, light sleep, wake-up time, etc.).

[1167] Step 14:

[1168] The device transmits the collected sleep data to the server at regular intervals.

[1169] Step 15:

[1170] The server analyzes the received sleep data and evaluates the quality of the user's sleep.

[1171] Step 16:

[1172] Based on the analysis results, the server generates feedback to the user regarding the next day's activities and relaxation methods.

[1173] Step 17:

[1174] The server transmits the generated feedback to the terminal.

[1175] Step 18:

[1176] The device provides feedback to the user and displays suggestions for the next day's activities and relaxation.

[1177] In this way, the system performs a series of processes to reduce the user's stress and provide an optimal sleeping environment. By utilizing emotion data from the emotion engine, it becomes possible to more accurately assess stress and provide relaxation.

[1178] Example 2

[1179] 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."

[1180] In modern society, many people suffer from health problems due to daily stress and inadequate sleep environments. Exposure to stress in particular can impair physical and mental health and reduce quality of life. To solve this problem, it is necessary to accurately grasp users' stress levels, provide appropriate relaxation programs, and optimize their sleep environments. However, there is currently no system that can comprehensively achieve these goals.

[1181] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1182] In this invention, the server includes means for collecting biometric data and environmental data using a wearable device or sensor, means for collecting user emotion data using an emotion engine, means for periodically transmitting the collected data to the server, means for analyzing the transmitted data and determining the user's stress level, means for using a generation AI to generate a relaxation program suitable for the user based on the determination result, means for providing the generated relaxation program to the user, means for adjusting the indoor environment and bedding settings based on the environmental data to optimize the user's sleep environment, means for measuring the user's sleep data and transmitting it to the server, and means for providing feedback to the user based on the analysis result, thereby enabling the user to reduce stress and improve the quality of their sleep.

[1183] A "wearable device" is an electronic device that can be worn by a user and collects biometric data such as heart rate, body temperature, and activity level.

[1184] A "sensor" is a device that measures environmental data and collects data such as temperature, humidity, and brightness.

[1185] The "emotion engine" is a system that analyzes emotions from the user's voice and facial expressions and collects that data.

[1186] "Biometric data" refers to data relating to the user's physical condition, such as the user's heart rate, body temperature, and activity level.

[1187] "Environmental data" refers to data relating to the user's surrounding environment, such as the temperature, humidity, and brightness of the room.

[1188] The "means for determining stress level" refers to an algorithm or program that the server uses to analyze the collected biometric data and emotional data and determine the user's stress state.

[1189] "Generative AI" is an artificial intelligence model that generates a relaxation program suitable for the user based on collected data.

[1190] "Relaxation programs" are means such as guided meditation audio, guided breathing exercises, and relaxation music provided to help users reduce stress.

[1191] A "means for optimizing the sleep environment" is a system for adjusting the room temperature, humidity, lighting, and bedding settings based on environmental data.

[1192] "Sleep data" refers to data relating to the user's sleep state, such as the time it takes for the user to fall asleep, deep sleep, light sleep, and wake-up time.

[1193] The "means for providing feedback" is a system that notifies the user of advice about the next day's activities and relaxation methods based on the analysis results.

[1194] This invention is a system for reducing user stress and providing an optimal sleeping environment. Specifically, it uses wearable devices and environmental sensors to collect biometric and environmental data from the user, and also combines it with an emotion engine to collect the user's emotional data. This data is analyzed to determine the user's stress level, and a generative AI model is used to generate a relaxation program, and the indoor environment and bedding are adjusted accordingly.

[1195] Data collection and analysis

[1196] First, the device (wearable device or sensor) collects data in real time, such as the user's heart rate, body temperature, activity level, room temperature, humidity, and brightness. Furthermore, an emotion engine is used to collect emotional data from the user's voice and facial expressions. This data is continuously sent to a server, which analyzes it and determines the user's stress level. This analysis is carried out using programming languages ​​such as Python and machine learning algorithms.

[1197] Examples:

[1198] While the user is wearing the wearable device at 15:00, the device measures their heart rate and body temperature every minute, and periodically sends the data to a server.

[1199] Example prompt sentence:

[1200] Collect the user's morning heart rate and activity data, as well as emotional data, to determine their stress level.

[1201] Creation and provision of relaxation programs

[1202] The server uses the analysis results, taking into account the user's stress level and emotional data, to generate a relaxation program using a generative AI. The relaxation program generated by the generative AI includes audio guided meditation, breathing guides, relaxation music, etc. This program is sent to the device, which notifies the user and encourages them to carry it out.

[1203] Examples:

[1204] For users who are judged by the server to be in a high stress state, a generative AI model is used to generate a 10-minute audio guided meditation and send it to the user's device.

[1205] Example prompt sentence:

[1206] Generate and notify relaxation programs according to the user's high stress state.

[1207] Adjusting the indoor environment and bedding

[1208] The server adjusts the indoor environment based on the collected environmental and emotional data to optimize the user's sleeping environment. For example, it can set the appropriate room temperature and humidity for the smart thermostat and adjust the brightness of the smart lights. It can also adjust the firmness and height of bedding based on the user's body pressure distribution data to provide a comfortable sleeping environment.

[1209] Examples:

[1210] One hour before the user goes to bed, the server instructs the smart thermostat to set the room temperature to 22 degrees and the humidifier to adjust the humidity to 50%.

[1211] Example prompt sentence:

[1212] Calculate the optimal room temperature and humidity based on the user's pre-sleep data and provide instructions to adjust the indoor environment.

[1213] Sleep data measurement and analysis

[1214] The device measures the user's sleep status (time to fall asleep, deep sleep, light sleep, wake-up time, etc.) and sends the data to a server. The server analyzes this data and evaluates the user's sleep quality. Based on this evaluation, the device provides feedback on the next day's activities and relaxation methods.

[1215] Examples:

[1216] When the user goes to sleep, the device automatically switches to sleep mode and measures their heart rate and body temperature. When they wake up, the data is sent to a server and feedback is generated to assess the quality of their sleep.

[1217] Example prompt sentence:

[1218] Analyze the user's sleep data and generate and notify relaxation suggestions for the next day.

[1219] In this way, the system performs a series of processes, from collecting data and analyzing it to providing a relaxation program, adjusting the environment, and providing feedback, thereby reducing stress and improving the quality of sleep for the user. Furthermore, by utilizing the emotion engine, it is possible to provide more personalized stress assessments and relaxation programs.

[1220] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1221] Step 1:

[1222] Terminals (wearable devices and sensors) collect biometric and environmental data.

[1223] Specific operation: The device collects the user's heart rate, body temperature, activity level, indoor temperature, humidity, and brightness in real time.

[1224] Input: Biometric and environmental data obtained from the user.

[1225] Output: A set of collected data (e.g. heart rate, body temperature, temperature, humidity, etc.).

[1226] Step 2:

[1227] The terminal uses an emotion engine to collect emotion data of the user.

[1228] Specific operation: The device uses its built-in microphone and camera to analyze the user's voice and facial expressions and obtain emotional data.

[1229] Input: User's voice and facial expressions.

[1230] Output: Emotional data (e.g., emotional state or tendency).

[1231] Step 3:

[1232] The terminal periodically transmits the collected data to the server.

[1233] Specific operation: The device uses Wi-Fi or Bluetooth to send data to the server at regular intervals. If the transmission is successful, it proceeds to the next data collection, but if it fails, it retries.

[1234] Input: Collected biometric, environmental, and emotional data.

[1235] Output: The data sent to the server.

[1236] Step 4:

[1237] The server analyzes the received data and determines the user's stress level.

[1238] How it works: The server uses programming languages ​​such as Python to analyze data with machine learning algorithms and evaluate stress levels. For example, if your heart rate is high, your activity level is low, or there is a lot of negative emotional data, it will determine that you are under high stress.

[1239] Input: The dataset sent from the device.

[1240] Output: The user's stress level as a result of the analysis (e.g. high stress, low stress).

[1241] Step 5:

[1242] The server generates a relaxation program using a generative AI model based on the judgment results.

[1243] Specific operation: The server uses the generative AI model to generate a relaxation program (e.g., meditation audio guide, breathing exercise guide) that takes into account the user's stress level and emotional data.

[1244] Input: Stress level analysis results and emotion data.

[1245] Output: Relaxation program (e.g. 10-minute guided meditation).

[1246] Step 6:

[1247] The server transmits the generated relaxation program to the terminal, and the terminal notifies the user and prompts them to execute it.

[1248] Specific operation: The server sends the relaxation program to the terminal, and the terminal displays a notification of the program to the user and a message prompting the user to run it.

[1249] Input: The generated relaxation program.

[1250] Output: The relaxation program notified to the user.

[1251] Step 7:

[1252] The server issues instructions to adjust the room climate and bedding settings based on the environmental data.

[1253] Specific operation: Based on current indoor environment data, the server issues appropriate temperature, humidity, and brightness instructions to smart thermostats and smart lights.

[1254] Input: User's environmental data and sleep preparation data.

[1255] Output: Calibration instructions sent to the smart device.

[1256] Step 8:

[1257] The device measures the user's sleep state and sends the data to a server.

[1258] How it works: The device measures the user's heart rate, body temperature, and activity level from the time they fall asleep until they wake up, and sends this data to a server in the morning.

[1259] Input: User's biometric data while sleeping.

[1260] Output: Measured sleep data.

[1261] Step 9:

[1262] The server analyzes the sleep data and provides feedback to the user.

[1263] Specific operation: The server analyzes the acquired sleep data and evaluates the user's sleep quality. Based on the results, it generates feedback on the user's behavior and relaxation methods for the next day and notifies the user via the device.

[1264] Input: Sleep data.

[1265] Output: The generated feedback and notifications based on it.

[1266] (Application example 2)

[1267] 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."

[1268] Currently, the working environment for staff in brick-and-mortar stores is often stressful, making it difficult to improve staff efficiency and reduce their mental burden. Furthermore, in order to provide comfortable service to customers, it is necessary to properly manage staff health and emotional states. Current technology does not adequately analyze real-time biometric and emotional data, and provide effective relaxation programs based on that data. Therefore, a system that solves these issues is needed.

[1269] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1270] In this invention, the server includes means for collecting biometric data and environmental data using a wearable device or sensor, means for analyzing the collected data and determining the user's stress level, means for using a generation AI to generate a relaxation program suitable for the user based on the determination result, means for providing the generated relaxation program to the user, means for adjusting the facility environment based on the environmental data to optimize the user's working environment, means for analyzing the emotion data of the user and facility users using an emotion engine, and notification means for providing a relaxation program in real time according to the stress level, thereby reducing staff stress, improving work efficiency, and providing comfortable service to customers.

[1271] A "wearable device" is a measuring device that can be worn by a user and has the function of collecting biological data and environmental data.

[1272] A "sensor" is a device that detects physical quantities, and is particularly used to collect environmental data (temperature, humidity, brightness, etc.) and biological data (heart rate, body temperature, etc.).

[1273] "Biometric data" refers to data relating to the user's physical condition, including, for example, heart rate, body temperature, and the like.

[1274] "Environmental data" refers to data relating to the environment surrounding the user, including, for example, temperature, humidity, brightness, and the like.

[1275] "Stress level" is an indicator of the user's psychological and physical stress state.

[1276] "Generative AI" is a system that uses an artificial intelligence model to generate the optimal relaxation program for each user.

[1277] A "relaxation program" is a series of guides, music, and other content designed to promote relaxation in users.

[1278] "Working environment" refers to the place and conditions in which staff work, including in particular factors relating to the comfort and safety of that environment.

[1279] "Facility environment" refers to the entire environment of a physical store or service location, including physical conditions such as temperature, humidity, and brightness.

[1280] The "emotion engine" is a system for analyzing the emotional state of users and facility visitors from their facial expressions and voices.

[1281] "Notification means" refers to a mechanism for conveying information and instructions to the user in real time, and specifically includes the use of smart glasses or a smartphone.

[1282] This invention mainly uses wearable devices, sensors, emotion engines, generative AI, servers, and notification means to create a system that reduces staff stress and optimizes the working environment in physical stores.

[1283] System Overview

[1284] Wearable devices and sensors collect real-time biometric data from staff and store environment data, including heart rate, body temperature, ambient temperature, humidity, and brightness, and periodically transmit this data to a server.

[1285] The server analyzes the collected data and uses generative AI to determine the stress level of staff. If the stress level is high, the server generates an appropriate relaxation program and provides it to the relevant staff member. It also uses an emotion engine to analyze the emotional data of staff and customers, making even more accurate stress assessments.

[1286] Optimizing your environment

[1287] The server adjusts the facility environment based on environmental data to optimize the staff's working environment. For example, if the room temperature is too high, the server will automatically issue instructions to lower the temperature. If the light level is low, the server will adjust the lighting.

[1288] Real-time notifications

[1289] Smart glasses or smartphones are used as notification devices to provide real-time relaxation programs based on stress levels, allowing staff to receive the programs on the spot.

[1290] Specific examples

[1291] For example, imagine a brick-and-mortar store staff wearing smart glasses, which could enable the following daily routine:

[1292] 1. Morning:

[1293] The smart glasses measure the heart rate and body temperature of staff, and an emotion engine analyzes the staff's emotional data, which is then sent to a server.

[1294] Example prompt sentence:

[1295] "Data will now be sent to the server: including staff heart rate, body temperature, store temperature, humidity, and lighting intensity."

[1296] 2. Afternoon:

[1297] The server analyzes the data and determines that the staff member's stress level is high. Using generative AI, it generates a relaxation program suitable for the staff member and notifies the smart glasses.

[1298] Example prompt sentence:

[1299] "We have determined that staff members have high stress levels. We will now play a 10-minute guided breathing program."

[1300] 3. Evening:

[1301] If the environment inside the facility is not comfortable, the server will send instructions to smart devices to adjust the temperature and lighting.

[1302] Example prompt sentence:

[1303] "The temperature is high, please adjust the set temperature to 22 degrees."

[1304] These processes ensure that the working environment for staff is always optimized, reducing stress and enabling the provision of comfortable service to customers.

[1305] Such systems and methods will enable stress management and environmental adjustment in physical stores.

[1306] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1307] Step 1: Data collection

[1308] The terminal uses wearable devices and sensors to collect staff biometric data (heart rate, body temperature, etc.) and store environmental data (temperature, humidity, brightness, etc.) in real time. This data is periodically aggregated in the terminal. The input is various data from the wearable devices and sensors, and the output is a set of collected data.

[1309] Step 2: Send data

[1310] The terminal sends the collected biometric and environmental data to the server. The input is the set of data collected in step 1, and the output is the data received at the server side.

[1311] Step 3: Data analysis

[1312] The server analyzes the received biometric and environmental data. This analysis uses a generative AI model to determine the staff member's stress level from each data point. It also uses an emotion engine to extract and analyze emotional data from the facial expressions and voices of staff and customers. The input is the transmitted data set, and the output is the staff member's stress level and emotional state.

[1313] Step 4: Create a relaxation program

[1314] The server generates a relaxation program suited to each staff member based on the analysis results. In this process, it uses a generative AI to create a program (e.g., meditation audio guide, breathing exercise guide, relaxation music, etc.) that is optimal for each staff member's stress level and emotional state. The input is the analysis results, and the output is the generated relaxation program.

[1315] Step 5: Notification of relaxation program

[1316] The server sends the generated relaxation program to the terminal, which then notifies the staff via smart glasses or a smartphone. The input is the relaxation program, and the output is the program notification received by the staff. Specifically, the program is displayed on the smart glasses or instructions are played back via voice.

[1317] Step 6: Adjusting the environment

[1318] Based on the collected environmental data, the server sends instructions to smart devices to optimize the environment within the facility. For example, if the room temperature is too high, lower the temperature, or if the lights are too dim, increase the brightness. These instructions are transmitted to smart thermostats and smart lights. The input is environmental data, and the output is the adjusted environmental conditions.

[1319] Step 7: Reassessment and feedback

[1320] The terminals continuously collect biometric and environmental data from staff. The server then analyzes this data again, monitors changes in stress levels and emotional states, and provides feedback as needed. The input is a newly collected set of data, and the output is feedback information. Specifically, the system notifies staff of relaxation suggestions and work environment advice for the following day.

[1321] 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.

[1322] 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.

[1323] 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.

[1324] [Fourth embodiment]

[1325] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1326] 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.

[1327] 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).

[1328] 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.

[1329] 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.

[1330] 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).

[1331] 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.

[1332] 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.

[1333] 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.

[1334] 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.

[1335] 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.

[1336] 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.

[1337] 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."

[1338] The present invention relates to a system for reducing stress in a user and providing an optimal sleeping environment. Specific embodiments of the system will be described below.

[1339] System Overview

[1340] The system uses wearable devices and environmental sensors to collect biometric and environmental data from users, analyzes their stress levels based on that data, and uses generative AI to provide optimal relaxation programs for users based on the analysis results. It also optimizes the indoor environment and bedding settings to improve the quality of users' sleep.

[1341] Data collection and analysis

[1342] The device (wearable device or sensor) collects real-time data such as the user's heart rate, body temperature, activity level, room temperature, humidity, and brightness. This data is periodically sent to a server, which analyzes the data and determines the user's stress level. For example, if the heart rate is higher than normal or the activity level is low, it is determined to be a state of high stress.

[1343] Creation and provision of relaxation programs

[1344] Based on the analysis results, the server generates a relaxation program tailored to the user's stress level. The relaxation program created using the generative AI includes audio guided meditation, breathing exercise guides, relaxation music, etc. This program is sent to the device, which notifies the user of the program and encourages them to execute it.

[1345] Adjusting the indoor environment and bedding

[1346] The server optimizes the indoor environment (temperature, humidity, and brightness) based on past and current environmental data to optimize the user's sleeping environment. For example, it sends instructions to a smart thermostat to check whether the temperature is set to the optimal level for the user when they go to bed. It also adjusts the firmness and height of bedding based on the user's body pressure distribution data to provide a more comfortable sleeping environment.

[1347] Sleep tracking and feedback

[1348] The device continuously measures the user's sleep state (time to fall asleep, deep sleep, light sleep, wake-up time, etc.) and sends this data to a server. The server analyzes this data and evaluates the user's sleep quality. Based on the analysis results, it provides the user with feedback on the next day's activities and relaxation methods.

[1349] Specific examples

[1350] A concrete example of the daily routine

[1351] 1. Morning:

[1352] The device measures the user's heart rate and activity level and sends the data to a server.

[1353] The server analyzes the data and determines that the user is in a high stress state.

[1354] 2. Afternoon:

[1355] The server uses a generation AI to generate a relaxation program suitable for the user (e.g., a 10-minute breathing guide) and sends it to the device.

[1356] The terminal notifies the user of the program and prompts the user to execute the program.

[1357] 3. Night:

[1358] The device collects the user's sleep preparation data (heart rate, indoor environment) and sends it to the server.

[1359] The server calculates the recommended room temperature and humidity, and issues instructions to the smart device to adjust the environment.

[1360] 4. Next day morning:

[1361] The terminal collects the user's sleep data and transmits it to the server.

[1362] The server analyzes the user's sleep state and generates feedback.

[1363] The terminal notifies the user of relaxation suggestions for the next day.

[1364] In this way, the system carries out a series of processes, including data collection, analysis, provision of relaxation programs, environmental adjustments, and feedback, to reduce stress and improve the quality of sleep for users.

[1365] The processing flow will be explained below.

[1366] Step 1:

[1367] The terminal (wearable device or sensor) collects data such as the user's heart rate, body temperature, activity level, indoor temperature, humidity, and brightness in real time.

[1368] Step 2:

[1369] The device sends the collected data to the server at regular intervals (e.g., every minute).

[1370] Step 3:

[1371] The server receives the transmitted data and stores it in a database.

[1372] Step 4:

[1373] The server analyzes the stored data and determines the user's stress level.

[1374] For example, if your heart rate is higher than normal, your body temperature is high, or you are less active.

[1375] Step 5:

[1376] Based on the analysis results, the server uses a generation AI to generate a relaxation program suitable for the user.

[1377] Examples include guided meditation audio, guided breathing exercises, relaxation music, etc.

[1378] Step 6:

[1379] The server transmits the generated relaxation program to the terminal.

[1380] Step 7:

[1381] The terminal notifies the user of the received relaxation program and prompts the user to start the program.

[1382] Step 8:

[1383] The terminal provides the content of the relaxation program to the user.

[1384] Examples include playing audio guided breathing exercises, providing guided meditations, playing relaxation music, etc.

[1385] Step 9:

[1386] The server applies machine learning models based on past data and analyzes current data to optimize the user's sleep environment.

[1387] Example: Calculating the optimum room temperature, humidity, brightness, etc.

[1388] Step 10:

[1389] The server generates appropriate configuration instructions for smart devices (e.g., smart thermostats, smart lighting).

[1390] Step 11:

[1391] The terminal receives instructions from the server and sends environment adjustment commands to the associated smart devices.

[1392] Step 12:

[1393] The device continuously records the user's sleep state (time to fall asleep, deep sleep, light sleep, wake-up time, etc.).

[1394] Step 13:

[1395] The device transmits the collected sleep data to the server at regular intervals.

[1396] Step 14:

[1397] The server analyzes the received sleep data and evaluates the quality of the user's sleep.

[1398] Step 15:

[1399] Based on the analysis results, the server generates feedback to the user regarding the next day's activities and relaxation methods.

[1400] Step 16:

[1401] The server transmits the generated feedback to the terminal.

[1402] Step 17:

[1403] The device provides feedback to the user and displays suggestions for the next day's activities and relaxation.

[1404] In this way, the system performs a series of processes to reduce stress for the user and provide an optimal sleeping environment.

[1405] Example 1

[1406] 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."

[1407] In modern society, increased stress and poor sleep environments are causing health problems for many users. Furthermore, there is no integrated system for effectively managing stress and providing an optimal sleep environment. There is a need for a system that collects and analyzes users' biometric and environmental data in real time, and then provides appropriate relaxation programs and environmental adjustments.

[1408] 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.

[1409] In this invention, the server includes a means for collecting biological data and environmental data using a wearable device or a sensor, a means for periodically transmitting the collected data to the server, and a means for analyzing the data received by the server and determining the user's stress level. This allows the user's stress level to be specifically understood, enabling effective stress management.

[1410] The server includes a means for generating a relaxation program suitable for the user using a generative AI model based on the judgment result, and a means for transmitting the generated relaxation program to the terminal and notifying the user. This allows the user to relax at an appropriate time and reduce stress.

[1411] The server includes a means for adjusting the room environment and bedding settings based on past and current data to optimize the user's sleep environment, and a means for measuring the user's sleep state, transmitting the data to the server for analysis, thereby improving the user's sleep quality. The server also includes a means for providing the user with feedback based on the analysis results, allowing the user to take optimal actions for the next day.

[1412] A "wearable device" is an electronic device that can be worn on the body and can measure and collect the user's biometric data in real time.

[1413] A "sensor" is a device that detects and collects environmental and biological data.

[1414] "Biometric data" refers to data that indicates the physiological state of the user, such as heart rate, body temperature, and activity level.

[1415] "Environmental data" refers to data that indicates the state of the physical environment, such as the temperature, humidity, and brightness of the room.

[1416] The "server" is a computer system that oversees data collection, analysis, creation of relaxation programs, and feedback to users.

[1417] A "generative AI model" is a model used to create programs and analyses that are suitable for users using artificial intelligence.

[1418] "Relaxation programs" are programs such as guided meditation audio, breathing exercise guides, and relaxation music aimed at relieving stress in users.

[1419] A "terminal" is a device that a user directly operates or uses to receive information.

[1420] "Sleep environment" refers to the surrounding physical environment (temperature, humidity, brightness, etc.) that allows a user to sleep comfortably.

[1421] "Analysis" is the process of evaluating collected data using statistical or computational methods to derive useful information.

[1422] "Feedback" refers to advice or information provided to the user based on the analysis results.

[1423] System Overview

[1424] This invention is a system for reducing stress and providing an optimal sleep environment for users. The system uses wearable devices and environmental sensors to collect biometric and environmental data, and analyzes stress levels based on that data. Based on the analysis results, a generative AI model is used to provide the user with an optimal relaxation program, and the system further improves the user's sleep quality by optimizing the indoor environment and bedding settings.

[1425] Hardware and software used

[1426] The system's hardware includes wearable devices (e.g., Fitbit, smartwatches) and environmental sensors (e.g., smart thermostats, smart lights). The software uses generative AI models (e.g., OpenAI's GPT) for data analysis and relaxation program generation.

[1427] Data collection and analysis

[1428] Terminals (wearable devices and sensors) collect data in real time, such as the user's heart rate, body temperature, activity level, room temperature, humidity, and brightness. For example, a Fitbit measures the user's heart rate and activity level, while a smart thermostat measures the room temperature. This data is sent to a server at regular intervals, which analyzes it and determines the user's stress level. If the heart rate is higher than normal and the activity level is low, it is determined to be in a state of high stress.

[1429] Creation and provision of relaxation programs

[1430] The server uses a generative AI model to generate a relaxation program based on the analysis results. For example, if the user's stress level is high, a relaxation program including a 10-minute breathing exercise guide is generated. The generated program is sent to the device, which then notifies the user. For example, a notification may appear on the smartphone saying, "A 10-minute breathing exercise guide has been prepared. Start now."

[1431] Adjusting the indoor environment and bedding

[1432] The server optimizes the indoor environment (temperature, humidity, brightness) based on past and current data to optimize the user's sleeping environment. For example, to set the optimal room temperature (22 degrees) before bed, it instructs the smart thermostat to adjust it to 22 degrees. It also provides a more comfortable sleeping environment by adjusting the firmness and height of the smart bed based on the user's body pressure distribution data.

[1433] Sleep tracking and feedback

[1434] The device continuously measures the user's sleep status (time to fall asleep, deep sleep, light sleep, wake-up time, etc.) and sends this data to the server. The server analyzes the data and evaluates the quality of the user's sleep. For example, if the user had little time to get deep sleep, the server generates feedback about the next day's activities and relaxation methods and sends it to the device. The device then notifies the user of the feedback.

[1435] Examples of prompts:

[1436] "Explain how to create an appropriate relaxation program based on a user's heart rate and activity data."

[1437] "How can I use smart devices to optimize my users' sleep environment?"

[1438] keyword

[1439] Generative AI model, prompt sentence

[1440] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1441] System program processing flow

[1442] Step 1: Collect data

[1443] Terminals (wearable devices and environmental sensors) collect biometric and environmental data in real time, such as the user's heart rate, body temperature, activity level, room temperature, humidity, and brightness. The input is the user's biometric and environmental status, and the output is the collected data. For example, a user wearing a smartwatch measures their heart rate every minute, and a smart thermostat measures the room temperature every hour.

[1444] Step 2: Sending data

[1445] The terminal sends the collected data to the server at regular intervals. The input is the data collected by the terminal, and the output is the data sent to the server. For example, it is possible to set the collected data to be sent to the server every morning at 8:00 and evening at 8:00. This transmission ensures that the data is available for the next analysis step.

[1446] Step 3: Analyze the data

[1447] The server analyzes the received data and determines the user's stress level and sleep quality. The input is the biometric and environmental data sent to the server, and the output is the stress level assessment result. For example, it may analyze the data using statistical or computational methods using a Python data analysis library (e.g., Pandas) and determine that a user is in a high-stress state if their heart rate is higher than normal and their activity level is low.

[1448] Step 4: Create a relaxation program

[1449] The server uses a generative AI model based on the analysis results to generate a relaxation program suitable for the user. The input is the stress level assessment result, and the output is the generated relaxation program. For example, the operation includes inputting a prompt such as "Please create a 10-minute breathing exercise guide suitable for when the user is in a high-stress state" into the generative AI model and obtaining the generated program.

[1450] Step 5: Inform and implement a relaxation program

[1451] The server sends the generated relaxation program to the device, and the device notifies the user. The input is the generated relaxation program, and the output is the program notified to the user. For example, this includes an action to display a notification on the smartphone saying, "A 10-minute breathing exercise guide is ready. Start now." The user confirms the notification and runs the program.

[1452] Step 6: Adjust your indoor environment and bedding

[1453] The server issues instructions to optimize the indoor environment (temperature, humidity, brightness) based on past and current data to optimize the user's sleeping environment. The input is past and current data, and the output is instructions for the optimized environment settings. For example, to set the optimal room temperature (22 degrees) before bed, this includes issuing an instruction to the smart thermostat to adjust it to 22 degrees. It also adjusts the firmness and height of the smart bed to provide a more comfortable sleeping environment.

[1454] Step 7: Sleep tracking and feedback

[1455] The device continuously measures the user's sleep state (time to fall asleep, deep sleep, light sleep, wake-up time, etc.) and sends this data to the server. The server analyzes the sent data and evaluates the quality of the user's sleep. The input is the measured sleep data, and the output is an evaluation and feedback based on the analysis results. For example, feedback such as "You got one hour of deep sleep today. To relax even more, try the following meditation technique" is generated and sent to the device. The device notifies the user of the feedback.

[1456] (Application example 1)

[1457] 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."

[1458] Conventional systems have limitations in comprehensively analyzing users' stress levels and sleep quality and providing appropriate relaxation methods. Furthermore, they lack specific solutions for improving customer experience in brick-and-mortar stores. As a result, users lack the means to appropriately reduce stress in their daily lives and in stores, and are unable to create an optimal sleeping environment. This is the issue that needs to be resolved.

[1459] 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.

[1460] In this invention, the server includes: a means for collecting biometric data and environmental data by a wearable device or a sensor;

[1461] means for analyzing the collected data and determining the user's stress level;

[1462] A means for using a generation AI to generate a relaxation program suitable for the user based on the judgment result;

[1463] means for providing the generated relaxation program to a user;

[1464] A means to suggest relaxation services and recommended products according to the user's stress level,

[1465] means for informing the user of proposed relaxation services and products through the smartphone application in order to enhance the user's experience;

[1466] and means for adjusting room climate and bedding settings based on the environmental data to optimize the user's sleep environment.

[1467] This allows users to effectively reduce stress and enjoy an optimal sleeping environment, while also improving the customer experience in physical stores and increasing customer satisfaction within the store.

[1468] A "wearable device" is a device that can collect biometric data when worn by a user.

[1469] A "sensor" is a device for collecting environmental data in real time.

[1470] "Biometric data" refers to data that indicates the user's physical condition, such as heart rate, body temperature, and activity level.

[1471] "Environmental data" refers to data that indicates the state of the environment in which the user is located, such as the temperature, humidity, and brightness of the room.

[1472] "Stress level" is an indicator that indicates the user's current level of stress.

[1473] "Generative AI" is a system that uses artificial intelligence technology to generate optimal suggestions and programs based on specific input data.

[1474] "Relaxation programs" are means such as guided meditation audio, guided breathing exercises, and relaxation music provided to help users reduce stress.

[1475] A "smartphone application" is software that runs on a smartphone and provides notifications and suggestions to the user.

[1476] "Notification" is a means by which the system notifies the user of information or offers.

[1477] The "sleeping environment" refers to the state of the environment that allows the user to sleep comfortably.

[1478] The "indoor environment" refers to the environmental conditions such as temperature, humidity, and brightness in the room where the user spends time.

[1479] "Bedding" refers to items such as beds, mattresses, and pillows that users use when sleeping.

[1480] To implement this invention, it is first necessary to collect biometric data and environmental data of a user using a wearable device or sensor. The biometric data includes heart rate, body temperature, and activity level, and the environmental data includes indoor temperature, humidity, and brightness. This data is collected in real time and transmitted to a server.

[1481] The server then receives and analyzes the biometric and environmental data. Specifically, it analyzes the user's heart rate and activity level to determine the user's stress level. For example, if the heart rate is higher than normal or the activity level is low, it is determined that the user is in a high-stress state.

[1482] After determining the stress level, the server uses a generative AI to generate a relaxation program suited to the user. This relaxation program may include audio guided meditations, breathing exercises, relaxation music, etc. The generated relaxation program is notified to the user via a smartphone application.

[1483] Furthermore, the system suggests optimal relaxation services and recommended products to users based on their stress level. For example, a user who is judged to be in a high stress state may be offered a relaxing experience in a specific area (music, aromatherapy, etc.) or a discount coupon for a specific product. These suggestions are also notified via a smartphone application.

[1484] Before going to sleep, the system adjusts the user's sleep environment to optimize it. Specifically, the room temperature and humidity are adjusted to optimal conditions based on environmental data, and the firmness and height of the bedding are also adjusted based on the user's body pressure distribution data.

[1485] The hardware used includes a smartphone, a heart rate sensor, and an activity tracker, while the software includes a generative AI model using Python and a data collection library (sensor data acquisition module).

[1486] As a specific example, the following case can be considered.

[1487] Examples:

[1488] 1. At 2:00 PM, a customer enters the store wearing a heart rate sensor.

[1489] 2. The application detects that the heart rate is above 90 and determines that the person is in a state of high stress.

[1490] 3. The generative AI generates a 10-minute guided meditation audio and notifies the user via a smartphone app.

[1491] 4. The client follows a guided meditation to achieve relaxation.

[1492] 5. Furthermore, secondary relaxation can be provided by directing people to specific areas with relaxing music.

[1493] Example prompt sentence:

[1494] "The client's heart rate is over 90 and their activity level is low, indicating a high stress state. Please generate an appropriate relaxation program to reduce the client's stress."

[1495] "Generate a relaxing 10-minute guided meditation audio for clients with high stress levels."

[1496] As described above, this system reduces stress and improves the quality of sleep for users, improving the customer experience in physical stores and increasing customer satisfaction within the stores.

[1497] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1498] Step 1:

[1499] Data collection

[1500] Wearable devices worn by users and installed sensors collect biometric and environmental data such as heart rate, body temperature, activity level, indoor temperature, humidity, and brightness.

[1501] Input: Biometric and environmental data from wearable devices and sensors

[1502] Output: Collected biometric and environmental data

[1503] Specific operation: The sensor measures data in real time and transmits the data to the device via Bluetooth, Wi-Fi, etc.

[1504] Step 2:

[1505] Data transmission and analysis

[1506] The device sends the collected data to a server, which analyzes the data and determines the user's stress level.

[1507] Input: Biometric and environmental data sent from the device to the server

[1508] Output: Analyzed stress level judgment result

[1509] Specific operation: The server inputs the received data into an analysis algorithm to detect abnormal values ​​in heart rate and activity level.

[1510] Step 3:

[1511] Creation of relaxation programs

[1512] Based on the stress level assessment results, the server uses a generation AI to generate a relaxation program suitable for the user.

[1513] Input: Stress level judgment result

[1514] Output: Generated relaxation program

[1515] Specific operation: A prompt statement (e.g., "The customer's heart rate is over 90 and their activity level is low, indicating a high level of stress. Please generate an appropriate relaxation program to reduce the customer's stress.") is input into the generative AI model, and a program is generated.

[1516] Step 4:

[1517] Program Offerings and Notifications

[1518] The generated relaxation program is sent to the terminal (smartphone application) and notified to the user.

[1519] Input: Generated relaxation program

[1520] Output: Relaxation program notified to the user

[1521] Specific operation: The server sends program data to the device, and the application displays a push notification.

[1522] Step 5:

[1523] Relaxation services and product proposals

[1524] Depending on the user's stress level, the system will suggest optimal relaxation services and recommended products (such as discount coupons).

[1525] Input: Relaxation program generated by AI, user's current stress level

[1526] Output: Suggested relaxation services and products

[1527] Specific operation: The application displays appropriate service and product suggestions based on the user's stress level.

[1528] Step 6:

[1529] Adjusting the indoor environment and bedding

[1530] While the user is performing the relaxation program or afterwards, the server optimally adjusts the room environment and bedding settings based on the environmental data.

[1531] Input: Historical and current environmental data

[1532] Output: Optimized room environment and bedding settings

[1533] Specific operation: The server analyzes environmental data and sends instructions to the smart thermostat or smart bed to make adjustments.

[1534] Step 7:

[1535] Feedback and analysis results

[1536] The next morning, the device collects the user's sleep data and sends it to the server, which analyzes the data, evaluates the user's sleep quality, generates feedback, and notifies the user.

[1537] Input: Sleep data sent from the device

[1538] Output: Analysis results and feedback

[1539] Specific operation: The server analyzes the data, compiles feedback, sends it to the device, and the application displays it.

[1540] This allows users to receive appropriate relaxation programs, optimize their environment to ensure high quality sleep, and improve their experience in physical stores.

[1541] 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.

[1542] The present invention relates to a system that reduces stress and provides an optimal sleeping environment for users. Furthermore, the system incorporates an emotion engine that recognizes the user's emotions, and performs relaxation programs and environmental adjustments based on the emotions.

[1543] System Overview

[1544] The system uses wearable devices and environmental sensors to collect biometric and environmental data from the user, and also collects emotional data from the user using an emotion engine. The data is then analyzed to determine the user's stress level. Based on the analysis results, generative AI is used to provide the user with an optimal relaxation program, and the system also optimizes the indoor environment and bedding settings to improve the user's sleep quality.

[1545] Data collection and analysis

[1546] The terminal (wearable device or sensor) collects data such as the user's heart rate, body temperature, activity level, room temperature, humidity, and brightness in real time. In addition, an emotion engine is used to collect emotional data in real time from the user's voice and facial expressions. This data is periodically sent to a server, which analyzes the data and determines the user's stress level. For example, if the heart rate is higher than normal or the activity level is low, it is determined to be in a state of high stress. Emotional data is also used to assist in determining the stress level.

[1547] Creation and provision of relaxation programs

[1548] Based on the analysis results, the server generates a relaxation program that takes into account the user's stress level and emotional data. The relaxation program created using generative AI includes audio guided meditation, breathing exercise guides, relaxation music, etc. This program is sent to the device, which notifies the user of the program and encourages them to execute it.

[1549] Adjusting the indoor environment and bedding

[1550] The server optimizes the indoor environment (temperature, humidity, and brightness) based on past data, current environmental data, and emotional data to optimize the user's sleeping environment. For example, it sends instructions to a smart thermostat to check whether the temperature is set to the optimum level for the user when they go to bed. It also adjusts the firmness and height of bedding based on the user's body pressure distribution data to provide a more comfortable sleeping environment.

[1551] Sleep tracking and feedback

[1552] The device continuously measures the user's sleep status (time to fall asleep, deep sleep, light sleep, wake-up time, etc.) and sends this data to the server. The server analyzes this data and evaluates the user's sleep quality. Based on the analysis results, it provides the user with feedback on the next day's activities and relaxation methods.

[1553] Specific examples

[1554] A concrete example of the daily routine

[1555] 1. Morning:

[1556] The device measures the user's heart rate and activity level, collects the user's emotional data using an emotion engine, and sends the data to a server.

[1557] The server analyzes the data and determines that the user is in a high stress state.

[1558] 2. Afternoon:

[1559] The server uses generative AI to generate a relaxation program (e.g., a 10-minute breathing guide) based on the user's stress level and emotional data, and sends it to the device.

[1560] The terminal notifies the user of the program and prompts him to run it.

[1561] 3. Night:

[1562] The device collects the user's sleep preparation data (heart rate, indoor environment, and emotional data) and sends it to the server.

[1563] The server calculates the recommended room temperature and humidity, and issues instructions to the smart device to adjust the environment.

[1564] 4. Next day morning:

[1565] The device collects the user's sleep data and sends it to the server.

[1566] The server analyzes the user's sleep state and generates feedback.

[1567] The device notifies the user of relaxation suggestions for the next day.

[1568] In this way, the system performs a series of processes, from data collection and analysis to providing a relaxation program, adjusting the environment, and providing feedback, thereby reducing stress and improving the quality of sleep for users. At the same time, by utilizing emotional data from the emotion engine, it becomes possible to more accurately assess stress and provide relaxation programs.

[1569] The processing flow will be explained below.

[1570] Step 1:

[1571] The terminal (wearable device or sensor) collects data such as the user's heart rate, body temperature, activity level, indoor temperature, humidity, and brightness in real time.

[1572] Step 2:

[1573] The emotion engine collects emotional data from the user's voice and facial expressions in real time.

[1574] Step 3:

[1575] The device transmits the collected biometric data, environmental data, and emotional data to the server at regular intervals (e.g., every minute).

[1576] Step 4:

[1577] The server receives the transmitted data and stores it in a database.

[1578] Step 5:

[1579] The server analyzes the stored data and determines the user's stress level.

[1580] For example, if your heart rate is higher than normal, your body temperature is high, or your activity level is low, you are considered to be in a high stress state.

[1581] Emotion data from the emotion engine is used as auxiliary information to improve the accuracy of stress judgment.

[1582] Step 6:

[1583] Based on the analysis results, the server uses a generation AI to generate a relaxation program suitable for the user.

[1584] Examples include guided meditation audio, guided breathing exercises, relaxation music, etc.

[1585] Step 7:

[1586] The server transmits the generated relaxation program to the terminal.

[1587] Step 8:

[1588] The terminal notifies the user of the received relaxation program and prompts the user to start the program.

[1589] Step 9:

[1590] The terminal provides the content of the relaxation program to the user.

[1591] Examples include playing audio guided breathing exercises, providing guided meditations, playing relaxation music, etc.

[1592] Step 10:

[1593] The server applies machine learning models based on past data and analyzes current data to optimize the user's sleep environment.

[1594] Example: Calculating the optimum room temperature, humidity, brightness, etc.

[1595] Step 11:

[1596] The server generates appropriate configuration instructions for smart devices (e.g., smart thermostats, smart lighting).

[1597] Step 12:

[1598] The terminal receives instructions from the server and sends environment adjustment commands to the associated smart devices.

[1599] Step 13:

[1600] The device continuously measures the user's sleep state (time to fall asleep, deep sleep, light sleep, wake-up time, etc.).

[1601] Step 14:

[1602] The device transmits the collected sleep data to the server at regular intervals.

[1603] Step 15:

[1604] The server analyzes the received sleep data and evaluates the quality of the user's sleep.

[1605] Step 16:

[1606] Based on the analysis results, the server generates feedback to the user regarding the next day's activities and relaxation methods.

[1607] Step 17:

[1608] The server transmits the generated feedback to the terminal.

[1609] Step 18:

[1610] The device provides feedback to the user and displays suggestions for the next day's activities and relaxation.

[1611] In this way, the system performs a series of processes to reduce the user's stress and provide an optimal sleeping environment. By utilizing emotion data from the emotion engine, it becomes possible to more accurately assess stress and provide relaxation.

[1612] Example 2

[1613] 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."

[1614] In modern society, many people suffer from health problems due to daily stress and inadequate sleep environments. Exposure to stress in particular can impair physical and mental health and reduce quality of life. To solve this problem, it is necessary to accurately grasp users' stress levels, provide appropriate relaxation programs, and optimize their sleep environments. However, there is currently no system that can comprehensively achieve these goals.

[1615] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1616] In this invention, the server includes means for collecting biometric data and environmental data using a wearable device or sensor, means for collecting user emotion data using an emotion engine, means for periodically transmitting the collected data to the server, means for analyzing the transmitted data and determining the user's stress level, means for using a generation AI to generate a relaxation program suitable for the user based on the determination result, means for providing the generated relaxation program to the user, means for adjusting the indoor environment and bedding settings based on the environmental data to optimize the user's sleep environment, means for measuring the user's sleep data and transmitting it to the server, and means for providing feedback to the user based on the analysis result, thereby enabling the user to reduce stress and improve the quality of their sleep.

[1617] A "wearable device" is an electronic device that can be worn by a user and collects biometric data such as heart rate, body temperature, and activity level.

[1618] A "sensor" is a device that measures environmental data and collects data such as temperature, humidity, and brightness.

[1619] The "emotion engine" is a system that analyzes emotions from the user's voice and facial expressions and collects that data.

[1620] "Biometric data" refers to data relating to the user's physical condition, such as the user's heart rate, body temperature, and activity level.

[1621] "Environmental data" refers to data relating to the user's surrounding environment, such as the temperature, humidity, and brightness of the room.

[1622] The "means for determining stress level" refers to an algorithm or program that the server uses to analyze the collected biometric data and emotional data and determine the user's stress state.

[1623] "Generative AI" is an artificial intelligence model that generates a relaxation program suitable for the user based on collected data.

[1624] "Relaxation programs" are means such as guided meditation audio, guided breathing exercises, and relaxation music provided to help users reduce stress.

[1625] A "means for optimizing the sleep environment" is a system for adjusting the room temperature, humidity, lighting, and bedding settings based on environmental data.

[1626] "Sleep data" refers to data relating to the user's sleep state, such as the time it takes for the user to fall asleep, deep sleep, light sleep, and wake-up time.

[1627] The "means for providing feedback" is a system that notifies the user of advice about the next day's activities and relaxation methods based on the analysis results.

[1628] This invention is a system for reducing user stress and providing an optimal sleeping environment. Specifically, it uses wearable devices and environmental sensors to collect biometric and environmental data from the user, and also combines it with an emotion engine to collect the user's emotional data. This data is analyzed to determine the user's stress level, and a generative AI model is used to generate a relaxation program, and the indoor environment and bedding are adjusted accordingly.

[1629] Data collection and analysis

[1630] First, the device (wearable device or sensor) collects data in real time, such as the user's heart rate, body temperature, activity level, room temperature, humidity, and brightness. Furthermore, an emotion engine is used to collect emotional data from the user's voice and facial expressions. This data is continuously sent to a server, which analyzes it and determines the user's stress level. This analysis is carried out using programming languages ​​such as Python and machine learning algorithms.

[1631] Examples:

[1632] While the user is wearing the wearable device at 15:00, the device measures their heart rate and body temperature every minute, and periodically sends the data to a server.

[1633] Example prompt sentence:

[1634] Collect the user's morning heart rate and activity data, as well as emotional data, to determine their stress level.

[1635] Creation and provision of relaxation programs

[1636] The server uses the analysis results, taking into account the user's stress level and emotional data, to generate a relaxation program using a generative AI. The relaxation program generated by the generative AI includes audio guided meditation, breathing guides, relaxation music, etc. This program is sent to the device, which notifies the user and encourages them to carry it out.

[1637] Examples:

[1638] For users who are judged by the server to be in a high stress state, a generative AI model is used to generate a 10-minute audio guided meditation and send it to the user's device.

[1639] Example prompt sentence:

[1640] Generate and notify relaxation programs according to the user's high stress state.

[1641] Adjusting the indoor environment and bedding

[1642] The server adjusts the indoor environment based on the collected environmental and emotional data to optimize the user's sleeping environment. For example, it can set the appropriate room temperature and humidity for the smart thermostat and adjust the brightness of the smart lights. It can also adjust the firmness and height of bedding based on the user's body pressure distribution data to provide a comfortable sleeping environment.

[1643] Examples:

[1644] One hour before the user goes to bed, the server instructs the smart thermostat to set the room temperature to 22 degrees and the humidifier to adjust the humidity to 50%.

[1645] Example prompt sentence:

[1646] Calculate the optimal room temperature and humidity based on the user's pre-sleep data and provide instructions to adjust the indoor environment.

[1647] Sleep data measurement and analysis

[1648] The device measures the user's sleep status (time to fall asleep, deep sleep, light sleep, wake-up time, etc.) and sends the data to a server. The server analyzes this data and evaluates the user's sleep quality. Based on this evaluation, the device provides feedback on the next day's activities and relaxation methods.

[1649] Examples:

[1650] When the user goes to sleep, the device automatically switches to sleep mode and measures their heart rate and body temperature. When they wake up, the data is sent to a server and feedback is generated to assess the quality of their sleep.

[1651] Example prompt sentence:

[1652] Analyze the user's sleep data and generate and notify relaxation suggestions for the next day.

[1653] In this way, the system performs a series of processes, from collecting data and analyzing it to providing a relaxation program, adjusting the environment, and providing feedback, thereby reducing stress and improving the quality of sleep for the user. Furthermore, by utilizing the emotion engine, it is possible to provide more personalized stress assessments and relaxation programs.

[1654] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1655] Step 1:

[1656] Terminals (wearable devices and sensors) collect biometric and environmental data.

[1657] Specific operation: The device collects the user's heart rate, body temperature, activity level, indoor temperature, humidity, and brightness in real time.

[1658] Input: Biometric and environmental data obtained from the user.

[1659] Output: A set of collected data (e.g. heart rate, body temperature, temperature, humidity, etc.).

[1660] Step 2:

[1661] The terminal uses an emotion engine to collect emotion data of the user.

[1662] Specific operation: The device uses its built-in microphone and camera to analyze the user's voice and facial expressions and obtain emotional data.

[1663] Input: User's voice and facial expressions.

[1664] Output: Emotional data (e.g., emotional state or tendency).

[1665] Step 3:

[1666] The terminal periodically transmits the collected data to the server.

[1667] Specific operation: The device uses Wi-Fi or Bluetooth to send data to the server at regular intervals. If the transmission is successful, it proceeds to the next data collection, but if it fails, it retries.

[1668] Input: Collected biometric, environmental, and emotional data.

[1669] Output: The data sent to the server.

[1670] Step 4:

[1671] The server analyzes the received data and determines the user's stress level.

[1672] How it works: The server uses programming languages ​​such as Python to analyze data with machine learning algorithms and evaluate stress levels. For example, if your heart rate is high, your activity level is low, or there is a lot of negative emotional data, it will determine that you are under high stress.

[1673] Input: The dataset sent from the device.

[1674] Output: The user's stress level as a result of the analysis (e.g. high stress, low stress).

[1675] Step 5:

[1676] The server generates a relaxation program using a generative AI model based on the judgment results.

[1677] Specific operation: The server uses the generative AI model to generate a relaxation program (e.g., meditation audio guide, breathing exercise guide) that takes into account the user's stress level and emotional data.

[1678] Input: Stress level analysis results and emotion data.

[1679] Output: Relaxation program (e.g. 10-minute guided meditation).

[1680] Step 6:

[1681] The server transmits the generated relaxation program to the terminal, and the terminal notifies the user and prompts them to execute it.

[1682] Specific operation: The server sends the relaxation program to the terminal, and the terminal displays a notification of the program to the user and a message prompting the user to run it.

[1683] Input: The generated relaxation program.

[1684] Output: The relaxation program notified to the user.

[1685] Step 7:

[1686] The server issues instructions to adjust the room climate and bedding settings based on the environmental data.

[1687] Specific operation: Based on current indoor environment data, the server issues appropriate temperature, humidity, and brightness instructions to smart thermostats and smart lights.

[1688] Input: User's environmental data and sleep preparation data.

[1689] Output: Calibration instructions sent to the smart device.

[1690] Step 8:

[1691] The device measures the user's sleep state and sends the data to a server.

[1692] How it works: The device measures the user's heart rate, body temperature, and activity level from the time they fall asleep until they wake up, and sends this data to a server in the morning.

[1693] Input: User's biometric data while sleeping.

[1694] Output: Measured sleep data.

[1695] Step 9:

[1696] The server analyzes the sleep data and provides feedback to the user.

[1697] Specific operation: The server analyzes the acquired sleep data and evaluates the user's sleep quality. Based on the results, it generates feedback on the user's behavior and relaxation methods for the next day and notifies the user via the device.

[1698] Input: Sleep data.

[1699] Output: The generated feedback and notifications based on it.

[1700] (Application example 2)

[1701] 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."

[1702] Currently, the working environment for staff in brick-and-mortar stores is often stressful, making it difficult to improve staff efficiency and reduce their mental burden. Furthermore, in order to provide comfortable service to customers, it is necessary to properly manage staff health and emotional states. Current technology does not adequately analyze real-time biometric and emotional data, and provide effective relaxation programs based on that data. Therefore, a system that solves these issues is needed.

[1703] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1704] In this invention, the server includes means for collecting biometric data and environmental data using a wearable device or sensor, means for analyzing the collected data and determining the user's stress level, means for using a generation AI to generate a relaxation program suitable for the user based on the determination result, means for providing the generated relaxation program to the user, means for adjusting the facility environment based on the environmental data to optimize the user's working environment, means for analyzing the emotion data of the user and facility users using an emotion engine, and notification means for providing a relaxation program in real time according to the stress level, thereby reducing staff stress, improving work efficiency, and providing comfortable service to customers.

[1705] A "wearable device" is a measuring device that can be worn by a user and has the function of collecting biological data and environmental data.

[1706] A "sensor" is a device that detects physical quantities, and is particularly used to collect environmental data (temperature, humidity, brightness, etc.) and biological data (heart rate, body temperature, etc.).

[1707] "Biometric data" refers to data relating to the user's physical condition, including, for example, heart rate, body temperature, and the like.

[1708] "Environmental data" refers to data relating to the environment surrounding the user, including, for example, temperature, humidity, brightness, and the like.

[1709] "Stress level" is an indicator of the user's psychological and physical stress state.

[1710] "Generative AI" is a system that uses an artificial intelligence model to generate the optimal relaxation program for each user.

[1711] A "relaxation program" is a series of guides, music, and other content designed to promote relaxation in users.

[1712] "Working environment" refers to the place and conditions in which staff work, including in particular factors relating to the comfort and safety of that environment.

[1713] "Facility environment" refers to the entire environment of a physical store or service location, including physical conditions such as temperature, humidity, and brightness.

[1714] The "emotion engine" is a system for analyzing the emotional state of users and facility visitors from their facial expressions and voices.

[1715] "Notification means" refers to a mechanism for conveying information and instructions to the user in real time, and specifically includes the use of smart glasses or a smartphone.

[1716] This invention mainly uses wearable devices, sensors, emotion engines, generative AI, servers, and notification means to create a system that reduces staff stress and optimizes the working environment in physical stores.

[1717] System Overview

[1718] Wearable devices and sensors collect real-time biometric data from staff and store environment data, including heart rate, body temperature, ambient temperature, humidity, and brightness, and periodically transmit this data to a server.

[1719] The server analyzes the collected data and uses generative AI to determine the stress level of staff. If the stress level is high, the server generates an appropriate relaxation program and provides it to the relevant staff member. It also uses an emotion engine to analyze the emotional data of staff and customers, making even more accurate stress assessments.

[1720] Optimizing your environment

[1721] The server adjusts the facility environment based on environmental data to optimize the staff's working environment. For example, if the room temperature is too high, the server will automatically issue instructions to lower the temperature. If the light level is low, the server will adjust the lighting.

[1722] Real-time notifications

[1723] Smart glasses or smartphones are used as notification devices to provide real-time relaxation programs based on stress levels, allowing staff to receive the programs on the spot.

[1724] Specific examples

[1725] For example, imagine a brick-and-mortar store staff wearing smart glasses, which could enable the following daily routine:

[1726] 1. Morning:

[1727] The smart glasses measure the heart rate and body temperature of staff, and an emotion engine analyzes the staff's emotional data, which is then sent to a server.

[1728] Example prompt sentence:

[1729] "Data will now be sent to the server: including staff heart rate, body temperature, store temperature, humidity, and lighting intensity."

[1730] 2. Afternoon:

[1731] The server analyzes the data and determines that the staff member's stress level is high. Using generative AI, it generates a relaxation program suitable for the staff member and notifies the smart glasses.

[1732] Example prompt sentence:

[1733] "We have determined that staff members have high stress levels. We will now play a 10-minute guided breathing program."

[1734] 3. Evening:

[1735] If the environment inside the facility is not comfortable, the server will send instructions to smart devices to adjust the temperature and lighting.

[1736] Example prompt sentence:

[1737] "The temperature is high, please adjust the set temperature to 22 degrees."

[1738] These processes ensure that the working environment for staff is always optimized, reducing stress and enabling the provision of comfortable service to customers.

[1739] Such systems and methods will enable stress management and environmental adjustment in physical stores.

[1740] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1741] Step 1: Data collection

[1742] The terminal uses wearable devices and sensors to collect staff biometric data (heart rate, body temperature, etc.) and store environmental data (temperature, humidity, brightness, etc.) in real time. This data is periodically aggregated in the terminal. The input is various data from the wearable devices and sensors, and the output is a set of collected data.

[1743] Step 2: Send data

[1744] The terminal sends the collected biometric and environmental data to the server. The input is the set of data collected in step 1, and the output is the data received at the server side.

[1745] Step 3: Data analysis

[1746] The server analyzes the received biometric and environmental data. This analysis uses a generative AI model to determine the staff member's stress level from each data point. It also uses an emotion engine to extract and analyze emotional data from the facial expressions and voices of staff and customers. The input is the transmitted data set, and the output is the staff member's stress level and emotional state.

[1747] Step 4: Create a relaxation program

[1748] The server generates a relaxation program suited to each staff member based on the analysis results. In this process, it uses a generative AI to create a program (e.g., meditation audio guide, breathing exercise guide, relaxation music, etc.) that is optimal for each staff member's stress level and emotional state. The input is the analysis results, and the output is the generated relaxation program.

[1749] Step 5: Notification of relaxation program

[1750] The server sends the generated relaxation program to the terminal, which then notifies the staff via smart glasses or a smartphone. The input is the relaxation program, and the output is the program notification received by the staff. Specifically, the program is displayed on the smart glasses or instructions are played back via voice.

[1751] Step 6: Adjusting the environment

[1752] Based on the collected environmental data, the server sends instructions to smart devices to optimize the environment within the facility. For example, if the room temperature is too high, lower the temperature, or if the lights are too dim, increase the brightness. These instructions are transmitted to smart thermostats and smart lights. The input is environmental data, and the output is the adjusted environmental conditions.

[1753] Step 7: Reassessment and feedback

[1754] The terminals continuously collect biometric and environmental data from staff. The server then analyzes this data again, monitors changes in stress levels and emotional states, and provides feedback as needed. The input is a newly collected set of data, and the output is feedback information. Specifically, the system notifies staff of relaxation suggestions and work environment advice for the following day.

[1755] 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.

[1756] 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.

[1757] 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.

[1758] 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.

[1759] FIG. 9 illustrates 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 behaviors 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.

[1760] 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.

[1761] 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).

[1762] 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.

[1763] 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."

[1764] 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.

[1765] 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).

[1766] 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.

[1767] 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.

[1768] 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.

[1769] 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.

[1770] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, 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 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.

[1771] 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.

[1772] 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.

[1773] 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.

[1774] 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.

[1775] 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.

[1776] The following is further disclosed regarding the above embodiment.

[1777] (Claim 1)

[1778] a means for collecting biometric and environmental data by a wearable device or sensor;

[1779] means for analyzing the collected data and determining the user's stress level;

[1780] A means for using a generation AI to generate a relaxation program suitable for the user based on the judgment result;

[1781] means for providing the generated relaxation program to a user;

[1782] and means for adjusting the room environment and bedding settings based on the environmental data to optimize the user's sleep environment.

[1783] Including system.

[1784] (Claim 2)

[1785] 2. The system of claim 1, wherein the relaxation program is one of a guided meditation audio, a guided breathing technique, or relaxation music.

[1786] (Claim 3)

[1787] 10. The system of claim 1, further comprising means for measuring a user's sleep state, collecting sleep data, and analyzing the data.

[1788] "Example 1"

[1789] (Claim 1)

[1790] a means for collecting biometric and environmental data by a wearable device or sensor;

[1791] means for periodically transmitting the collected data to a server;

[1792] means for analyzing the data received by the server and determining the stress level of the user;

[1793] A means for generating a relaxation program suitable for the user using a generative AI model based on the determination result;

[1794] means for transmitting the generated relaxation program to a terminal and notifying the user;

[1795] means for adjusting room climate and bedding settings based on historical and current data to optimize the user's sleep environment;

[1796] A means for measuring the user's sleep state, sending the data to a server, and analyzing it.

[1797] Including system.

[1798] (Claim 2)

[1799] 2. The system of claim 1, wherein the relaxation program is one of a guided meditation audio, a guided breathing technique, or relaxation music.

[1800] (Claim 3)

[1801] 10. The system of claim 1, further comprising means for providing a user with feedback based on the analysis results.

[1802] "Application Example 1"

[1803] (Claim 1)

[1804] a means for collecting biometric and environmental data by a wearable device or sensor;

[1805] means for analyzing the collected data and determining the user's stress level;

[1806] A means for using a generation AI to generate a relaxation program suitable for the user based on the judgment result;

[1807] means for providing the generated relaxation program to a user;

[1808] A means to suggest relaxation services and recommended products according to the user's stress level,

[1809] means for informing the user of proposed relaxation services and products through the smartphone application in order to enhance the user's experience;

[1810] and means for adjusting the room environment and bedding settings based on the environmental data to optimize the user's sleep environment.

[1811] Including system.

[1812] (Claim 2)

[1813] 2. The system of claim 1, wherein the relaxation program is one of a guided meditation audio, a guided breathing technique, or relaxation music.

[1814] (Claim 3)

[1815] 10. The system of claim 1, further comprising means for measuring a user's sleep state, collecting sleep data, and analyzing the data.

[1816] "Example 2: Combining Emotion Engines"

[1817] (Claim 1)

[1818] a means for collecting biometric and environmental data by a wearable device or sensor;

[1819] means for collecting user emotion data using an emotion engine;

[1820] means for periodically transmitting the collected data to a server;

[1821] means for analyzing the transmitted data and determining the stress level of the user;

[1822] A means for using a generation AI to generate a relaxation program suitable for the user based on the judgment result;

[1823] means for providing the generated relaxation program to a user;

[1824] means for adjusting room environment and bedding settings based on the environmental data to optimize the user's sleep environment;

[1825] A means for measuring the user's sleep data and transmitting the data to a server;

[1826] A means of providing feedback to users based on the analysis results.

[1827] Including system.

[1828] (Claim 2)

[1829] 2. The system of claim 1, wherein the relaxation program is one of a guided meditation audio, a guided breathing technique, or relaxation music.

[1830] (Claim 3)

[1831] The system according to claim 1, which uses a generation AI that generates a relaxation program based on the user's emotional data.

[1832] "Application example 2 when combining emotion engines"

[1833] (Claim 1)

[1834] a means for collecting biometric and environmental data by a wearable device or sensor;

[1835] means for analyzing the collected data and determining the user's stress level;

[1836] A means for using a generation AI to generate a relaxation program suitable for the user based on the judgment result;

[1837] means for providing the generated relaxation program to a user;

[1838] means for adjusting the facility environment based on the environmental data to optimize the user's working environment;

[1839] A means for analyzing emotion data of users and policy users using an emotion engine;

[1840] A notification method that provides real-time relaxation programs according to stress levels;

[1841] ...

[1842] A system including:

[1843] (Claim 2)

[1844] 2. The system of claim 1, wherein the relaxation program is one of a guided meditation audio, a guided breathing technique, or relaxation music.

[1845] (Claim 3)

[1846] 10. The system of claim 1, further comprising means for measuring a user's working condition and collecting and analyzing environmental data. [Explanation of symbols]

[1847] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for collecting biometric and environmental data by a wearable device or sensor; means for analyzing the collected data and determining the user's stress level; A means for using a generation AI to generate a relaxation program suitable for the user based on the judgment result; means for providing the generated relaxation program to a user; and means for adjusting the room environment and bedding settings based on the environmental data to optimize the user's sleep environment. Including system.

2. 2. The system of claim 1, wherein the relaxation program is any one of a guided meditation audio, a guided breathing technique, and relaxation music.

3. 10. The system of claim 1, further comprising means for measuring a user's sleep state, collecting and analyzing sleep data.

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