system

Abstract

Provide a system. [Solution] a means for collecting vital data; a means for collecting indoor environmental data; A means for analyzing the collected vital data and indoor environment data and generating optimal environment settings; means for transmitting instructions to apply the generated configuration; means for receiving the instruction and adjusting the indoor environment; A system including:

Description

[Technical Field]

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

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

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

[0004] In modern society, many people are unable to get enough sleep, resulting in an increasing number of health problems. This is due to the difficulty of providing a sleeping environment that is suited to each individual user. Furthermore, typical sleep environment provision services are unable to collect and analyze users' vital signs and indoor environmental data in real time, making it difficult to set up an optimal environment. Furthermore, they do not provide accurate advice or action suggestions for users' specific sleep concerns. As a result, there is a lack of concrete methods for improving sleep quality. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means. First, a system is provided that includes a means for collecting a user's vital data and a means for collecting indoor environment data. The system uses these data to perform analysis and generate optimal environmental settings. The system further includes a means for sending instructions to apply the generated environmental settings and a means for receiving the instructions and adjusting the indoor environment. The system also includes a means for calculating a sleep score from the collected vital data and indoor environment data and providing feedback. This allows the next environmental settings to be performed with greater accuracy. The system further includes a means for receiving and analyzing inquiries from the user and a means for generating and providing action suggestions to the user based on the analysis results, thereby addressing the specific sleep concerns of each individual user.

[0006] "Vital data" refers to data relating to the physiological condition of the user's body, including heart rate, body temperature, respiratory rate, etc.

[0007] "Indoor environment data" refers to data relating to the physical conditions of a room, including temperature, humidity, illuminance, noise, wind speed, and the like.

[0008] "Analysis" is the process of calculating and evaluating the collected data to determine optimal environmental settings and a sleep score.

[0009] "Environmental settings" refers to specific instructions and adjustment parameters for adjusting the physical conditions in a room to create an optimal sleeping environment.

[0010] The "means for sending instructions" is a communication means for notifying the terminal of the environmental settings generated by the server.

[0011] The "means for adjusting the indoor environment" refers to a means for controlling devices such as air conditioners, humidifiers, and lighting, and changing the indoor environmental conditions based on instructions.

[0012] "Sleep score" is a numerical indicator used to evaluate the quality and depth of a user's sleep, and is calculated based on vital data and indoor environmental data.

[0013] "Feedback measures" are measures that provide information to improve the next environmental adjustment based on past data and results.

[0014] "Action Suggestions" are advice on specific actions users should take or avoid to get better sleep.

[0015] The "means for interacting with the user" refers to a means by which the user can input questions or concerns about sleep and receive appropriate advice or suggestions in response. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This invention relates to a system that collects and analyzes vital data and indoor environmental data in real time to provide an optimal sleeping environment for users in order to improve their sleep quality. This system sets optimal environmental settings based on the user's vital data and indoor environmental data, and can also provide specific action suggestions in response to the user's questions and concerns.

[0038] First, before the user goes to bed, the system collects the data necessary to create a comfortable indoor environment for that person. Vital data (heart rate, body temperature, respiratory rate, etc.) is received in real time from the wearable device worn by the user and uploaded to the server. In addition, indoor environmental data is collected from sensors placed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor).

[0039] The server analyzes this data and generates optimal environmental settings. For example, if the heart rate is stable, it determines that the user is likely to fall into deep sleep and sets the room temperature to 22 degrees. If the humidity is too low, it turns on the humidifier, and if the brightness is too high, it turns off the lights.

[0040] The server then sends instructions to the device to apply these environmental settings, and the device automatically adjusts the room's air conditioner, humidifier, lighting, etc. based on the received instructions, allowing the user to sleep in a comfortable environment.

[0041] Furthermore, the server calculates a sleep score from the collected vital data and indoor environmental data, and uses the results as feedback to reflect in future environmental settings. For example, by fine-tuning the next settings based on the optimal environmental settings obtained from past data, a more accurate environment can be provided.

[0042] When a user inputs questions or concerns about sleep, the device sends the information to a server, which analyzes the data and generates specific recommendations for action, such as "don't use your smartphone before bed" or "try breathing exercises to relax," which are then provided to the user via the device.

[0043] As a concrete example, consider the case where a user wakes up in the middle of the night. Because the user is wearing a wearable device, the server immediately detects an increase in heart rate. The server analyzes this data and generates and sends to the device an instruction to lower the temperature by one degree to help the user fall back into deep sleep. The device receives this instruction and automatically adjusts the air conditioning settings, allowing the user to return to sleep in a comfortable environment.

[0044] As described above, the present invention provides a specific system for improving sleep quality by setting optimal environmental settings in real time based on the user's vital data and indoor environmental data.

[0045] The processing flow will be explained below.

[0046] Step 1:

[0047] The server receives vital data (heart rate, body temperature, respiratory rate, etc.) from the user's wearable device every 10 minutes, using data communication via Bluetooth or Wi-Fi.

[0048] Step 2:

[0049] The server receives indoor environment data every minute from sensors installed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor). The data from each sensor is acquired via API and stored in a database.

[0050] Step 3:

[0051] The server analyzes the collected vital data and indoor environmental data to evaluate the current state of the user. For example, if the heart rate is high, it may indicate that the user is feeling stressed and it may be necessary to lower the temperature.

[0052] Step 4:

[0053] The server generates optimal environmental settings (e.g., temperature 22°C, humidity 45%, illuminance 0 lumens) based on the analysis results, allowing users to obtain the optimal sleeping environment.

[0054] Step 5:

[0055] The server sends instructions to the device to apply the generated environment settings, such as setting the air conditioner temperature to 22 degrees and the humidifier humidity to 45%.

[0056] Step 6:

[0057] Based on the received instructions, the device adjusts indoor appliances such as air conditioners, humidifiers, and lighting. It analyzes the received data and changes the settings of each appliance accordingly.

[0058] Step 7:

[0059] The server calculates a sleep score from the collected vital data and indoor environmental data, which is compared with past data to evaluate sleep quality.

[0060] Step 8:

[0061] The server will provide feedback to the next environment settings based on the sleep score, which will result in more accurate settings from the next time onwards, further optimizing the user's sleep environment.

[0062] Step 9:

[0063] When a user enters a question or concern about sleep via voice or text, the device receives it and sends it to the server. For example, "I can't fall asleep easily."

[0064] Step 10:

[0065] The server analyzes the received questions and concerns and generates specific advice and action suggestions, such as "Avoid using your smartphone before going to bed."

[0066] Step 11:

[0067] The device notifies the user of any advice or suggested actions received from the server, which are played aloud through the voice assistant or displayed as text through the app's chat function.

[0068] These are the specific processing steps of this system. At each step, the server, device, and user each play their necessary roles and work together to optimize the sleep environment.

[0069] Example 1

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

[0071] In modern living environments, many people find it difficult to get a good night's sleep, which negatively impacts their health and quality of life. Conventional technologies lack systems that can collect and analyze users' vital signs and indoor environmental data in real time and provide optimal sleeping environments based on that data. Furthermore, they lack a means to provide appropriate action suggestions in response to sleep-related questions and problems, making it difficult for users to efficiently create a comfortable sleeping environment.

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

[0073] In this invention, the server includes means for collecting vital data from the wearable device, means for collecting indoor environmental data from a detector placed in the room, means for analyzing the collected vital data and indoor environmental data and generating optimal environmental settings, means for transmitting instructions for applying the generated environmental settings, and means for receiving the instructions and automatically adjusting the indoor environment. This enables comprehensive analysis of the user's vital data and indoor environmental data and automatic configuration of the optimal environmental settings in real time. It is also possible to receive inquiries from the user and provide specific action suggestions based on the analysis results, thereby improving the user's sleep quality.

[0074] A "wearable device" is an electronic device that can collect vital data such as heart rate, body temperature, and respiratory rate in real time by being worn by the user.

[0075] "Detector" refers to a sensor installed to collect environmental data such as indoor temperature, humidity, light, noise, and wind speed.

[0076] "Vital data" refers to biometric information related to the user's health status, such as heart rate, body temperature, and respiratory rate.

[0077] "Indoor environment data" refers to data necessary to create a comfortable sleeping environment for the user, such as the temperature, humidity, illuminance, noise, and wind speed in the room.

[0078] "Analysis" refers to the process of comprehensively evaluating the collected vital data and indoor environmental data to derive optimal environmental settings.

[0079] "Environmental settings" refers to adjusting settings such as air conditioning, humidifiers, and lighting to help users sleep comfortably.

[0080] An "instruction" is a specific operation command for transmitting the environmental settings generated by the server to the terminal.

[0081] The "sleep evaluation value" is a numerical value that represents the quality of the user's sleep, calculated from the collected vital data and indoor environment data.

[0082] "Action suggestions" are suggestions based on the analysis results for specific actions that users can take to get a good night's sleep.

[0083] The present invention provides a system for improving the quality of a user's sleep by collecting and analyzing data in real time using a wearable device and sensors placed in a room, and providing an optimal sleeping environment. Specific embodiments are described below.

[0084] First, the user puts on the wearable device before going to bed. The device collects real-time vital data such as the user's heart rate, body temperature, and respiratory rate and uploads it to a server. Additionally, temperature, humidity, light, noise, and wind speed sensors placed in the room collect indoor environmental data. All of this data is sent to the server.

[0085] The server analyzes the received vital data and indoor environmental data. For example, if the user's heart rate is stable, it determines that the user is likely to fall into deep sleep and sets the room temperature to 22 degrees. It also generates instructions to turn on the humidifier if the humidity is too low, or to turn off the lights if the brightness is too high.

[0086] The server then sends instructions to the device to apply the generated environmental settings, and the device automatically adjusts the room's appliances, such as the air conditioner, humidifier, and lighting, based on the received instructions, allowing the user to sleep in a comfortable environment.

[0087] The server then calculates a sleep score from the collected vital data and indoor environmental data. This sleep score is reflected as feedback in future environment settings. Specifically, by fine-tuning the environment settings based on past data, a more accurate environment can be provided.

[0088] When a user inputs a question or concern about sleep, the device sends the information to the server. The server then analyzes the information using a generative AI model and generates specific action suggestions based on the analysis results. For example, specific advice such as "don't use your smartphone before bed" or "try breathing exercises to relax" can be generated and provided to the user via the device.

[0089] As a concrete example, consider the case where a user wakes up in the middle of the night. When an increase in heart rate is detected from the wearable device worn by the user, the server immediately analyzes this data and sends an instruction to the device to lower the temperature by one degree to help the user fall into deep sleep again. The device receives this instruction and automatically adjusts the air conditioning settings, allowing the user to sleep in a comfortable environment again.

[0090] Here is an example prompt:

[0091] "Generate specific action suggestions to help the user enter deep sleep. For example, avoid using your phone before bed or try relaxing breathing exercises."

[0092] As described above, the present invention provides a specific system for improving sleep quality by setting optimal environmental settings in real time based on the user's vital data and indoor environmental data.

[0093] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0094] Step 1:

[0095] Users wear a wearable device that collects vital data such as heart rate, body temperature, and respiratory rate in real time.

[0096] Input: Real-time data of heart rate, body temperature, and respiratory rate

[0097] Output: Vital data sent from the wearable device

[0098] Specific operation: Device data is sent from the device worn on the user's wrist to the server via wireless communication such as Bluetooth.

[0099] Step 2:

[0100] Sensors placed in the room collect indoor environmental data such as temperature, humidity, illuminance, noise, and wind speed and send it to a server.

[0101] Input: Real-time data of temperature, humidity, light, noise, and wind speed

[0102] Output: Indoor environment data sent from the sensor

[0103] Specific operation: The sensor collects data and sends it to a server via Wi-Fi or other wireless communication.

[0104] Step 3:

[0105] The server receives and analyzes vital data from the wearable device and indoor environment data from sensors in the room.

[0106] Input: vital data, indoor environment data

[0107] Output: Recommended environment settings for analysis results

[0108] How it works: The server analyzes the data, estimates the depth of sleep from the user's heart rate and body temperature, and calculates the optimal temperature, humidity, and light settings.

[0109] Step 4:

[0110] The server generates instructions for optimal environment settings based on the analysis results and sends them to the device.

[0111] Input: Analysis results

[0112] Output: Setting instructions for air conditioners, humidifiers, lighting, etc.

[0113] Specific actions: The server generates specific instructions such as setting the air conditioner to 22 degrees, turning on the humidifier, or turning off the lights.

[0114] Step 5:

[0115] The device automatically adjusts the air conditioner, humidifier, lighting, etc. based on the environmental setting instructions received from the server.

[0116] Input: Setting instructions from the server

[0117] Output: Adjusted indoor environment

[0118] Specific actions: The device sets the air conditioner temperature to 22 degrees, turns on the humidifier, and turns off the lights.

[0119] Step 6:

[0120] The server calculates a sleep score from the collected vital data and indoor environmental data and provides feedback for the next environmental settings.

[0121] Input: vital data, indoor environment data

[0122] Output: Sleep score, next setting data

[0123] Specific operation: The server uses an algorithm to calculate a sleep score based on past data and reflects the result in the next environment settings.

[0124] Step 7:

[0125] Users enter their sleep-related questions and concerns into the device, which then sends the information to the server.

[0126] Input: User text input

[0127] Output: The question data sent to the server

[0128] Specific operation: The user enters a question into the device, which then sends it to the server.

[0129] Step 8:

[0130] The server generates action suggestions for the user's questions and concerns and sends them to the device.

[0131] Input: Question data from users, analysis results of the generative AI model

[0132] Output: Specific action suggestions

[0133] Specific operation: The server analyzes the question using the generative AI model and sends suggested actions to the device, such as "don't use your smartphone before bed" or "try breathing exercises to relax."

[0134] Through these steps, the system provides an optimal sleeping environment in real time based on the user's vital data and indoor environmental data, and suggests specific actions to take in response to the user's questions.

[0135] (Application example 1)

[0136] 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."

[0137] Thanks to recent technological advances, autonomous vehicles are becoming more commonplace, but technology for providing a comfortable in-vehicle sleeping environment is still in its infancy, and there is a need for improved comfort during long-distance travel. To provide an optimal sleeping environment, it is necessary to collect and analyze the user's vital signs and the vehicle's internal environmental data in real time, and automatically adjust the environmental settings based on the results. However, such a system has not yet been realized. Therefore, the challenge is to develop a system that provides the optimal in-vehicle environmental settings for users to achieve quality sleep.

[0138] 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.

[0139] In this invention, the server includes means for collecting vital data, means for collecting interior environment data, means for analyzing the collected vital data and interior environment data and generating optimal environment settings, means for sending instructions for applying the generated environment settings, means for receiving the instructions and adjusting the interior environment, means for collecting environmental data inside the vehicle, and means for adjusting the air conditioning, lighting, and seat reclining angle inside the vehicle, thereby making it possible to provide optimal environment settings for the user to sleep comfortably in the vehicle.

[0140] "Vital data" refers to data related to a user's vital activities, such as heart rate, body temperature, and respiratory rate.

[0141] "Indoor environment data" refers to data relating to the indoor environment, such as indoor temperature, humidity, illuminance, noise, and wind speed.

[0142] "Means for analyzing and generating optimal environmental settings" refers to algorithms or programs that automatically generate optimal environmental settings for the user based on collected vital data and indoor environmental data.

[0143] "Means for sending instructions to apply the generated configuration" refers to a communication function for sending instructions to operate the configuration from the server to the appropriate device or equipment.

[0144] "Means for adjusting the indoor environment" refers to control devices and software for adjusting indoor equipment such as air conditioners, humidifiers, and lighting.

[0145] "Interior vehicle environmental data" refers to data related to the in-vehicle environment, such as the temperature, humidity, lighting intensity, and seat reclining angle inside an autonomous vehicle.

[0146] "Means for adjusting the air conditioning, lighting, and seat reclining angle inside the vehicle" refers to the control systems and software that automatically adjust the air conditioning, lighting, and seat position inside the vehicle.

[0147] "Means for inputting prompts to the generative AI model" refers to an interface or program for inputting necessary instructions or questions to the artificial intelligence model.

[0148] "Means for calculating sleep score" refers to algorithms or software that quantify a user's sleep quality based on collected vital data and indoor environmental data.

[0149] This invention is a system that collects and analyzes vital data and internal vehicle environmental data in real time to provide an optimal sleeping environment for users in autonomous vehicles, in order to improve their sleep quality. This system consists of the following main components:

[0150] 1. Vital data collection means: Vital data such as heart rate, body temperature, and respiratory rate are collected in real time from a wearable device (e.g., a smart watch) worn by the user.

[0151] 2. Means for collecting environmental data inside the vehicle: Various sensors such as temperature sensors, humidity sensors, and illuminance sensors are used to collect environmental data such as temperature, humidity, lighting intensity, and noise inside the vehicle.

[0152] 3. Data analysis and optimal environment generation: The server analyzes the collected vital and environmental data and generates optimal environmental settings for the user (for example, setting the temperature to 22°C and the humidity to 40%). At this time, it also calculates a sleep score based on past data and uses this as feedback to reflect in future settings.

[0153] 4. Instruction transmission means: The server transmits instructions to the vehicle's internal control system to apply the generated configuration. This communication is performed using wireless communication technologies such as Wi-Fi or Bluetooth.

[0154] 5. Environmental adjustment means: Automatically adjust the air conditioning, humidifier, lighting, seat reclining angle, etc. inside the vehicle, allowing users to enjoy an optimal sleeping environment inside the autonomous vehicle.

[0155] 6. Means for receiving and analyzing inquiries from users: Questions and concerns about sleep entered by users through their smartphone or vehicle interface are received and analyzed on the server.

[0156] 7. Means for generating and providing action suggestions: The server generates action suggestions based on the analysis results and provides them to the user according to the prompts entered into the generative AI model. For example, it provides specific advice such as "Don't use your smartphone before going to bed" or "Try breathing exercises to relax."

[0157] Specific examples

[0158] Suppose a user wears a smartwatch and reclines their seat to rest in an autonomous vehicle. The system detects that the user's heart rate begins to steadily decrease immediately after lying down. Analyzing this data in real time, the server sends instructions to adjust the interior temperature to 22 degrees. If the humidity is low, it also sends instructions to activate the humidifier. This allows the user to sleep in an optimal environment.

[0159] Example prompt for a generative AI model:

[0160] To ensure a user can sleep comfortably in an autonomous vehicle, calculate the optimal environmental settings based on the following data: Vital data includes heart rate, body temperature, and respiratory rate, while environmental data includes temperature, humidity, light intensity, noise, and wind speed. For example, if the heart rate is stable, the room temperature should be set to 22°C; if the humidity is too low, the humidifier should be turned on; and if the light intensity is high, the lights should be turned off. Output the appropriate environmental settings.

[0161] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0162] Step 1:

[0163] The user wears a smartwatch and sits in a reclining seat in an autonomous vehicle. At this point, the smartwatch measures vital data such as heart rate, body temperature, and respiratory rate in real time. This vital data is then transmitted from the wearable device to a terminal.

[0164] Input: Heart rate, body temperature, respiration rate (from wearable device)

[0165] Output: Real-time vital data

[0166] Step 2:

[0167] Temperature, humidity, and light sensors installed inside the vehicle measure environmental data inside the vehicle, which is then transmitted to the terminal.

[0168] Input: Temperature, humidity, illuminance (from in-car sensors)

[0169] Output: Real-time environmental data

[0170] Step 3:

[0171] The device sends the collected vital and environmental data to a server, which analyzes the data and determines the user's current condition.

[0172] Input: Vital data, environmental data (from terminal)

[0173] Output: Analysis data (user status)

[0174] Step 4:

[0175] Based on the analyzed data, the server inputs prompts into the generative AI model to generate optimal environment settings, taking into account feedback based on previous data.

[0176] Input: Analysis data (user status), prompt statement

[0177] Output: Optimal Environment Settings

[0178] Step 5:

[0179] The server sends the generated configuration to the device as instructions to apply it, which the device receives and sends to the in-car control system.

[0180] Input: Optimal environment settings (from server)

[0181] Output: Environmental adjustment instructions (from terminal to in-car control system)

[0182] Step 6:

[0183] Actuators inside the vehicle for air conditioning, lighting, humidifiers, seat reclining angles, etc. receive instructions from the terminal and automatically adjust to the specified settings.

[0184] Input: Environment adjustment instructions (from terminal)

[0185] Output: Controlled environment (temperature, humidity, light, seat angle)

[0186] Step 7:

[0187] Users input their sleep-related questions and concerns through a smartphone or vehicle interface, and the device then sends this information to a server.

[0188] Input: User's question or concern

[0189] Output: Query data (to server)

[0190] Step 8:

[0191] The server analyzes the user's query and generates appropriate action suggestions, also driven by a generative AI model, such as breathing exercises to help with relaxation.

[0192] Input: Question data (from user), analyzed by generative AI model

[0193] Output: Action suggestions

[0194] Step 9:

[0195] The server then sends the generated action suggestions to the device and provides them to the user, who can then take action to further improve their sleep quality.

[0196] Input: Action proposal (from server)

[0197] Output: Feedback to the user

[0198] 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.

[0199] This invention combines a system that collects and analyzes the user's vital data and indoor environmental data in real time to provide the optimal sleeping environment with an emotion engine that recognizes and analyzes the user's emotions. This system makes it possible to set environmental settings and suggest actions that take into account not only the user's physiological data but also their emotional state.

[0200] First, before the user goes to bed, the system collects the data necessary to create a comfortable indoor environment for that person. Vital data (heart rate, body temperature, respiratory rate, etc.) is received in real time from the wearable device worn by the user and uploaded to the server. In addition, indoor environmental data is collected from sensors placed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor).

[0201] The server analyzes this data and generates optimal environmental settings. For example, if the heart rate is stable, it determines that the user is likely to fall into deep sleep and sets the room temperature to 22 degrees. If the humidity is too low, it turns on the humidifier, and if the brightness is too high, it turns off the lights.

[0202] The system then takes the user's emotions into account. The emotion engine analyzes the user's voice and facial expressions to determine their emotions, and sends the results to the server. For example, if the system determines that the user is feeling stressed, it will generate relaxing environmental settings (such as lowering the temperature slightly or enabling silent mode).

[0203] The server then sends instructions to the device to apply these environmental settings, and the device automatically adjusts the room's air conditioner, humidifier, lighting, etc. based on the received instructions, allowing the user to sleep in a comfortable environment.

[0204] Furthermore, the server calculates a sleep score from the collected vital data and indoor environmental data, and uses the results as feedback to reflect in future environmental settings. For example, by fine-tuning the next settings based on the optimal environmental settings obtained from past data, a more accurate environment can be provided.

[0205] When a user inputs questions or concerns about sleep, the device sends the information to a server, which analyzes the data and generates specific recommendations for action, such as "don't use your smartphone before bed" or "try breathing exercises to relax," which are then provided to the user via the device.

[0206] As a concrete example, consider the case where a user wakes up in the middle of the night. Because the user is wearing a wearable device, the server immediately detects an increase in heart rate, and the emotion engine detects the user's anxiety. The server analyzes this data and generates and sends to the device an instruction to lower the temperature by one degree to help the user fall back into deep sleep. The device receives this instruction and automatically adjusts the air conditioning settings, allowing the user to return to sleep in a comfortable environment.

[0207] As described above, the present invention provides an integrated system that improves sleep quality by analyzing the user's emotional state using an emotion engine in addition to the user's vital data and indoor environmental data, and by optimally configuring the environment in real time.

[0208] The processing flow will be explained below.

[0209] Step 1:

[0210] The server receives vital data (heart rate, body temperature, respiratory rate, etc.) from the user's wearable device every 10 minutes, using data communication via Bluetooth or Wi-Fi.

[0211] Step 2:

[0212] The server receives indoor environment data every minute from sensors installed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor). The data from each sensor is acquired via API and stored in a database.

[0213] Step 3:

[0214] The server analyzes the collected vital data and indoor environmental data to evaluate the user's current condition. For example, if the user's heart rate is high, it may determine that the user is feeling stressed.

[0215] Step 4:

[0216] The emotion engine analyzes the user's emotions from their voice and facial expressions and sends the results to the server. For example, it can detect stress or anxiety from the user's tone of voice and choice of words.

[0217] Step 5:

[0218] The server integrates vital data, indoor environment data, and emotional data to generate optimal environmental settings (e.g., temperature 22°C, humidity 45%, illuminance 0 lumens), allowing users to achieve the optimal sleeping environment.

[0219] Step 6:

[0220] The server sends instructions to the terminal to apply the generated environment settings. For example, it sends instructions to set the air conditioner temperature to 22 degrees and the humidifier humidity to 45%.

[0221] Step 7:

[0222] The device adjusts indoor devices such as air conditioners, humidifiers, and lighting based on the received instructions, for example, changing the temperature setting of the air conditioner and turning off the lights.

[0223] Step 8:

[0224] The server calculates a sleep score from the collected vital data and indoor environmental data, which is compared with past data to evaluate sleep quality.

[0225] Step 9:

[0226] The server will provide feedback to the next environment settings based on the sleep score, which will result in more accurate settings from the next time onwards, further optimizing the user's sleep environment.

[0227] Step 10:

[0228] When users input their sleep-related questions or concerns by voice or text, the device receives them and sends them to the server. For example, "I can't fall asleep easily" or "I'm not sleeping well."

[0229] Step 11:

[0230] The server analyzes the received questions and concerns and generates specific advice and action suggestions, such as "don't use electronic devices before bed" or "try breathing exercises to relax."

[0231] Step 12:

[0232] The device notifies the user of any advice or suggested actions received from the server, which are played aloud through the voice assistant or displayed as text through the app's chat function.

[0233] As a concrete example, consider the case where a user wakes up in the middle of the night. Because the user is wearing a wearable device, the server immediately detects an increase in heart rate, and the emotion engine detects the user's anxiety. The server analyzes this data and generates and sends to the device an instruction to lower the temperature by one degree to help the user fall back into deep sleep. The device receives this instruction and automatically adjusts the air conditioning settings, allowing the user to return to sleep in a comfortable environment.

[0234] These are the specific processing steps of this system. At each step, the server, device, and user each play their necessary roles and work together to optimize the sleep environment.

[0235] Example 2

[0236] 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."

[0237] Conventional sleep environment improvement systems collect users' vital data and indoor environmental data to provide an optimal environment, but do not take their emotional state into account when making adjustments, which means they are unable to fully improve the quality of their sleep.In addition, they lack the functionality to properly analyze users' emotional state and sleep-related questions and concerns, and to suggest actions, which means they are unable to achieve comprehensive sleep improvement.

[0238] 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.

[0239] In this invention, the server includes means for collecting vital data, means for collecting indoor environmental data, means for analyzing the collected vital data and indoor environmental data and generating optimal environmental settings, means for sending instructions for applying the generated environmental settings, means for receiving the instructions and adjusting the indoor environment, means for collecting user voice and facial expression data and analyzing emotions, and means for fine-tuning the environmental settings based on the analyzed emotional data. This enables real-time environmental settings that take the user's emotional state into consideration, thereby improving overall sleep quality.

[0240] "Vital data" refers to data that indicates the physiological state of the user, such as heart rate, body temperature, and respiratory rate.

[0241] "Indoor environment data" refers to data that indicates the environmental conditions of the room where the user is located, such as temperature, humidity, illuminance, noise, and wind speed.

[0242] "Analysis" is the process of evaluating the current situation based on collected data and deriving optimal actions and settings.

[0243] "Environmental settings" are instructions for adjusting the operating conditions of home appliances such as air conditioners, humidifiers, and lighting to achieve a comfortable environment for the user.

[0244] "Means for sending instructions" refers to the processes and techniques for transmitting the environmental settings generated by the server to terminals such as air conditioners, humidifiers, and lights.

[0245] "Emotional data" is data that indicates the user's emotions, obtained by analyzing the user's voice and facial expressions.

[0246] The "emotion engine" is a system that uses deep learning and machine learning algorithms to analyze emotions based on the user's voice and facial expression data.

[0247] The "sleep score" is an index used to evaluate a user's sleep quality based on collected vital data and indoor environmental data.

[0248] "Action suggestions" provide specific advice and recommended actions to help users get a good night's sleep.

[0249] "Feedback" is the process of using past data and results to provide information to further optimize the next environment settings.

[0250] This invention combines a system that collects and analyzes the user's vital data and indoor environmental data in real time to provide the optimal sleeping environment with an emotion engine that recognizes and analyzes the user's emotions. This system makes it possible to set environmental settings and suggest actions that take into account not only the user's physiological data but also their emotional state.

[0251] First, before the user goes to bed, the system collects the data necessary to create a comfortable indoor environment for that person. Vital data (heart rate, body temperature, respiratory rate, etc.) is received in real time from the wearable device worn by the user and sent to the terminal. The terminal collects this data and also collects indoor environment data from sensors placed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor). This collected data is uploaded to a server via the Internet.

[0252] The server analyzes the received vital data and indoor environmental data using machine learning algorithms. For example, if the heart rate is stable, it determines that the user is likely to fall into deep sleep and sets the room temperature to 22 degrees. It also adjusts the environment by turning on the humidifier if the humidity is too low, or by turning off the lights if the brightness is too high.

[0253] Next, the server also collects the user's emotional data. The user's voice and facial expression data are collected via the device and analyzed by the emotion engine. This emotion engine uses a deep learning model to recognize the user's emotions (happiness, sadness, stress, etc.). The analysis results are sent to the server, and the environment settings are fine-tuned based on the emotional data. For example, if it is determined that the user is feeling stressed, relaxing environment settings (such as slightly lowering the temperature or enabling silent mode) will be generated.

[0254] The generated environmental settings are sent from the server to the device, which then automatically adjusts the room's air conditioner, humidifier, lighting, etc. based on the received instructions, allowing the user to sleep in a comfortable environment.

[0255] The server then calculates a sleep score from the collected vital data and indoor environmental data. The sleep score is compared with past data and reflected in the next environment settings. This feedback allows for more accurate environment settings.

[0256] When users input their sleep-related questions or concerns into the device, the information is sent to a server. The server then uses a generative AI model to analyze the input information, generate specific action suggestions, and provide them to the user via the device. For example, specific advice such as "don't use your smartphone before bed" or "try breathing exercises to relax" is provided.

[0257] For example, if a user wakes up in the middle of the night, the server will immediately detect an increase in heart rate because the user is wearing a wearable device, and the emotion engine will detect the user's anxiety. The server will analyze this data and generate an instruction to lower the temperature by one degree to help the user fall back into deep sleep, which will be sent to the device. The device will then receive this instruction and automatically adjust the air conditioning settings, allowing the user to return to sleep in a comfortable environment.

[0258] Examples of prompts include:

[0259] "I've been waking up a lot lately in the middle of the night. How can I get into a deep sleep?"

[0260] As described above, the present invention provides an integrated system that analyzes the user's emotional state using an emotion engine in addition to the user's vital data and indoor environmental data, thereby optimizing the environment settings in real time and improving sleep quality.

[0261] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0262] Step 1:

[0263] The user puts on the wearable device before going to bed, and the device measures vital data such as heart rate, body temperature, and respiratory rate in real time and transmits them to a device via Bluetooth.

[0264] Input: Vital data from the user

[0265] Output: Vital data sent to the device

[0266] Specific operation: Heart rate and body temperature data are measured at regular intervals and sent to the device via Bluetooth communication.

[0267] Step 2:

[0268] The device collects indoor environment data from sensors placed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor), and this data is streamed to the device.

[0269] Input: Environmental data from indoor sensors

[0270] Output: Environmental data aggregated on the device

[0271] Specific operation: Environmental data is acquired from each sensor at regular intervals and stored in the device's data storage.

[0272] Step 3:

[0273] The device uploads the collected vital data and indoor environmental data to a server via the Internet. This data is transmitted encrypted.

[0274] Input: Vital and environmental data stored on the device

[0275] Output: Data uploaded to the server

[0276] Specific operation: The data is packaged in a batch process and sent to the server using the SSL / TLS protocol.

[0277] Step 4:

[0278] The server analyzes the received vital data and indoor environmental data using machine learning algorithms, for example, to estimate the user's sleep depth from heart rate fluctuations.

[0279] Input: Uploaded vital and environmental data

[0280] Output: Optimal Environment Settings

[0281] Specific operation: Based on the collected data, a machine learning model is used to calculate environmental adjustment values ​​(temperature, humidity, etc.).

[0282] Step 5:

[0283] The device collects the user's voice and facial expression data. Voice data is acquired through a microphone, and facial expression data is acquired through a camera. These data are pre-processed on the device and sent to the emotion engine.

[0284] Input: Voice data and facial expression data from the user

[0285] Output: Preprocessed speech and facial expression data

[0286] Specific operations: Performs preprocessing such as removing noise from the audio and extracting facial features, preparing it for transmission to the emotion engine.

[0287] Step 6:

[0288] The server analyzes the user's emotions using an emotion engine, classifies emotions (such as joy, sadness, stress) using a deep learning model, and sends the results to the server.

[0289] Input: Preprocessed speech and facial expression data

[0290] Output: Parsed emotion data

[0291] What it does: Runs a deep learning emotion classification model to identify emotional states.

[0292] Step 7:

[0293] The server then uses the emotional data to fine-tune environmental settings, for example, lowering the room temperature slightly or enabling quiet mode if the user is feeling stressed.

[0294] Input: Parsed emotion data

[0295] Output: Tweaked preferences

[0296] Specific behavior: Further adjust existing environment settings taking into account sentiment analysis results.

[0297] Step 8:

[0298] The server sends the generated environmental settings to the device, which then automatically controls the air conditioner, humidifier, lighting, etc. based on the received instructions.

[0299] Input: Generated environment settings

[0300] Output: Operation instructions for home appliances via terminal

[0301] Specific operation: Sends control signals to each home appliance and changes its settings.

[0302] Step 9:

[0303] The server calculates the user's sleep score based on vital and environmental data collected throughout the night, and this score is fed back as the basis for the next environmental setting.

[0304] Input: overnight vital and environmental data

[0305] Output: Sleep score and feedback for next settings

[0306] Specific operation: Analyzes data, evaluates sleep quality, and calculates feedback values ​​to be reflected in the next settings.

[0307] Step 10:

[0308] When users input their sleep-related questions or concerns, the information is sent from the device to a server, which then uses a generative AI model to analyze the input information and generate and provide specific action suggestions.

[0309] Input: Questions and concerns from users

[0310] Output: Specific action suggestions

[0311] Specific operation: The generative AI model analyzes the prompt sentence, generates appropriate advice, and provides it to the user.

[0312] Example prompt sentence:

[0313] "I've been waking up a lot lately in the middle of the night. How can I get into a deep sleep?"

[0314] (Application example 2)

[0315] 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."

[0316] There is a need to provide a comfortable environment for customers in physical stores, but conventional systems have not been able to set the environment taking into account the customer's vital data or emotional state.Furthermore, they have not incorporated feedback on the environment settings or action suggestions based on emotional data analysis, which has limited the comfort they can provide.

[0317] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting vital data, means for collecting indoor environmental data, means for analyzing the collected vital data and indoor environmental data and generating optimal environmental settings, means for sending instructions for applying the generated environmental settings, means for receiving the instructions and adjusting the indoor environment, means for analyzing emotional data, means for generating environmental settings based on the customer's emotional state, and means for applying the generated environmental settings to lighting, sound, temperature, and humidity settings in the store. This makes it possible to perform optimal environmental settings in real time based on the customer's vital data and emotional data. Furthermore, by providing feedback to the next environmental settings based on the comfort score, it is possible to continuously provide an optimal comfortable environment and further increase customer satisfaction by suggesting actions based on customer inquiries.

[0318] "Vital data" refers to physiological data such as a customer's heart rate, body temperature, and respiratory rate.

[0319] "Indoor environment data" refers to data related to indoor environmental parameters such as temperature, humidity, lighting, noise, and wind speed.

[0320] "Environmental settings" refers to various settings to provide an optimal indoor environment based on collected and analyzed vital data and indoor environmental data.

[0321] "Emotional data" refers to data about a customer's emotional state (e.g., stress, relaxation, etc.) analyzed from voice and facial expressions.

[0322] "Emotional state" refers to the emotions and psychological state that a customer is currently experiencing.

[0323] "Lighting" refers to artificial lighting used to adjust the brightness of a room.

[0324] "Acoustics" refers to the settings for music and other sounds played in the room.

[0325] "Temperature and humidity settings" refers to settings for adjusting the temperature and humidity in the room.

[0326] "Comfort score" refers to a numerical value that indicates the comfort felt by a customer, calculated based on vital data and indoor environmental data.

[0327] "Action suggestions" refer to specific action suggestions provided to customers based on data analysis.

[0328] An embodiment of the present invention is a system for providing a comfortable environment for customers in a physical store. This system is implemented in the following manner.

[0329] First, vital data (heart rate, body temperature, respiratory rate, etc.) is collected from the wearable device worn by the customer. This data is uploaded to a server in real time. At the same time, indoor environmental data is collected from sensors placed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor).

[0330] The server analyzes the collected vital data and indoor environmental data to generate optimal environmental settings using data analysis tools such as Python or R. For example, if the heart rate is high, it determines that the customer is feeling stressed and generates instructions to lower the room temperature. It also activates the humidifier if the humidity is too low, and turns off the lights if the brightness is too high.

[0331] Furthermore, the server uses an emotion engine to take into account the customer's emotions. The emotion engine analyzes emotions from the customer's voice and facial expressions and sends the results to the server. For example, if it determines that the customer is feeling stressed, it generates relaxing environmental settings (such as lowering the temperature slightly or enabling quiet mode). The emotion engine uses voice analysis software (such as Google (registered trademark) Speech-to-Text API) and facial expression analysis software (such as OpenFace).

[0332] Next, the server sends instructions to the device to apply these environmental settings. Based on the received instructions, the device automatically adjusts the room's air conditioner, humidifier, lighting, etc. This is done using an IoT device management platform (such as AWS (registered trademark) IoT Core). This allows customers to spend their time in a comfortable environment.

[0333] Furthermore, the server calculates a comfort score from the collected vital data and indoor environmental data, and uses the result as feedback to reflect in future environmental settings. For example, by fine-tuning the next settings based on the optimal environmental settings obtained from past data, a more accurate environment can be provided.

[0334] When a customer inputs a question or concern about comfort, the device sends the information to the server, which analyzes it and generates specific action suggestions, such as "take breaks at regular intervals" or "try stretching to relax," which are provided to the customer via the device.

[0335] Examples and prompts

[0336] As a specific application example, the following prompt sentence can be used to analyze the customer's condition using an AI model and set the optimal environment.

[0337] Specific examples

[0338] Suppose a customer wearing a wearable device enters a store. The customer says, "It's a little hot in this store," and their face looks tired. In this case, the system analyzes the customer's voice and facial expression and sets the following environment settings:

[0339] Set the temperature to 22 degrees.

[0340] Adjust the lighting to be slightly darker.

[0341] Play relaxation music.

[0342] Prompt statement

[0343] "Analyze the customer's emotional state from their voice and facial expressions, and then set the optimal indoor environment based on the results. For example, if the customer says they're hot, lower the temperature. If they look tired, dim the lights and play relaxing music."

[0344] This allows us to build a system that maximizes customer comfort in brick-and-mortar stores. By analyzing customers' vital signs and emotional data in real time and setting the optimal environment, we can continuously provide a high level of comfort.

[0345] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0346] Step 1:

[0347] Vital data (heart rate, body temperature, respiratory rate, etc.) is collected from the wearable device worn by the user. This data is uploaded to a server in real time. The input is the vital data obtained from the wearable device, and this data is temporarily stored as an output that is accumulated on the server.

[0348] Step 2:

[0349] Indoor environment data is collected from sensors placed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor). These data are sent to the server and serve as input to understand the current state of environmental parameters. As an output, the collected indoor environment data is stored on the server.

[0350] Step 3:

[0351] The server analyzes the collected vital data and indoor environment data to generate optimal environmental settings. The analysis is performed using data analysis tools (such as Python or R). The input is the data collected in steps 1 and 2, and the output is the environmental settings generated based on this data. For example, if the heart rate is high, the output will be to lower the room temperature.

[0352] Step 4:

[0353] The server uses an emotion engine to analyze the user's emotional data. It identifies the user's emotional state by analyzing their voice and facial expressions. The input is emotion data obtained from voice analysis software (such as Google Speech-to-Text API) and facial expression analysis software (such as OpenFace). The output is the analyzed emotional state, such as whether the user is stressed or relaxed.

[0354] Step 5:

[0355] The server comprehensively analyzes vital data, indoor environment data, and emotional data to regenerate optimal environmental settings. The input is the data obtained at each step, and the output is the optimal environmental settings determined comprehensively. For example, the output may set the room temperature to 22 degrees, dim the lights, and play relaxation music.

[0356] Step 6:

[0357] The server sends instructions to the device to apply the generated configuration. The input is the configuration generated in step 5, and the output is data that indicates the configuration. This is done using an IoT device management platform (such as AWS IoT Core).

[0358] Step 7:

[0359] The device automatically adjusts the room's air conditioner, humidifier, lighting, etc. based on instructions received from the server. The input is the environmental setting instructions received from the server, and the output is the actual adjustment of the physical device. This operation is performed using the connected IoT device.

[0360] Step 8:

[0361] The server receives inquiries from users and analyzes their content. Natural language processing (NLP) technology is used for the analysis. The input is data related to the user's questions and concerns, and the output is the analysis results. For example, specific action suggestions, such as how to take breaks, are generated.

[0362] Step 9:

[0363] The server calculates a comfort score from the collected vital data and indoor environmental data, and reflects the results in future environmental settings. The input is the data collected up to that point, and the output is feedback information that will be used in the next environmental settings. For example, optimal room temperature and humidity settings can be fine-tuned based on past data.

[0364] Step 10:

[0365] The server provides the user with action suggestions. The input is the action suggestion data analyzed in step 8, and the output is the specific suggestion to be conveyed to the user. For example, advice such as "take a break at regular intervals" or "try stretching to relax" is provided.

[0366] In this way, the system of the present invention can implement a series of processes to maximize customer comfort.

[0367] 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.

[0368] 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.

[0369] 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.

[0370] [Second embodiment]

[0371] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0372] 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.

[0373] 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).

[0374] 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.

[0375] 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.

[0376] 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).

[0377] 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.

[0378] 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.

[0379] 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.

[0380] 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.

[0381] 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.

[0382] 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."

[0383] This invention relates to a system that collects and analyzes vital data and indoor environmental data in real time to provide an optimal sleeping environment for users in order to improve their sleep quality. This system sets optimal environmental settings based on the user's vital data and indoor environmental data, and can also provide specific action suggestions in response to the user's questions and concerns.

[0384] First, before the user goes to bed, the system collects the data necessary to create a comfortable indoor environment for that person. Vital data (heart rate, body temperature, respiratory rate, etc.) is received in real time from the wearable device worn by the user and uploaded to the server. In addition, indoor environmental data is collected from sensors placed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor).

[0385] The server analyzes this data and generates optimal environmental settings. For example, if the heart rate is stable, it determines that the user is likely to fall into deep sleep and sets the room temperature to 22 degrees. If the humidity is too low, it turns on the humidifier, and if the brightness is too high, it turns off the lights.

[0386] The server then sends instructions to the device to apply these environmental settings, and the device automatically adjusts the room's air conditioner, humidifier, lighting, etc. based on the received instructions, allowing the user to sleep in a comfortable environment.

[0387] Furthermore, the server calculates a sleep score from the collected vital data and indoor environmental data, and uses the results as feedback to reflect in future environmental settings. For example, by fine-tuning the next settings based on the optimal environmental settings obtained from past data, a more accurate environment can be provided.

[0388] When a user inputs questions or concerns about sleep, the device sends the information to a server, which analyzes the data and generates specific recommendations for action, such as "don't use your smartphone before bed" or "try breathing exercises to relax," which are then provided to the user via the device.

[0389] As a concrete example, consider the case where a user wakes up in the middle of the night. Because the user is wearing a wearable device, the server immediately detects an increase in heart rate. The server analyzes this data and generates and sends to the device an instruction to lower the temperature by one degree to help the user fall back into deep sleep. The device receives this instruction and automatically adjusts the air conditioning settings, allowing the user to return to sleep in a comfortable environment.

[0390] As described above, the present invention provides a specific system for improving sleep quality by setting optimal environmental settings in real time based on the user's vital data and indoor environmental data.

[0391] The processing flow will be explained below.

[0392] Step 1:

[0393] The server receives vital data (heart rate, body temperature, respiratory rate, etc.) from the user's wearable device every 10 minutes, using data communication via Bluetooth or Wi-Fi.

[0394] Step 2:

[0395] The server receives indoor environment data every minute from sensors installed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor). The data from each sensor is acquired via API and stored in a database.

[0396] Step 3:

[0397] The server analyzes the collected vital data and indoor environmental data to evaluate the current state of the user. For example, if the heart rate is high, it may indicate that the user is feeling stressed and it may be necessary to lower the temperature.

[0398] Step 4:

[0399] The server generates optimal environmental settings (e.g., temperature 22°C, humidity 45%, illuminance 0 lumens) based on the analysis results, allowing users to obtain the optimal sleeping environment.

[0400] Step 5:

[0401] The server sends instructions to the device to apply the generated environment settings, such as setting the air conditioner temperature to 22 degrees and the humidifier humidity to 45%.

[0402] Step 6:

[0403] Based on the received instructions, the device adjusts indoor appliances such as air conditioners, humidifiers, and lighting. It analyzes the received data and changes the settings of each appliance accordingly.

[0404] Step 7:

[0405] The server calculates a sleep score from the collected vital data and indoor environmental data, which is compared with past data to evaluate sleep quality.

[0406] Step 8:

[0407] The server will provide feedback to the next environment settings based on the sleep score, which will result in more accurate settings from the next time onwards, further optimizing the user's sleep environment.

[0408] Step 9:

[0409] When a user enters a question or concern about sleep via voice or text, the device receives it and sends it to the server. For example, "I can't fall asleep easily."

[0410] Step 10:

[0411] The server analyzes the received questions and concerns and generates specific advice and action suggestions, such as "Avoid using your smartphone before going to bed."

[0412] Step 11:

[0413] The device notifies the user of any advice or suggested actions received from the server, which are played aloud through the voice assistant or displayed as text through the app's chat function.

[0414] These are the specific processing steps of this system. At each step, the server, device, and user each play their necessary roles and work together to optimize the sleep environment.

[0415] Example 1

[0416] 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."

[0417] In modern living environments, many people find it difficult to get a good night's sleep, which negatively impacts their health and quality of life. Conventional technologies lack systems that can collect and analyze users' vital signs and indoor environmental data in real time and provide optimal sleeping environments based on that data. Furthermore, they lack a means to provide appropriate action suggestions in response to sleep-related questions and problems, making it difficult for users to efficiently create a comfortable sleeping environment.

[0418] 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.

[0419] In this invention, the server includes means for collecting vital data from the wearable device, means for collecting indoor environmental data from a detector placed in the room, means for analyzing the collected vital data and indoor environmental data and generating optimal environmental settings, means for transmitting instructions for applying the generated environmental settings, and means for receiving the instructions and automatically adjusting the indoor environment. This enables comprehensive analysis of the user's vital data and indoor environmental data and automatic configuration of the optimal environmental settings in real time. It is also possible to receive inquiries from the user and provide specific action suggestions based on the analysis results, thereby improving the user's sleep quality.

[0420] A "wearable device" is an electronic device that can collect vital data such as heart rate, body temperature, and respiratory rate in real time by being worn by the user.

[0421] "Detector" refers to a sensor installed to collect environmental data such as indoor temperature, humidity, light, noise, and wind speed.

[0422] "Vital data" refers to biometric information related to the user's health status, such as heart rate, body temperature, and respiratory rate.

[0423] "Indoor environment data" refers to data necessary to create a comfortable sleeping environment for the user, such as the temperature, humidity, illuminance, noise, and wind speed in the room.

[0424] "Analysis" refers to the process of comprehensively evaluating the collected vital data and indoor environmental data to derive optimal environmental settings.

[0425] "Environmental settings" refers to adjusting settings such as air conditioning, humidifiers, and lighting to help users sleep comfortably.

[0426] An "instruction" is a specific operation command for transmitting the environmental settings generated by the server to the terminal.

[0427] The "sleep evaluation value" is a numerical value that represents the quality of the user's sleep, calculated from the collected vital data and indoor environment data.

[0428] "Action suggestions" are suggestions based on the analysis results for specific actions that users can take to get a good night's sleep.

[0429] The present invention provides a system for improving the quality of a user's sleep by collecting and analyzing data in real time using a wearable device and sensors placed in a room, and providing an optimal sleeping environment. Specific embodiments are described below.

[0430] First, the user puts on the wearable device before going to bed. The device collects real-time vital data such as the user's heart rate, body temperature, and respiratory rate and uploads it to a server. Additionally, temperature, humidity, light, noise, and wind speed sensors placed in the room collect indoor environmental data. All of this data is sent to the server.

[0431] The server analyzes the received vital data and indoor environmental data. For example, if the user's heart rate is stable, it determines that the user is likely to fall into deep sleep and sets the room temperature to 22 degrees. It also generates instructions to turn on the humidifier if the humidity is too low, or to turn off the lights if the brightness is too high.

[0432] The server then sends instructions to the device to apply the generated environmental settings, and the device automatically adjusts the room's appliances, such as the air conditioner, humidifier, and lighting, based on the received instructions, allowing the user to sleep in a comfortable environment.

[0433] The server then calculates a sleep score from the collected vital data and indoor environmental data. This sleep score is reflected as feedback in future environment settings. Specifically, by fine-tuning the environment settings based on past data, a more accurate environment can be provided.

[0434] When a user inputs a question or concern about sleep, the device sends the information to the server. The server then analyzes the information using a generative AI model and generates specific action suggestions based on the analysis results. For example, specific advice such as "don't use your smartphone before bed" or "try breathing exercises to relax" can be generated and provided to the user via the device.

[0435] As a concrete example, consider the case where a user wakes up in the middle of the night. When an increase in heart rate is detected from the wearable device worn by the user, the server immediately analyzes this data and sends an instruction to the device to lower the temperature by one degree to help the user fall into deep sleep again. The device receives this instruction and automatically adjusts the air conditioning settings, allowing the user to sleep in a comfortable environment again.

[0436] Here is an example prompt:

[0437] "Generate specific action suggestions to help the user enter deep sleep. For example, avoid using your phone before bed or try relaxing breathing exercises."

[0438] As described above, the present invention provides a specific system for improving sleep quality by setting optimal environmental settings in real time based on the user's vital data and indoor environmental data.

[0439] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0440] Step 1:

[0441] Users wear a wearable device that collects vital data such as heart rate, body temperature, and respiratory rate in real time.

[0442] Input: Real-time data of heart rate, body temperature, and respiratory rate

[0443] Output: Vital data sent from the wearable device

[0444] Specific operation: Device data is sent from the device worn on the user's wrist to the server via wireless communication such as Bluetooth.

[0445] Step 2:

[0446] Sensors placed in the room collect indoor environmental data such as temperature, humidity, illuminance, noise, and wind speed and send it to a server.

[0447] Input: Real-time data of temperature, humidity, light, noise, and wind speed

[0448] Output: Indoor environment data sent from the sensor

[0449] Specific operation: The sensor collects data and sends it to a server via Wi-Fi or other wireless communication.

[0450] Step 3:

[0451] The server receives and analyzes vital data from the wearable device and indoor environment data from sensors in the room.

[0452] Input: vital data, indoor environment data

[0453] Output: Recommended environment settings for analysis results

[0454] How it works: The server analyzes the data, estimates the depth of sleep from the user's heart rate and body temperature, and calculates the optimal temperature, humidity, and light settings.

[0455] Step 4:

[0456] The server generates instructions for optimal environment settings based on the analysis results and sends them to the device.

[0457] Input: Analysis results

[0458] Output: Setting instructions for air conditioners, humidifiers, lighting, etc.

[0459] Specific actions: The server generates specific instructions such as setting the air conditioner to 22 degrees, turning on the humidifier, or turning off the lights.

[0460] Step 5:

[0461] The device automatically adjusts the air conditioner, humidifier, lighting, etc. based on the environmental setting instructions received from the server.

[0462] Input: Setting instructions from the server

[0463] Output: Adjusted indoor environment

[0464] Specific actions: The device sets the air conditioner temperature to 22 degrees, turns on the humidifier, and turns off the lights.

[0465] Step 6:

[0466] The server calculates a sleep score from the collected vital data and indoor environmental data and provides feedback for the next environmental settings.

[0467] Input: vital data, indoor environment data

[0468] Output: Sleep score, next setting data

[0469] Specific operation: The server uses an algorithm to calculate a sleep score based on past data and reflects the result in the next environment settings.

[0470] Step 7:

[0471] Users enter their sleep-related questions and concerns into the device, which then sends the information to the server.

[0472] Input: User text input

[0473] Output: The question data sent to the server

[0474] Specific operation: The user enters a question into the device, which then sends it to the server.

[0475] Step 8:

[0476] The server generates action suggestions for the user's questions and concerns and sends them to the device.

[0477] Input: Question data from users, analysis results of the generative AI model

[0478] Output: Specific action suggestions

[0479] Specific operation: The server analyzes the question using the generative AI model and sends suggested actions to the device, such as "don't use your smartphone before bed" or "try breathing exercises to relax."

[0480] Through these steps, the system provides an optimal sleeping environment in real time based on the user's vital data and indoor environmental data, and suggests specific actions to take in response to the user's questions.

[0481] (Application example 1)

[0482] 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."

[0483] Thanks to recent technological advances, autonomous vehicles are becoming more commonplace, but technology for providing a comfortable in-vehicle sleeping environment is still in its infancy, and there is a need for improved comfort during long-distance travel. To provide an optimal sleeping environment, it is necessary to collect and analyze the user's vital signs and the vehicle's internal environmental data in real time, and automatically adjust the environmental settings based on the results. However, such a system has not yet been realized. Therefore, the challenge is to develop a system that provides the optimal in-vehicle environmental settings for users to achieve quality sleep.

[0484] 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.

[0485] In this invention, the server includes means for collecting vital data, means for collecting interior environment data, means for analyzing the collected vital data and interior environment data and generating optimal environment settings, means for sending instructions for applying the generated environment settings, means for receiving the instructions and adjusting the interior environment, means for collecting environmental data inside the vehicle, and means for adjusting the air conditioning, lighting, and seat reclining angle inside the vehicle, thereby making it possible to provide optimal environment settings for the user to sleep comfortably in the vehicle.

[0486] "Vital data" refers to data related to a user's vital activities, such as heart rate, body temperature, and respiratory rate.

[0487] "Indoor environment data" refers to data relating to the indoor environment, such as indoor temperature, humidity, illuminance, noise, and wind speed.

[0488] "Means for analyzing and generating optimal environmental settings" refers to algorithms or programs that automatically generate optimal environmental settings for the user based on collected vital data and indoor environmental data.

[0489] "Means for sending instructions to apply the generated configuration" refers to a communication function for sending instructions to operate the configuration from the server to the appropriate device or equipment.

[0490] "Means for adjusting the indoor environment" refers to control devices and software for adjusting indoor equipment such as air conditioners, humidifiers, and lighting.

[0491] "Interior vehicle environmental data" refers to data related to the in-vehicle environment, such as the temperature, humidity, lighting intensity, and seat reclining angle inside an autonomous vehicle.

[0492] "Means for adjusting the air conditioning, lighting, and seat reclining angle inside the vehicle" refers to the control systems and software that automatically adjust the air conditioning, lighting, and seat position inside the vehicle.

[0493] "Means for inputting prompts to the generative AI model" refers to an interface or program for inputting necessary instructions or questions to the artificial intelligence model.

[0494] "Means for calculating sleep score" refers to algorithms or software that quantify a user's sleep quality based on collected vital data and indoor environmental data.

[0495] This invention is a system that collects and analyzes vital data and internal vehicle environmental data in real time to provide an optimal sleeping environment for users in autonomous vehicles, in order to improve their sleep quality. This system consists of the following main components:

[0496] 1. Vital data collection means: Vital data such as heart rate, body temperature, and respiratory rate are collected in real time from a wearable device (e.g., a smart watch) worn by the user.

[0497] 2. Means for collecting environmental data inside the vehicle: Various sensors such as temperature sensors, humidity sensors, and illuminance sensors are used to collect environmental data such as temperature, humidity, lighting intensity, and noise inside the vehicle.

[0498] 3. Data analysis and optimal environment generation: The server analyzes the collected vital and environmental data and generates optimal environmental settings for the user (for example, setting the temperature to 22°C and the humidity to 40%). At this time, it also calculates a sleep score based on past data and uses this as feedback to reflect in future settings.

[0499] 4. Instruction transmission means: The server transmits instructions to the vehicle's internal control system to apply the generated configuration. This communication is performed using wireless communication technologies such as Wi-Fi or Bluetooth.

[0500] 5. Environmental adjustment means: Automatically adjust the air conditioning, humidifier, lighting, seat reclining angle, etc. inside the vehicle, allowing users to enjoy an optimal sleeping environment inside the autonomous vehicle.

[0501] 6. Means for receiving and analyzing inquiries from users: Questions and concerns about sleep entered by users through their smartphone or vehicle interface are received and analyzed on the server.

[0502] 7. Means for generating and providing action suggestions: The server generates action suggestions based on the analysis results and provides them to the user according to the prompts entered into the generative AI model. For example, it provides specific advice such as "Don't use your smartphone before going to bed" or "Try breathing exercises to relax."

[0503] Specific examples

[0504] Suppose a user wears a smartwatch and reclines their seat to rest in an autonomous vehicle. The system detects that the user's heart rate begins to steadily decrease immediately after lying down. Analyzing this data in real time, the server sends instructions to adjust the interior temperature to 22 degrees. If the humidity is low, it also sends instructions to activate the humidifier. This allows the user to sleep in an optimal environment.

[0505] Example prompt for a generative AI model:

[0506] To ensure a user can sleep comfortably in an autonomous vehicle, calculate the optimal environmental settings based on the following data: Vital data includes heart rate, body temperature, and respiratory rate, while environmental data includes temperature, humidity, light intensity, noise, and wind speed. For example, if the heart rate is stable, the room temperature should be set to 22°C; if the humidity is too low, the humidifier should be turned on; and if the light intensity is high, the lights should be turned off. Output the appropriate environmental settings.

[0507] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0508] Step 1:

[0509] The user wears a smartwatch and sits in a reclining seat in an autonomous vehicle. At this point, the smartwatch measures vital data such as heart rate, body temperature, and respiratory rate in real time. This vital data is then transmitted from the wearable device to a terminal.

[0510] Input: Heart rate, body temperature, respiration rate (from wearable device)

[0511] Output: Real-time vital data

[0512] Step 2:

[0513] Temperature, humidity, and light sensors installed inside the vehicle measure environmental data inside the vehicle, which is then transmitted to the terminal.

[0514] Input: Temperature, humidity, illuminance (from in-car sensors)

[0515] Output: Real-time environmental data

[0516] Step 3:

[0517] The device sends the collected vital and environmental data to a server, which analyzes the data and determines the user's current condition.

[0518] Input: Vital data, environmental data (from terminal)

[0519] Output: Analysis data (user status)

[0520] Step 4:

[0521] Based on the analyzed data, the server inputs prompts into the generative AI model to generate optimal environment settings, taking into account feedback based on previous data.

[0522] Input: Analysis data (user status), prompt statement

[0523] Output: Optimal Environment Settings

[0524] Step 5:

[0525] The server sends the generated configuration to the device as instructions to apply it, which the device receives and sends to the in-car control system.

[0526] Input: Optimal environment settings (from server)

[0527] Output: Environmental adjustment instructions (from terminal to in-car control system)

[0528] Step 6:

[0529] Actuators inside the vehicle for air conditioning, lighting, humidifiers, seat reclining angles, etc. receive instructions from the terminal and automatically adjust to the specified settings.

[0530] Input: Environment adjustment instructions (from terminal)

[0531] Output: Controlled environment (temperature, humidity, light, seat angle)

[0532] Step 7:

[0533] Users input their sleep-related questions and concerns through a smartphone or vehicle interface, and the device then sends this information to a server.

[0534] Input: User's question or concern

[0535] Output: Query data (to server)

[0536] Step 8:

[0537] The server analyzes the user's query and generates appropriate action suggestions, also driven by a generative AI model, such as breathing exercises to help with relaxation.

[0538] Input: Question data (from user), analyzed by generative AI model

[0539] Output: Action suggestions

[0540] Step 9:

[0541] The server then sends the generated action suggestions to the device and provides them to the user, who can then take action to further improve their sleep quality.

[0542] Input: Action proposal (from server)

[0543] Output: Feedback to the user

[0544] 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.

[0545] This invention combines a system that collects and analyzes the user's vital data and indoor environmental data in real time to provide the optimal sleeping environment with an emotion engine that recognizes and analyzes the user's emotions. This system makes it possible to set environmental settings and suggest actions that take into account not only the user's physiological data but also their emotional state.

[0546] First, before the user goes to bed, the system collects the data necessary to create a comfortable indoor environment for that person. Vital data (heart rate, body temperature, respiratory rate, etc.) is received in real time from the wearable device worn by the user and uploaded to the server. In addition, indoor environmental data is collected from sensors placed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor).

[0547] The server analyzes this data and generates optimal environmental settings. For example, if the heart rate is stable, it determines that the user is likely to fall into deep sleep and sets the room temperature to 22 degrees. If the humidity is too low, it turns on the humidifier, and if the brightness is too high, it turns off the lights.

[0548] The system then takes the user's emotions into account. The emotion engine analyzes the user's voice and facial expressions to determine their emotions, and sends the results to the server. For example, if the system determines that the user is feeling stressed, it will generate relaxing environmental settings (such as lowering the temperature slightly or enabling silent mode).

[0549] The server then sends instructions to the device to apply these environmental settings, and the device automatically adjusts the room's air conditioner, humidifier, lighting, etc. based on the received instructions, allowing the user to sleep in a comfortable environment.

[0550] Furthermore, the server calculates a sleep score from the collected vital data and indoor environmental data, and uses the results as feedback to reflect in future environmental settings. For example, by fine-tuning the next settings based on the optimal environmental settings obtained from past data, a more accurate environment can be provided.

[0551] When a user inputs questions or concerns about sleep, the device sends the information to a server, which analyzes the data and generates specific recommendations for action, such as "don't use your smartphone before bed" or "try breathing exercises to relax," which are then provided to the user via the device.

[0552] As a concrete example, consider the case where a user wakes up in the middle of the night. Because the user is wearing a wearable device, the server immediately detects an increase in heart rate, and the emotion engine detects the user's anxiety. The server analyzes this data and generates and sends to the device an instruction to lower the temperature by one degree to help the user fall back into deep sleep. The device receives this instruction and automatically adjusts the air conditioning settings, allowing the user to return to sleep in a comfortable environment.

[0553] As described above, the present invention provides an integrated system that improves sleep quality by analyzing the user's emotional state using an emotion engine in addition to the user's vital data and indoor environmental data, and by optimally configuring the environment in real time.

[0554] The processing flow will be explained below.

[0555] Step 1:

[0556] The server receives vital data (heart rate, body temperature, respiratory rate, etc.) from the user's wearable device every 10 minutes, using data communication via Bluetooth or Wi-Fi.

[0557] Step 2:

[0558] The server receives indoor environment data every minute from sensors installed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor). The data from each sensor is acquired via API and stored in a database.

[0559] Step 3:

[0560] The server analyzes the collected vital data and indoor environmental data to evaluate the user's current condition. For example, if the user's heart rate is high, it may determine that the user is feeling stressed.

[0561] Step 4:

[0562] The emotion engine analyzes the user's emotions from their voice and facial expressions and sends the results to the server. For example, it can detect stress or anxiety from the user's tone of voice and choice of words.

[0563] Step 5:

[0564] The server integrates vital data, indoor environment data, and emotional data to generate optimal environmental settings (e.g., temperature 22°C, humidity 45%, illuminance 0 lumens), allowing users to achieve the optimal sleeping environment.

[0565] Step 6:

[0566] The server sends instructions to the terminal to apply the generated environment settings. For example, it sends instructions to set the air conditioner temperature to 22 degrees and the humidifier humidity to 45%.

[0567] Step 7:

[0568] The device adjusts indoor devices such as air conditioners, humidifiers, and lighting based on the received instructions, for example, changing the temperature setting of the air conditioner and turning off the lights.

[0569] Step 8:

[0570] The server calculates a sleep score from the collected vital data and indoor environmental data, which is compared with past data to evaluate sleep quality.

[0571] Step 9:

[0572] The server will provide feedback to the next environment settings based on the sleep score, which will result in more accurate settings from the next time onwards, further optimizing the user's sleep environment.

[0573] Step 10:

[0574] When users input their sleep-related questions or concerns by voice or text, the device receives them and sends them to the server. For example, "I can't fall asleep easily" or "I'm not sleeping well."

[0575] Step 11:

[0576] The server analyzes the received questions and concerns and generates specific advice and action suggestions, such as "don't use electronic devices before bed" or "try breathing exercises to relax."

[0577] Step 12:

[0578] The device notifies the user of any advice or suggested actions received from the server, which are played aloud through the voice assistant or displayed as text through the app's chat function.

[0579] As a concrete example, consider the case where a user wakes up in the middle of the night. Because the user is wearing a wearable device, the server immediately detects an increase in heart rate, and the emotion engine detects the user's anxiety. The server analyzes this data and generates and sends to the device an instruction to lower the temperature by one degree to help the user fall back into deep sleep. The device receives this instruction and automatically adjusts the air conditioning settings, allowing the user to return to sleep in a comfortable environment.

[0580] These are the specific processing steps of this system. At each step, the server, device, and user each play their necessary roles and work together to optimize the sleep environment.

[0581] Example 2

[0582] 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."

[0583] Conventional sleep environment improvement systems collect users' vital data and indoor environmental data to provide an optimal environment, but do not take their emotional state into account when making adjustments, which means they are unable to fully improve the quality of their sleep.In addition, they lack the functionality to properly analyze users' emotional state and sleep-related questions and concerns, and to suggest actions, which means they are unable to achieve comprehensive sleep improvement.

[0584] 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.

[0585] In this invention, the server includes means for collecting vital data, means for collecting indoor environmental data, means for analyzing the collected vital data and indoor environmental data and generating optimal environmental settings, means for sending instructions for applying the generated environmental settings, means for receiving the instructions and adjusting the indoor environment, means for collecting user voice and facial expression data and analyzing emotions, and means for fine-tuning the environmental settings based on the analyzed emotional data. This enables real-time environmental settings that take the user's emotional state into consideration, thereby improving overall sleep quality.

[0586] "Vital data" refers to data that indicates the physiological state of the user, such as heart rate, body temperature, and respiratory rate.

[0587] "Indoor environment data" refers to data that indicates the environmental conditions of the room where the user is located, such as temperature, humidity, illuminance, noise, and wind speed.

[0588] "Analysis" is the process of evaluating the current situation based on collected data and deriving optimal actions and settings.

[0589] "Environmental settings" are instructions for adjusting the operating conditions of home appliances such as air conditioners, humidifiers, and lighting to achieve a comfortable environment for the user.

[0590] "Means for sending instructions" refers to the processes and techniques for transmitting the environmental settings generated by the server to terminals such as air conditioners, humidifiers, and lights.

[0591] "Emotional data" is data that indicates the user's emotions, obtained by analyzing the user's voice and facial expressions.

[0592] The "emotion engine" is a system that uses deep learning and machine learning algorithms to analyze emotions based on the user's voice and facial expression data.

[0593] The "sleep score" is an index used to evaluate a user's sleep quality based on collected vital data and indoor environmental data.

[0594] "Action suggestions" provide specific advice and recommended actions to help users get a good night's sleep.

[0595] "Feedback" is the process of using past data and results to provide information to further optimize the next environment settings.

[0596] This invention combines a system that collects and analyzes the user's vital data and indoor environmental data in real time to provide the optimal sleeping environment with an emotion engine that recognizes and analyzes the user's emotions. This system makes it possible to set environmental settings and suggest actions that take into account not only the user's physiological data but also their emotional state.

[0597] First, before the user goes to bed, the system collects the data necessary to create a comfortable indoor environment for that person. Vital data (heart rate, body temperature, respiratory rate, etc.) is received in real time from the wearable device worn by the user and sent to the terminal. The terminal collects this data and also collects indoor environment data from sensors placed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor). This collected data is uploaded to a server via the Internet.

[0598] The server analyzes the received vital data and indoor environmental data using machine learning algorithms. For example, if the heart rate is stable, it determines that the user is likely to fall into deep sleep and sets the room temperature to 22 degrees. It also adjusts the environment by turning on the humidifier if the humidity is too low, or by turning off the lights if the brightness is too high.

[0599] Next, the server also collects the user's emotional data. The user's voice and facial expression data are collected via the device and analyzed by the emotion engine. This emotion engine uses a deep learning model to recognize the user's emotions (happiness, sadness, stress, etc.). The analysis results are sent to the server, and the environment settings are fine-tuned based on the emotional data. For example, if it is determined that the user is feeling stressed, relaxing environment settings (such as slightly lowering the temperature or enabling silent mode) will be generated.

[0600] The generated environmental settings are sent from the server to the device, which then automatically adjusts the room's air conditioner, humidifier, lighting, etc. based on the received instructions, allowing the user to sleep in a comfortable environment.

[0601] The server then calculates a sleep score from the collected vital data and indoor environmental data. The sleep score is compared with past data and reflected in the next environment settings. This feedback allows for more accurate environment settings.

[0602] When users input their sleep-related questions or concerns into the device, the information is sent to a server. The server then uses a generative AI model to analyze the input information, generate specific action suggestions, and provide them to the user via the device. For example, specific advice such as "don't use your smartphone before bed" or "try breathing exercises to relax" is provided.

[0603] For example, if a user wakes up in the middle of the night, the server will immediately detect an increase in heart rate because the user is wearing a wearable device, and the emotion engine will detect the user's anxiety. The server will analyze this data and generate an instruction to lower the temperature by one degree to help the user fall back into deep sleep, which will be sent to the device. The device will then receive this instruction and automatically adjust the air conditioning settings, allowing the user to return to sleep in a comfortable environment.

[0604] Examples of prompts include:

[0605] "I've been waking up a lot lately in the middle of the night. How can I get into a deep sleep?"

[0606] As described above, the present invention provides an integrated system that analyzes the user's emotional state using an emotion engine in addition to the user's vital data and indoor environmental data, thereby optimizing the environment settings in real time and improving sleep quality.

[0607] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0608] Step 1:

[0609] The user puts on the wearable device before going to bed, and the device measures vital data such as heart rate, body temperature, and respiratory rate in real time and transmits them to a device via Bluetooth.

[0610] Input: Vital data from the user

[0611] Output: Vital data sent to the device

[0612] Specific operation: Heart rate and body temperature data are measured at regular intervals and sent to the device via Bluetooth communication.

[0613] Step 2:

[0614] The device collects indoor environment data from sensors placed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor), and this data is streamed to the device.

[0615] Input: Environmental data from indoor sensors

[0616] Output: Environmental data aggregated on the device

[0617] Specific operation: Environmental data is acquired from each sensor at regular intervals and stored in the device's data storage.

[0618] Step 3:

[0619] The device uploads the collected vital data and indoor environmental data to a server via the Internet. This data is transmitted encrypted.

[0620] Input: Vital and environmental data stored on the device

[0621] Output: Data uploaded to the server

[0622] Specific operation: The data is packaged in a batch process and sent to the server using the SSL / TLS protocol.

[0623] Step 4:

[0624] The server analyzes the received vital data and indoor environmental data using machine learning algorithms, for example, to estimate the user's sleep depth from heart rate fluctuations.

[0625] Input: Uploaded vital and environmental data

[0626] Output: Optimal Environment Settings

[0627] Specific operation: Based on the collected data, a machine learning model is used to calculate environmental adjustment values ​​(temperature, humidity, etc.).

[0628] Step 5:

[0629] The device collects the user's voice and facial expression data. Voice data is acquired through a microphone, and facial expression data is acquired through a camera. These data are pre-processed on the device and sent to the emotion engine.

[0630] Input: Voice data and facial expression data from the user

[0631] Output: Preprocessed speech and facial expression data

[0632] Specific operations: Performs preprocessing such as removing noise from the audio and extracting facial features, preparing it for transmission to the emotion engine.

[0633] Step 6:

[0634] The server analyzes the user's emotions using an emotion engine, classifies emotions (such as joy, sadness, stress) using a deep learning model, and sends the results to the server.

[0635] Input: Preprocessed speech and facial expression data

[0636] Output: Parsed emotion data

[0637] What it does: Runs a deep learning emotion classification model to identify emotional states.

[0638] Step 7:

[0639] The server then uses the emotional data to fine-tune environmental settings, for example, lowering the room temperature slightly or enabling quiet mode if the user is feeling stressed.

[0640] Input: Parsed emotion data

[0641] Output: Tweaked preferences

[0642] Specific behavior: Further adjust existing environment settings taking into account sentiment analysis results.

[0643] Step 8:

[0644] The server sends the generated environmental settings to the device, which then automatically controls the air conditioner, humidifier, lighting, etc. based on the received instructions.

[0645] Input: Generated environment settings

[0646] Output: Operation instructions for home appliances via terminal

[0647] Specific operation: Sends control signals to each home appliance and changes its settings.

[0648] Step 9:

[0649] The server calculates the user's sleep score based on vital and environmental data collected throughout the night, and this score is fed back as the basis for the next environmental setting.

[0650] Input: overnight vital and environmental data

[0651] Output: Sleep score and feedback for next settings

[0652] Specific operation: Analyzes data, evaluates sleep quality, and calculates feedback values ​​to be reflected in the next settings.

[0653] Step 10:

[0654] When users input their sleep-related questions or concerns, the information is sent from the device to a server, which then uses a generative AI model to analyze the input information and generate and provide specific action suggestions.

[0655] Input: Questions and concerns from users

[0656] Output: Specific action suggestions

[0657] Specific operation: The generative AI model analyzes the prompt sentence, generates appropriate advice, and provides it to the user.

[0658] Example prompt sentence:

[0659] "I've been waking up a lot lately in the middle of the night. How can I get into a deep sleep?"

[0660] (Application example 2)

[0661] 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."

[0662] There is a need to provide a comfortable environment for customers in physical stores, but conventional systems have not been able to set the environment taking into account the customer's vital data or emotional state.Furthermore, they have not incorporated feedback on the environment settings or action suggestions based on emotional data analysis, which has limited the comfort they can provide.

[0663] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting vital data, means for collecting indoor environmental data, means for analyzing the collected vital data and indoor environmental data and generating optimal environmental settings, means for sending instructions for applying the generated environmental settings, means for receiving the instructions and adjusting the indoor environment, means for analyzing emotional data, means for generating environmental settings based on the customer's emotional state, and means for applying the generated environmental settings to lighting, sound, temperature, and humidity settings in the store. This makes it possible to perform optimal environmental settings in real time based on the customer's vital data and emotional data. Furthermore, by providing feedback to the next environmental settings based on the comfort score, it is possible to continuously provide an optimal comfortable environment and further increase customer satisfaction by suggesting actions based on customer inquiries.

[0664] "Vital data" refers to physiological data such as a customer's heart rate, body temperature, and respiratory rate.

[0665] "Indoor environment data" refers to data related to indoor environmental parameters such as temperature, humidity, lighting, noise, and wind speed.

[0666] "Environmental settings" refers to various settings to provide an optimal indoor environment based on collected and analyzed vital data and indoor environmental data.

[0667] "Emotional data" refers to data about a customer's emotional state (e.g., stress, relaxation, etc.) analyzed from voice and facial expressions.

[0668] "Emotional state" refers to the emotions and psychological state that a customer is currently experiencing.

[0669] "Lighting" refers to artificial lighting used to adjust the brightness of a room.

[0670] "Acoustics" refers to the settings for music and other sounds played in the room.

[0671] "Temperature and humidity settings" refers to settings for adjusting the temperature and humidity in the room.

[0672] "Comfort score" refers to a numerical value that indicates the comfort felt by a customer, calculated based on vital data and indoor environmental data.

[0673] "Action suggestions" refer to specific action suggestions provided to customers based on data analysis.

[0674] An embodiment of the present invention is a system for providing a comfortable environment for customers in a physical store. This system is implemented in the following manner.

[0675] First, vital data (heart rate, body temperature, respiratory rate, etc.) is collected from the wearable device worn by the customer. This data is uploaded to a server in real time. At the same time, indoor environmental data is collected from sensors placed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor).

[0676] The server analyzes the collected vital data and indoor environmental data to generate optimal environmental settings using data analysis tools such as Python or R. For example, if the heart rate is high, it determines that the customer is feeling stressed and generates instructions to lower the room temperature. It also activates the humidifier if the humidity is too low, and turns off the lights if the brightness is too high.

[0677] In addition, the server uses an emotion engine to take into account the customer's emotions. The emotion engine analyzes emotions from the customer's voice and facial expressions and sends the results to the server. For example, if it determines that the customer is feeling stressed, it generates relaxing environmental settings (such as lowering the temperature slightly or enabling quiet mode). The emotion engine uses voice analysis software (such as Google Speech-to-Text API) and facial expression analysis software (such as OpenFace).

[0678] Next, the server sends instructions to the device to apply these environmental settings. The device automatically adjusts the room's air conditioner, humidifier, lighting, etc. based on the received instructions. This is done using an IoT device management platform (such as AWS IoT Core). This allows customers to spend their time in a comfortable environment.

[0679] Furthermore, the server calculates a comfort score from the collected vital data and indoor environmental data, and uses the result as feedback to reflect in future environmental settings. For example, by fine-tuning the next settings based on the optimal environmental settings obtained from past data, a more accurate environment can be provided.

[0680] When a customer inputs a question or concern about comfort, the device sends the information to the server, which analyzes it and generates specific action suggestions, such as "take breaks at regular intervals" or "try stretching to relax," which are provided to the customer via the device.

[0681] Examples and prompts

[0682] As a specific application example, the following prompt sentence can be used to analyze the customer's condition using an AI model and set the optimal environment.

[0683] Specific examples

[0684] Suppose a customer wearing a wearable device enters a store. The customer says, "It's a little hot in this store," and their face looks tired. In this case, the system analyzes the customer's voice and facial expression and sets the following environment settings:

[0685] Set the temperature to 22 degrees.

[0686] Adjust the lighting to be slightly darker.

[0687] Play relaxation music.

[0688] Prompt statement

[0689] "Analyze the customer's emotional state from their voice and facial expressions, and then set the optimal indoor environment based on the results. For example, if the customer says they're hot, lower the temperature. If they look tired, dim the lights and play relaxing music."

[0690] This allows us to build a system that maximizes customer comfort in brick-and-mortar stores. By analyzing customers' vital signs and emotional data in real time and setting the optimal environment, we can continuously provide a high level of comfort.

[0691] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0692] Step 1:

[0693] Vital data (heart rate, body temperature, respiratory rate, etc.) is collected from the wearable device worn by the user. This data is uploaded to a server in real time. The input is the vital data obtained from the wearable device, and this data is temporarily stored as an output that is accumulated on the server.

[0694] Step 2:

[0695] Indoor environment data is collected from sensors placed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor). These data are sent to the server and serve as input to understand the current state of environmental parameters. As an output, the collected indoor environment data is stored on the server.

[0696] Step 3:

[0697] The server analyzes the collected vital data and indoor environment data to generate optimal environmental settings. The analysis is performed using data analysis tools (such as Python or R). The input is the data collected in steps 1 and 2, and the output is the environmental settings generated based on this data. For example, if the heart rate is high, the output will be to lower the room temperature.

[0698] Step 4:

[0699] The server uses an emotion engine to analyze the user's emotional data. It identifies the user's emotional state by analyzing their voice and facial expressions. The input is emotion data obtained from voice analysis software (such as Google Speech-to-Text API) and facial expression analysis software (such as OpenFace). The output is the analyzed emotional state, such as whether the user is stressed or relaxed.

[0700] Step 5:

[0701] The server comprehensively analyzes vital data, indoor environment data, and emotional data to regenerate optimal environmental settings. The input is the data obtained at each step, and the output is the optimal environmental settings determined comprehensively. For example, the output may set the room temperature to 22 degrees, dim the lights, and play relaxation music.

[0702] Step 6:

[0703] The server sends instructions to the device to apply the generated configuration. The input is the configuration generated in step 5, and the output is data that indicates the configuration. This is done using an IoT device management platform (such as AWS IoT Core).

[0704] Step 7:

[0705] The device automatically adjusts the room's air conditioner, humidifier, lighting, etc. based on instructions received from the server. The input is the environmental setting instructions received from the server, and the output is the actual adjustment of the physical device. This operation is performed using the connected IoT device.

[0706] Step 8:

[0707] The server receives inquiries from users and analyzes their content. Natural language processing (NLP) technology is used for the analysis. The input is data related to the user's questions and concerns, and the output is the analysis results. For example, specific action suggestions, such as how to take breaks, are generated.

[0708] Step 9:

[0709] The server calculates a comfort score from the collected vital data and indoor environmental data, and reflects the results in future environmental settings. The input is the data collected up to that point, and the output is feedback information that will be used in the next environmental settings. For example, optimal room temperature and humidity settings can be fine-tuned based on past data.

[0710] Step 10:

[0711] The server provides the user with action suggestions. The input is the action suggestion data analyzed in step 8, and the output is the specific suggestion to be conveyed to the user. For example, advice such as "take a break at regular intervals" or "try stretching to relax" is provided.

[0712] In this way, the system of the present invention can implement a series of processes to maximize customer comfort.

[0713] 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.

[0714] 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.

[0715] 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.

[0716] [Third embodiment]

[0717] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0718] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0719] 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).

[0720] 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.

[0721] 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.

[0722] 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).

[0723] 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.

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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."

[0729] This invention relates to a system that collects and analyzes vital data and indoor environmental data in real time to provide an optimal sleeping environment for users in order to improve their sleep quality. This system sets optimal environmental settings based on the user's vital data and indoor environmental data, and can also provide specific action suggestions in response to the user's questions and concerns.

[0730] First, before the user goes to bed, the system collects the data necessary to create a comfortable indoor environment for that person. Vital data (heart rate, body temperature, respiratory rate, etc.) is received in real time from the wearable device worn by the user and uploaded to the server. In addition, indoor environmental data is collected from sensors placed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor).

[0731] The server analyzes this data and generates optimal environmental settings. For example, if the heart rate is stable, it determines that the user is likely to fall into deep sleep and sets the room temperature to 22 degrees. If the humidity is too low, it turns on the humidifier, and if the brightness is too high, it turns off the lights.

[0732] The server then sends instructions to the device to apply these environmental settings, and the device automatically adjusts the room's air conditioner, humidifier, lighting, etc. based on the received instructions, allowing the user to sleep in a comfortable environment.

[0733] Furthermore, the server calculates a sleep score from the collected vital data and indoor environmental data, and uses the results as feedback to reflect in future environmental settings. For example, by fine-tuning the next settings based on the optimal environmental settings obtained from past data, a more accurate environment can be provided.

[0734] When a user inputs questions or concerns about sleep, the device sends the information to a server, which analyzes the data and generates specific recommendations for action, such as "don't use your smartphone before bed" or "try breathing exercises to relax," which are then provided to the user via the device.

[0735] As a concrete example, consider the case where a user wakes up in the middle of the night. Because the user is wearing a wearable device, the server immediately detects an increase in heart rate. The server analyzes this data and generates and sends to the device an instruction to lower the temperature by one degree to help the user fall back into deep sleep. The device receives this instruction and automatically adjusts the air conditioning settings, allowing the user to return to sleep in a comfortable environment.

[0736] As described above, the present invention provides a specific system for improving sleep quality by setting optimal environmental settings in real time based on the user's vital data and indoor environmental data.

[0737] The processing flow will be explained below.

[0738] Step 1:

[0739] The server receives vital data (heart rate, body temperature, respiratory rate, etc.) from the user's wearable device every 10 minutes, using data communication via Bluetooth or Wi-Fi.

[0740] Step 2:

[0741] The server receives indoor environment data every minute from sensors installed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor). The data from each sensor is acquired via API and stored in a database.

[0742] Step 3:

[0743] The server analyzes the collected vital data and indoor environmental data to evaluate the current state of the user. For example, if the heart rate is high, it may indicate that the user is feeling stressed and it may be necessary to lower the temperature.

[0744] Step 4:

[0745] The server generates optimal environmental settings (e.g., temperature 22°C, humidity 45%, illuminance 0 lumens) based on the analysis results, allowing users to obtain the optimal sleeping environment.

[0746] Step 5:

[0747] The server sends instructions to the device to apply the generated environment settings, such as setting the air conditioner temperature to 22 degrees and the humidifier humidity to 45%.

[0748] Step 6:

[0749] Based on the received instructions, the device adjusts indoor appliances such as air conditioners, humidifiers, and lighting. It analyzes the received data and changes the settings of each appliance accordingly.

[0750] Step 7:

[0751] The server calculates a sleep score from the collected vital data and indoor environmental data, which is compared with past data to evaluate sleep quality.

[0752] Step 8:

[0753] The server will provide feedback to the next environment settings based on the sleep score, which will result in more accurate settings from the next time onwards, further optimizing the user's sleep environment.

[0754] Step 9:

[0755] When a user enters a question or concern about sleep via voice or text, the device receives it and sends it to the server. For example, "I can't fall asleep easily."

[0756] Step 10:

[0757] The server analyzes the received questions and concerns and generates specific advice and action suggestions, such as "Avoid using your smartphone before going to bed."

[0758] Step 11:

[0759] The device notifies the user of any advice or suggested actions received from the server, which are played aloud through the voice assistant or displayed as text through the app's chat function.

[0760] These are the specific processing steps of this system. At each step, the server, device, and user each play their necessary roles and work together to optimize the sleep environment.

[0761] Example 1

[0762] 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."

[0763] In modern living environments, many people find it difficult to get a good night's sleep, which negatively impacts their health and quality of life. Conventional technologies lack systems that can collect and analyze users' vital signs and indoor environmental data in real time and provide optimal sleeping environments based on that data. Furthermore, they lack a means to provide appropriate action suggestions in response to sleep-related questions and problems, making it difficult for users to efficiently create a comfortable sleeping environment.

[0764] 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.

[0765] In this invention, the server includes means for collecting vital data from the wearable device, means for collecting indoor environmental data from a detector placed in the room, means for analyzing the collected vital data and indoor environmental data and generating optimal environmental settings, means for transmitting instructions for applying the generated environmental settings, and means for receiving the instructions and automatically adjusting the indoor environment. This enables comprehensive analysis of the user's vital data and indoor environmental data and automatic configuration of the optimal environmental settings in real time. It is also possible to receive inquiries from the user and provide specific action suggestions based on the analysis results, thereby improving the user's sleep quality.

[0766] A "wearable device" is an electronic device that can collect vital data such as heart rate, body temperature, and respiratory rate in real time by being worn by the user.

[0767] "Detector" refers to a sensor installed to collect environmental data such as indoor temperature, humidity, light, noise, and wind speed.

[0768] "Vital data" refers to biometric information related to the user's health status, such as heart rate, body temperature, and respiratory rate.

[0769] "Indoor environment data" refers to data necessary to create a comfortable sleeping environment for the user, such as the temperature, humidity, illuminance, noise, and wind speed in the room.

[0770] "Analysis" refers to the process of comprehensively evaluating the collected vital data and indoor environmental data to derive optimal environmental settings.

[0771] "Environmental settings" refers to adjusting settings such as air conditioning, humidifiers, and lighting to help users sleep comfortably.

[0772] An "instruction" is a specific operation command for transmitting the environmental settings generated by the server to the terminal.

[0773] The "sleep evaluation value" is a numerical value that represents the quality of the user's sleep, calculated from the collected vital data and indoor environment data.

[0774] "Action suggestions" are suggestions based on the analysis results for specific actions that users can take to get a good night's sleep.

[0775] The present invention provides a system for improving the quality of a user's sleep by collecting and analyzing data in real time using a wearable device and sensors placed in a room, and providing an optimal sleeping environment. Specific embodiments are described below.

[0776] First, the user puts on the wearable device before going to bed. The device collects real-time vital data such as the user's heart rate, body temperature, and respiratory rate and uploads it to a server. Additionally, temperature, humidity, light, noise, and wind speed sensors placed in the room collect indoor environmental data. All of this data is sent to the server.

[0777] The server analyzes the received vital data and indoor environmental data. For example, if the user's heart rate is stable, it determines that the user is likely to fall into deep sleep and sets the room temperature to 22 degrees. It also generates instructions to turn on the humidifier if the humidity is too low, or to turn off the lights if the brightness is too high.

[0778] The server then sends instructions to the device to apply the generated environmental settings, and the device automatically adjusts the room's appliances, such as the air conditioner, humidifier, and lighting, based on the received instructions, allowing the user to sleep in a comfortable environment.

[0779] The server then calculates a sleep score from the collected vital data and indoor environmental data. This sleep score is reflected as feedback in future environment settings. Specifically, by fine-tuning the environment settings based on past data, a more accurate environment can be provided.

[0780] When a user inputs a question or concern about sleep, the device sends the information to the server. The server then analyzes the information using a generative AI model and generates specific action suggestions based on the analysis results. For example, specific advice such as "don't use your smartphone before bed" or "try breathing exercises to relax" can be generated and provided to the user via the device.

[0781] As a concrete example, consider the case where a user wakes up in the middle of the night. When an increase in heart rate is detected from the wearable device worn by the user, the server immediately analyzes this data and sends an instruction to the device to lower the temperature by one degree to help the user fall into deep sleep again. The device receives this instruction and automatically adjusts the air conditioning settings, allowing the user to sleep in a comfortable environment again.

[0782] Here is an example prompt:

[0783] "Generate specific action suggestions to help the user enter deep sleep. For example, avoid using your phone before bed or try relaxing breathing exercises."

[0784] As described above, the present invention provides a specific system for improving sleep quality by setting optimal environmental settings in real time based on the user's vital data and indoor environmental data.

[0785] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0786] Step 1:

[0787] Users wear a wearable device that collects vital data such as heart rate, body temperature, and respiratory rate in real time.

[0788] Input: Real-time data of heart rate, body temperature, and respiratory rate

[0789] Output: Vital data sent from the wearable device

[0790] Specific operation: Device data is sent from the device worn on the user's wrist to the server via wireless communication such as Bluetooth.

[0791] Step 2:

[0792] Sensors placed in the room collect indoor environmental data such as temperature, humidity, illuminance, noise, and wind speed and send it to a server.

[0793] Input: Real-time data of temperature, humidity, light, noise, and wind speed

[0794] Output: Indoor environment data sent from the sensor

[0795] Specific operation: The sensor collects data and sends it to a server via Wi-Fi or other wireless communication.

[0796] Step 3:

[0797] The server receives and analyzes vital data from the wearable device and indoor environment data from sensors in the room.

[0798] Input: vital data, indoor environment data

[0799] Output: Recommended environment settings for analysis results

[0800] How it works: The server analyzes the data, estimates the depth of sleep from the user's heart rate and body temperature, and calculates the optimal temperature, humidity, and light settings.

[0801] Step 4:

[0802] The server generates instructions for optimal environment settings based on the analysis results and sends them to the device.

[0803] Input: Analysis results

[0804] Output: Setting instructions for air conditioners, humidifiers, lighting, etc.

[0805] Specific actions: The server generates specific instructions such as setting the air conditioner to 22 degrees, turning on the humidifier, or turning off the lights.

[0806] Step 5:

[0807] The device automatically adjusts the air conditioner, humidifier, lighting, etc. based on the environmental setting instructions received from the server.

[0808] Input: Setting instructions from the server

[0809] Output: Adjusted indoor environment

[0810] Specific actions: The device sets the air conditioner temperature to 22 degrees, turns on the humidifier, and turns off the lights.

[0811] Step 6:

[0812] The server calculates a sleep score from the collected vital data and indoor environmental data and provides feedback for the next environmental settings.

[0813] Input: vital data, indoor environment data

[0814] Output: Sleep score, next setting data

[0815] Specific operation: The server uses an algorithm to calculate a sleep score based on past data and reflects the result in the next environment settings.

[0816] Step 7:

[0817] Users enter their sleep-related questions and concerns into the device, which then sends the information to the server.

[0818] Input: User text input

[0819] Output: The question data sent to the server

[0820] Specific operation: The user enters a question into the device, which then sends it to the server.

[0821] Step 8:

[0822] The server generates action suggestions for the user's questions and concerns and sends them to the device.

[0823] Input: Question data from users, analysis results of the generative AI model

[0824] Output: Specific action suggestions

[0825] Specific operation: The server analyzes the question using the generative AI model and sends suggested actions to the device, such as "don't use your smartphone before bed" or "try breathing exercises to relax."

[0826] Through these steps, the system provides an optimal sleeping environment in real time based on the user's vital data and indoor environmental data, and suggests specific actions to take in response to the user's questions.

[0827] (Application example 1)

[0828] 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."

[0829] Thanks to recent technological advances, autonomous vehicles are becoming more commonplace, but technology for providing a comfortable in-vehicle sleeping environment is still in its infancy, and there is a need for improved comfort during long-distance travel. To provide an optimal sleeping environment, it is necessary to collect and analyze the user's vital signs and the vehicle's internal environmental data in real time, and automatically adjust the environmental settings based on the results. However, such a system has not yet been realized. Therefore, the challenge is to develop a system that provides the optimal in-vehicle environmental settings for users to achieve quality sleep.

[0830] 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.

[0831] In this invention, the server includes means for collecting vital data, means for collecting interior environment data, means for analyzing the collected vital data and interior environment data and generating optimal environment settings, means for sending instructions for applying the generated environment settings, means for receiving the instructions and adjusting the interior environment, means for collecting environmental data inside the vehicle, and means for adjusting the air conditioning, lighting, and seat reclining angle inside the vehicle, thereby making it possible to provide optimal environment settings for the user to sleep comfortably in the vehicle.

[0832] "Vital data" refers to data related to a user's vital activities, such as heart rate, body temperature, and respiratory rate.

[0833] "Indoor environment data" refers to data relating to the indoor environment, such as indoor temperature, humidity, illuminance, noise, and wind speed.

[0834] "Means for analyzing and generating optimal environmental settings" refers to algorithms or programs that automatically generate optimal environmental settings for the user based on collected vital data and indoor environmental data.

[0835] "Means for sending instructions to apply the generated configuration" refers to a communication function for sending instructions to operate the configuration from the server to the appropriate device or equipment.

[0836] "Means for adjusting the indoor environment" refers to control devices and software for adjusting indoor equipment such as air conditioners, humidifiers, and lighting.

[0837] "Interior vehicle environmental data" refers to data related to the in-vehicle environment, such as the temperature, humidity, lighting intensity, and seat reclining angle inside an autonomous vehicle.

[0838] "Means for adjusting the air conditioning, lighting, and seat reclining angle inside the vehicle" refers to the control systems and software that automatically adjust the air conditioning, lighting, and seat position inside the vehicle.

[0839] "Means for inputting prompts to the generative AI model" refers to an interface or program for inputting necessary instructions or questions to the artificial intelligence model.

[0840] "Means for calculating sleep score" refers to algorithms or software that quantify a user's sleep quality based on collected vital data and indoor environmental data.

[0841] This invention is a system that collects and analyzes vital data and internal vehicle environmental data in real time to provide an optimal sleeping environment for users in autonomous vehicles, in order to improve their sleep quality. This system consists of the following main components:

[0842] 1. Vital data collection means: Vital data such as heart rate, body temperature, and respiratory rate are collected in real time from a wearable device (e.g., a smart watch) worn by the user.

[0843] 2. Means for collecting environmental data inside the vehicle: Various sensors such as temperature sensors, humidity sensors, and illuminance sensors are used to collect environmental data such as temperature, humidity, lighting intensity, and noise inside the vehicle.

[0844] 3. Data analysis and optimal environment generation: The server analyzes the collected vital and environmental data and generates optimal environmental settings for the user (for example, setting the temperature to 22°C and the humidity to 40%). At this time, it also calculates a sleep score based on past data and uses this as feedback to reflect in future settings.

[0845] 4. Instruction transmission means: The server transmits instructions to the vehicle's internal control system to apply the generated configuration. This communication is performed using wireless communication technologies such as Wi-Fi or Bluetooth.

[0846] 5. Environmental adjustment means: Automatically adjust the air conditioning, humidifier, lighting, seat reclining angle, etc. inside the vehicle, allowing users to enjoy an optimal sleeping environment inside the autonomous vehicle.

[0847] 6. Means for receiving and analyzing inquiries from users: Questions and concerns about sleep entered by users through their smartphone or vehicle interface are received and analyzed on the server.

[0848] 7. Means for generating and providing action suggestions: The server generates action suggestions based on the analysis results and provides them to the user according to the prompts entered into the generative AI model. For example, it provides specific advice such as "Don't use your smartphone before going to bed" or "Try breathing exercises to relax."

[0849] Specific examples

[0850] Suppose a user wears a smartwatch and reclines their seat to rest in an autonomous vehicle. The system detects that the user's heart rate begins to steadily decrease immediately after lying down. Analyzing this data in real time, the server sends instructions to adjust the interior temperature to 22 degrees. If the humidity is low, it also sends instructions to activate the humidifier. This allows the user to sleep in an optimal environment.

[0851] Example prompt for a generative AI model:

[0852] To ensure a user can sleep comfortably in an autonomous vehicle, calculate the optimal environmental settings based on the following data: Vital data includes heart rate, body temperature, and respiratory rate, while environmental data includes temperature, humidity, light intensity, noise, and wind speed. For example, if the heart rate is stable, the room temperature should be set to 22°C; if the humidity is too low, the humidifier should be turned on; and if the light intensity is high, the lights should be turned off. Output the appropriate environmental settings.

[0853] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0854] Step 1:

[0855] The user wears a smartwatch and sits in a reclining seat in an autonomous vehicle. At this point, the smartwatch measures vital data such as heart rate, body temperature, and respiratory rate in real time. This vital data is then transmitted from the wearable device to a terminal.

[0856] Input: Heart rate, body temperature, respiration rate (from wearable device)

[0857] Output: Real-time vital data

[0858] Step 2:

[0859] Temperature, humidity, and light sensors installed inside the vehicle measure environmental data inside the vehicle, which is then transmitted to the terminal.

[0860] Input: Temperature, humidity, illuminance (from in-car sensors)

[0861] Output: Real-time environmental data

[0862] Step 3:

[0863] The device sends the collected vital and environmental data to a server, which analyzes the data and determines the user's current condition.

[0864] Input: Vital data, environmental data (from terminal)

[0865] Output: Analysis data (user status)

[0866] Step 4:

[0867] Based on the analyzed data, the server inputs prompts into the generative AI model to generate optimal environment settings, taking into account feedback based on previous data.

[0868] Input: Analysis data (user status), prompt statement

[0869] Output: Optimal Environment Settings

[0870] Step 5:

[0871] The server sends the generated configuration to the device as instructions to apply it, which the device receives and sends to the in-car control system.

[0872] Input: Optimal environment settings (from server)

[0873] Output: Environmental adjustment instructions (from terminal to in-car control system)

[0874] Step 6:

[0875] Actuators inside the vehicle for air conditioning, lighting, humidifiers, seat reclining angles, etc. receive instructions from the terminal and automatically adjust to the specified settings.

[0876] Input: Environment adjustment instructions (from terminal)

[0877] Output: Controlled environment (temperature, humidity, light, seat angle)

[0878] Step 7:

[0879] Users input their sleep-related questions and concerns through a smartphone or vehicle interface, and the device then sends this information to a server.

[0880] Input: User's question or concern

[0881] Output: Query data (to server)

[0882] Step 8:

[0883] The server analyzes the user's query and generates appropriate action suggestions, also driven by a generative AI model, such as breathing exercises to help with relaxation.

[0884] Input: Question data (from user), analyzed by generative AI model

[0885] Output: Action suggestions

[0886] Step 9:

[0887] The server then sends the generated action suggestions to the device and provides them to the user, who can then take action to further improve their sleep quality.

[0888] Input: Action proposal (from server)

[0889] Output: Feedback to the user

[0890] 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.

[0891] This invention combines a system that collects and analyzes the user's vital data and indoor environmental data in real time to provide the optimal sleeping environment with an emotion engine that recognizes and analyzes the user's emotions. This system makes it possible to set environmental settings and suggest actions that take into account not only the user's physiological data but also their emotional state.

[0892] First, before the user goes to bed, the system collects the data necessary to create a comfortable indoor environment for that person. Vital data (heart rate, body temperature, respiratory rate, etc.) is received in real time from the wearable device worn by the user and uploaded to the server. In addition, indoor environmental data is collected from sensors placed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor).

[0893] The server analyzes this data and generates optimal environmental settings. For example, if the heart rate is stable, it determines that the user is likely to fall into deep sleep and sets the room temperature to 22 degrees. If the humidity is too low, it turns on the humidifier, and if the brightness is too high, it turns off the lights.

[0894] The system then takes the user's emotions into account. The emotion engine analyzes the user's voice and facial expressions to determine their emotions, and sends the results to the server. For example, if the system determines that the user is feeling stressed, it will generate relaxing environmental settings (such as lowering the temperature slightly or enabling silent mode).

[0895] The server then sends instructions to the device to apply these environmental settings, and the device automatically adjusts the room's air conditioner, humidifier, lighting, etc. based on the received instructions, allowing the user to sleep in a comfortable environment.

[0896] Furthermore, the server calculates a sleep score from the collected vital data and indoor environmental data, and uses the results as feedback to reflect in future environmental settings. For example, by fine-tuning the next settings based on the optimal environmental settings obtained from past data, a more accurate environment can be provided.

[0897] When a user inputs questions or concerns about sleep, the device sends the information to a server, which analyzes the data and generates specific recommendations for action, such as "don't use your smartphone before bed" or "try breathing exercises to relax," which are then provided to the user via the device.

[0898] As a concrete example, consider the case where a user wakes up in the middle of the night. Because the user is wearing a wearable device, the server immediately detects an increase in heart rate, and the emotion engine detects the user's anxiety. The server analyzes this data and generates and sends to the device an instruction to lower the temperature by one degree to help the user fall back into deep sleep. The device receives this instruction and automatically adjusts the air conditioning settings, allowing the user to return to sleep in a comfortable environment.

[0899] As described above, the present invention provides an integrated system that improves sleep quality by analyzing the user's emotional state using an emotion engine in addition to the user's vital data and indoor environmental data, and by optimally configuring the environment in real time.

[0900] The processing flow will be explained below.

[0901] Step 1:

[0902] The server receives vital data (heart rate, body temperature, respiratory rate, etc.) from the user's wearable device every 10 minutes, using data communication via Bluetooth or Wi-Fi.

[0903] Step 2:

[0904] The server receives indoor environment data every minute from sensors installed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor). The data from each sensor is acquired via API and stored in a database.

[0905] Step 3:

[0906] The server analyzes the collected vital data and indoor environmental data to evaluate the user's current condition. For example, if the user's heart rate is high, it may determine that the user is feeling stressed.

[0907] Step 4:

[0908] The emotion engine analyzes the user's emotions from their voice and facial expressions and sends the results to the server. For example, it can detect stress or anxiety from the user's tone of voice and choice of words.

[0909] Step 5:

[0910] The server integrates vital data, indoor environment data, and emotional data to generate optimal environmental settings (e.g., temperature 22°C, humidity 45%, illuminance 0 lumens), allowing users to achieve the optimal sleeping environment.

[0911] Step 6:

[0912] The server sends instructions to the terminal to apply the generated environment settings. For example, it sends instructions to set the air conditioner temperature to 22 degrees and the humidifier humidity to 45%.

[0913] Step 7:

[0914] The device adjusts indoor devices such as air conditioners, humidifiers, and lighting based on the received instructions, for example, changing the temperature setting of the air conditioner and turning off the lights.

[0915] Step 8:

[0916] The server calculates a sleep score from the collected vital data and indoor environmental data, which is compared with past data to evaluate sleep quality.

[0917] Step 9:

[0918] The server will provide feedback to the next environment settings based on the sleep score, which will result in more accurate settings from the next time onwards, further optimizing the user's sleep environment.

[0919] Step 10:

[0920] When users input their sleep-related questions or concerns by voice or text, the device receives them and sends them to the server. For example, "I can't fall asleep easily" or "I'm not sleeping well."

[0921] Step 11:

[0922] The server analyzes the received questions and concerns and generates specific advice and action suggestions, such as "don't use electronic devices before bed" or "try breathing exercises to relax."

[0923] Step 12:

[0924] The device notifies the user of any advice or suggested actions received from the server, which are played aloud through the voice assistant or displayed as text through the app's chat function.

[0925] As a concrete example, consider the case where a user wakes up in the middle of the night. Because the user is wearing a wearable device, the server immediately detects an increase in heart rate, and the emotion engine detects the user's anxiety. The server analyzes this data and generates and sends to the device an instruction to lower the temperature by one degree to help the user fall back into deep sleep. The device receives this instruction and automatically adjusts the air conditioning settings, allowing the user to return to sleep in a comfortable environment.

[0926] These are the specific processing steps of this system. At each step, the server, device, and user each play their necessary roles and work together to optimize the sleep environment.

[0927] Example 2

[0928] 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."

[0929] Conventional sleep environment improvement systems collect users' vital data and indoor environmental data to provide an optimal environment, but do not take their emotional state into account when making adjustments, which means they are unable to fully improve the quality of their sleep.In addition, they lack the functionality to properly analyze users' emotional state and sleep-related questions and concerns, and to suggest actions, which means they are unable to achieve comprehensive sleep improvement.

[0930] 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.

[0931] In this invention, the server includes means for collecting vital data, means for collecting indoor environmental data, means for analyzing the collected vital data and indoor environmental data and generating optimal environmental settings, means for sending instructions for applying the generated environmental settings, means for receiving the instructions and adjusting the indoor environment, means for collecting user voice and facial expression data and analyzing emotions, and means for fine-tuning the environmental settings based on the analyzed emotional data. This enables real-time environmental settings that take the user's emotional state into consideration, thereby improving overall sleep quality.

[0932] "Vital data" refers to data that indicates the physiological state of the user, such as heart rate, body temperature, and respiratory rate.

[0933] "Indoor environment data" refers to data that indicates the environmental conditions of the room where the user is located, such as temperature, humidity, illuminance, noise, and wind speed.

[0934] "Analysis" is the process of evaluating the current situation based on collected data and deriving optimal actions and settings.

[0935] "Environmental settings" are instructions for adjusting the operating conditions of home appliances such as air conditioners, humidifiers, and lighting to achieve a comfortable environment for the user.

[0936] "Means for sending instructions" refers to the processes and techniques for transmitting the environmental settings generated by the server to terminals such as air conditioners, humidifiers, and lights.

[0937] "Emotional data" is data that indicates the user's emotions, obtained by analyzing the user's voice and facial expressions.

[0938] The "emotion engine" is a system that uses deep learning and machine learning algorithms to analyze emotions based on the user's voice and facial expression data.

[0939] The "sleep score" is an index used to evaluate a user's sleep quality based on collected vital data and indoor environmental data.

[0940] "Action suggestions" provide specific advice and recommended actions to help users get a good night's sleep.

[0941] "Feedback" is the process of using past data and results to provide information to further optimize the next environment settings.

[0942] This invention combines a system that collects and analyzes the user's vital data and indoor environmental data in real time to provide the optimal sleeping environment with an emotion engine that recognizes and analyzes the user's emotions. This system makes it possible to set environmental settings and suggest actions that take into account not only the user's physiological data but also their emotional state.

[0943] First, before the user goes to bed, the system collects the data necessary to create a comfortable indoor environment for that person. Vital data (heart rate, body temperature, respiratory rate, etc.) is received in real time from the wearable device worn by the user and sent to the terminal. The terminal collects this data and also collects indoor environment data from sensors placed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor). This collected data is uploaded to a server via the Internet.

[0944] The server analyzes the received vital data and indoor environmental data using machine learning algorithms. For example, if the heart rate is stable, it determines that the user is likely to fall into deep sleep and sets the room temperature to 22 degrees. It also adjusts the environment by turning on the humidifier if the humidity is too low, or by turning off the lights if the brightness is too high.

[0945] Next, the server also collects the user's emotional data. The user's voice and facial expression data are collected via the device and analyzed by the emotion engine. This emotion engine uses a deep learning model to recognize the user's emotions (happiness, sadness, stress, etc.). The analysis results are sent to the server, and the environment settings are fine-tuned based on the emotional data. For example, if it is determined that the user is feeling stressed, relaxing environment settings (such as slightly lowering the temperature or enabling silent mode) will be generated.

[0946] The generated environmental settings are sent from the server to the device, which then automatically adjusts the room's air conditioner, humidifier, lighting, etc. based on the received instructions, allowing the user to sleep in a comfortable environment.

[0947] The server then calculates a sleep score from the collected vital data and indoor environmental data. The sleep score is compared with past data and reflected in the next environment settings. This feedback allows for more accurate environment settings.

[0948] When users input their sleep-related questions or concerns into the device, the information is sent to a server. The server then uses a generative AI model to analyze the input information, generate specific action suggestions, and provide them to the user via the device. For example, specific advice such as "don't use your smartphone before bed" or "try breathing exercises to relax" is provided.

[0949] For example, if a user wakes up in the middle of the night, the server will immediately detect an increase in heart rate because the user is wearing a wearable device, and the emotion engine will detect the user's anxiety. The server will analyze this data and generate an instruction to lower the temperature by one degree to help the user fall back into deep sleep, which will be sent to the device. The device will then receive this instruction and automatically adjust the air conditioning settings, allowing the user to return to sleep in a comfortable environment.

[0950] Examples of prompts include:

[0951] "I've been waking up a lot lately in the middle of the night. How can I get into a deep sleep?"

[0952] As described above, the present invention provides an integrated system that analyzes the user's emotional state using an emotion engine in addition to the user's vital data and indoor environmental data, thereby optimizing the environment settings in real time and improving sleep quality.

[0953] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0954] Step 1:

[0955] The user puts on the wearable device before going to bed, and the device measures vital data such as heart rate, body temperature, and respiratory rate in real time and transmits them to a device via Bluetooth.

[0956] Input: Vital data from the user

[0957] Output: Vital data sent to the device

[0958] Specific operation: Heart rate and body temperature data are measured at regular intervals and sent to the device via Bluetooth communication.

[0959] Step 2:

[0960] The device collects indoor environment data from sensors placed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor), and this data is streamed to the device.

[0961] Input: Environmental data from indoor sensors

[0962] Output: Environmental data aggregated on the device

[0963] Specific operation: Environmental data is acquired from each sensor at regular intervals and stored in the device's data storage.

[0964] Step 3:

[0965] The device uploads the collected vital data and indoor environmental data to a server via the Internet. This data is transmitted encrypted.

[0966] Input: Vital and environmental data stored on the device

[0967] Output: Data uploaded to the server

[0968] Specific operation: The data is packaged in a batch process and sent to the server using the SSL / TLS protocol.

[0969] Step 4:

[0970] The server analyzes the received vital data and indoor environmental data using machine learning algorithms, for example, to estimate the user's sleep depth from heart rate fluctuations.

[0971] Input: Uploaded vital and environmental data

[0972] Output: Optimal Environment Settings

[0973] Specific operation: Based on the collected data, a machine learning model is used to calculate environmental adjustment values ​​(temperature, humidity, etc.).

[0974] Step 5:

[0975] The device collects the user's voice and facial expression data. Voice data is acquired through a microphone, and facial expression data is acquired through a camera. These data are pre-processed on the device and sent to the emotion engine.

[0976] Input: Voice data and facial expression data from the user

[0977] Output: Preprocessed speech and facial expression data

[0978] Specific operations: Performs preprocessing such as removing noise from the audio and extracting facial features, preparing it for transmission to the emotion engine.

[0979] Step 6:

[0980] The server analyzes the user's emotions using an emotion engine, classifies emotions (such as joy, sadness, stress) using a deep learning model, and sends the results to the server.

[0981] Input: Preprocessed speech and facial expression data

[0982] Output: Parsed emotion data

[0983] What it does: Runs a deep learning emotion classification model to identify emotional states.

[0984] Step 7:

[0985] The server then uses the emotional data to fine-tune environmental settings, for example, lowering the room temperature slightly or enabling quiet mode if the user is feeling stressed.

[0986] Input: Parsed emotion data

[0987] Output: Tweaked preferences

[0988] Specific behavior: Further adjust existing environment settings taking into account sentiment analysis results.

[0989] Step 8:

[0990] The server sends the generated environmental settings to the device, which then automatically controls the air conditioner, humidifier, lighting, etc. based on the received instructions.

[0991] Input: Generated environment settings

[0992] Output: Operation instructions for home appliances via terminal

[0993] Specific operation: Sends control signals to each home appliance and changes its settings.

[0994] Step 9:

[0995] The server calculates the user's sleep score based on vital and environmental data collected throughout the night, and this score is fed back as the basis for the next environmental setting.

[0996] Input: overnight vital and environmental data

[0997] Output: Sleep score and feedback for next settings

[0998] Specific operation: Analyzes data, evaluates sleep quality, and calculates feedback values ​​to be reflected in the next settings.

[0999] Step 10:

[1000] When users input their sleep-related questions or concerns, the information is sent from the device to a server, which then uses a generative AI model to analyze the input information and generate and provide specific action suggestions.

[1001] Input: Questions and concerns from users

[1002] Output: Specific action suggestions

[1003] Specific operation: The generative AI model analyzes the prompt sentence, generates appropriate advice, and provides it to the user.

[1004] Example prompt sentence:

[1005] "I've been waking up a lot lately in the middle of the night. How can I get into a deep sleep?"

[1006] (Application example 2)

[1007] 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."

[1008] There is a need to provide a comfortable environment for customers in physical stores, but conventional systems have not been able to set the environment taking into account the customer's vital data or emotional state.Furthermore, they have not incorporated feedback on the environment settings or action suggestions based on emotional data analysis, which has limited the comfort they can provide.

[1009] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting vital data, means for collecting indoor environmental data, means for analyzing the collected vital data and indoor environmental data and generating optimal environmental settings, means for sending instructions for applying the generated environmental settings, means for receiving the instructions and adjusting the indoor environment, means for analyzing emotional data, means for generating environmental settings based on the customer's emotional state, and means for applying the generated environmental settings to lighting, sound, temperature, and humidity settings in the store. This makes it possible to perform optimal environmental settings in real time based on the customer's vital data and emotional data. Furthermore, by providing feedback to the next environmental settings based on the comfort score, it is possible to continuously provide an optimal comfortable environment and further increase customer satisfaction by suggesting actions based on customer inquiries.

[1010] "Vital data" refers to physiological data such as a customer's heart rate, body temperature, and respiratory rate.

[1011] "Indoor environment data" refers to data related to indoor environmental parameters such as temperature, humidity, lighting, noise, and wind speed.

[1012] "Environmental settings" refers to various settings to provide an optimal indoor environment based on collected and analyzed vital data and indoor environmental data.

[1013] "Emotional data" refers to data about a customer's emotional state (e.g., stress, relaxation, etc.) analyzed from voice and facial expressions.

[1014] "Emotional state" refers to the emotions and psychological state that a customer is currently experiencing.

[1015] "Lighting" refers to artificial lighting used to adjust the brightness of a room.

[1016] "Acoustics" refers to the settings for music and other sounds played in the room.

[1017] "Temperature and humidity settings" refers to settings for adjusting the temperature and humidity in the room.

[1018] "Comfort score" refers to a numerical value that indicates the comfort felt by a customer, calculated based on vital data and indoor environmental data.

[1019] "Action suggestions" refer to specific action suggestions provided to customers based on data analysis.

[1020] An embodiment of the present invention is a system for providing a comfortable environment for customers in a physical store. This system is implemented in the following manner.

[1021] First, vital data (heart rate, body temperature, respiratory rate, etc.) is collected from the wearable device worn by the customer. This data is uploaded to a server in real time. At the same time, indoor environmental data is collected from sensors placed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor).

[1022] The server analyzes the collected vital data and indoor environmental data to generate optimal environmental settings using data analysis tools such as Python or R. For example, if the heart rate is high, it determines that the customer is feeling stressed and generates instructions to lower the room temperature. It also activates the humidifier if the humidity is too low, and turns off the lights if the brightness is too high.

[1023] In addition, the server uses an emotion engine to take into account the customer's emotions. The emotion engine analyzes emotions from the customer's voice and facial expressions and sends the results to the server. For example, if it determines that the customer is feeling stressed, it generates relaxing environmental settings (such as lowering the temperature slightly or enabling quiet mode). The emotion engine uses voice analysis software (such as Google Speech-to-Text API) and facial expression analysis software (such as OpenFace).

[1024] Next, the server sends instructions to the device to apply these environmental settings. The device automatically adjusts the room's air conditioner, humidifier, lighting, etc. based on the received instructions. This is done using an IoT device management platform (such as AWS IoT Core). This allows customers to spend their time in a comfortable environment.

[1025] Furthermore, the server calculates a comfort score from the collected vital data and indoor environmental data, and uses the result as feedback to reflect in future environmental settings. For example, by fine-tuning the next settings based on the optimal environmental settings obtained from past data, a more accurate environment can be provided.

[1026] When a customer inputs a question or concern about comfort, the device sends the information to the server, which analyzes it and generates specific action suggestions, such as "take breaks at regular intervals" or "try stretching to relax," which are provided to the customer via the device.

[1027] Examples and prompts

[1028] As a specific application example, the following prompt sentence can be used to analyze the customer's condition using an AI model and set the optimal environment.

[1029] Specific examples

[1030] Suppose a customer wearing a wearable device enters a store. The customer says, "It's a little hot in this store," and their face looks tired. In this case, the system analyzes the customer's voice and facial expression and sets the following environment settings:

[1031] Set the temperature to 22 degrees.

[1032] Adjust the lighting to be slightly darker.

[1033] Play relaxation music.

[1034] Prompt statement

[1035] "Analyze the customer's emotional state from their voice and facial expressions, and then set the optimal indoor environment based on the results. For example, if the customer says they're hot, lower the temperature. If they look tired, dim the lights and play relaxing music."

[1036] This allows us to build a system that maximizes customer comfort in brick-and-mortar stores. By analyzing customers' vital signs and emotional data in real time and setting the optimal environment, we can continuously provide a high level of comfort.

[1037] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1038] Step 1:

[1039] Vital data (heart rate, body temperature, respiratory rate, etc.) is collected from the wearable device worn by the user. This data is uploaded to a server in real time. The input is the vital data obtained from the wearable device, and this data is temporarily stored as an output that is accumulated on the server.

[1040] Step 2:

[1041] Indoor environment data is collected from sensors placed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor). These data are sent to the server and serve as input to understand the current state of environmental parameters. As an output, the collected indoor environment data is stored on the server.

[1042] Step 3:

[1043] The server analyzes the collected vital data and indoor environment data to generate optimal environmental settings. The analysis is performed using data analysis tools (such as Python or R). The input is the data collected in steps 1 and 2, and the output is the environmental settings generated based on this data. For example, if the heart rate is high, the output will be to lower the room temperature.

[1044] Step 4:

[1045] The server uses an emotion engine to analyze the user's emotional data. It identifies the user's emotional state by analyzing their voice and facial expressions. The input is emotion data obtained from voice analysis software (such as Google Speech-to-Text API) and facial expression analysis software (such as OpenFace). The output is the analyzed emotional state, such as whether the user is stressed or relaxed.

[1046] Step 5:

[1047] The server comprehensively analyzes vital data, indoor environment data, and emotional data to regenerate optimal environmental settings. The input is the data obtained at each step, and the output is the optimal environmental settings determined comprehensively. For example, the output may set the room temperature to 22 degrees, dim the lights, and play relaxation music.

[1048] Step 6:

[1049] The server sends instructions to the device to apply the generated configuration. The input is the configuration generated in step 5, and the output is data that indicates the configuration. This is done using an IoT device management platform (such as AWS IoT Core).

[1050] Step 7:

[1051] The device automatically adjusts the room's air conditioner, humidifier, lighting, etc. based on instructions received from the server. The input is the environmental setting instructions received from the server, and the output is the actual adjustment of the physical device. This operation is performed using the connected IoT device.

[1052] Step 8:

[1053] The server receives inquiries from users and analyzes their content. Natural language processing (NLP) technology is used for the analysis. The input is data related to the user's questions and concerns, and the output is the analysis results. For example, specific action suggestions, such as how to take breaks, are generated.

[1054] Step 9:

[1055] The server calculates a comfort score from the collected vital data and indoor environmental data, and reflects the results in future environmental settings. The input is the data collected up to that point, and the output is feedback information that will be used in the next environmental settings. For example, optimal room temperature and humidity settings can be fine-tuned based on past data.

[1056] Step 10:

[1057] The server provides the user with action suggestions. The input is the action suggestion data analyzed in step 8, and the output is the specific suggestion to be conveyed to the user. For example, advice such as "take a break at regular intervals" or "try stretching to relax" is provided.

[1058] In this way, the system of the present invention can implement a series of processes to maximize customer comfort.

[1059] 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.

[1060] 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.

[1061] 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.

[1062] [Fourth embodiment]

[1063] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1064] 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.

[1065] 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).

[1066] 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.

[1067] 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.

[1068] 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).

[1069] 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.

[1070] 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.

[1071] 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.

[1072] 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.

[1073] 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.

[1074] 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.

[1075] 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."

[1076] This invention relates to a system that collects and analyzes vital data and indoor environmental data in real time to provide an optimal sleeping environment for users in order to improve their sleep quality. This system sets optimal environmental settings based on the user's vital data and indoor environmental data, and can also provide specific action suggestions in response to the user's questions and concerns.

[1077] First, before the user goes to bed, the system collects the data necessary to create a comfortable indoor environment for that person. Vital data (heart rate, body temperature, respiratory rate, etc.) is received in real time from the wearable device worn by the user and uploaded to the server. In addition, indoor environmental data is collected from sensors placed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor).

[1078] The server analyzes this data and generates optimal environmental settings. For example, if the heart rate is stable, it determines that the user is likely to fall into deep sleep and sets the room temperature to 22 degrees. If the humidity is too low, it turns on the humidifier, and if the brightness is too high, it turns off the lights.

[1079] The server then sends instructions to the device to apply these environmental settings, and the device automatically adjusts the room's air conditioner, humidifier, lighting, etc. based on the received instructions, allowing the user to sleep in a comfortable environment.

[1080] Furthermore, the server calculates a sleep score from the collected vital data and indoor environmental data, and uses the results as feedback to reflect in future environmental settings. For example, by fine-tuning the next settings based on the optimal environmental settings obtained from past data, a more accurate environment can be provided.

[1081] When a user inputs questions or concerns about sleep, the device sends the information to a server, which analyzes the data and generates specific recommendations for action, such as "don't use your smartphone before bed" or "try breathing exercises to relax," which are then provided to the user via the device.

[1082] As a concrete example, consider the case where a user wakes up in the middle of the night. Because the user is wearing a wearable device, the server immediately detects an increase in heart rate. The server analyzes this data and generates and sends to the device an instruction to lower the temperature by one degree to help the user fall back into deep sleep. The device receives this instruction and automatically adjusts the air conditioning settings, allowing the user to return to sleep in a comfortable environment.

[1083] As described above, the present invention provides a specific system for improving sleep quality by setting optimal environmental settings in real time based on the user's vital data and indoor environmental data.

[1084] The processing flow will be explained below.

[1085] Step 1:

[1086] The server receives vital data (heart rate, body temperature, respiratory rate, etc.) from the user's wearable device every 10 minutes, using data communication via Bluetooth or Wi-Fi.

[1087] Step 2:

[1088] The server receives indoor environment data every minute from sensors installed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor). The data from each sensor is acquired via API and stored in a database.

[1089] Step 3:

[1090] The server analyzes the collected vital data and indoor environmental data to evaluate the current state of the user. For example, if the heart rate is high, it may indicate that the user is feeling stressed and it may be necessary to lower the temperature.

[1091] Step 4:

[1092] The server generates optimal environmental settings (e.g., temperature 22°C, humidity 45%, illuminance 0 lumens) based on the analysis results, allowing users to obtain the optimal sleeping environment.

[1093] Step 5:

[1094] The server sends instructions to the device to apply the generated environment settings, such as setting the air conditioner temperature to 22 degrees and the humidifier humidity to 45%.

[1095] Step 6:

[1096] Based on the received instructions, the device adjusts indoor appliances such as air conditioners, humidifiers, and lighting. It analyzes the received data and changes the settings of each appliance accordingly.

[1097] Step 7:

[1098] The server calculates a sleep score from the collected vital data and indoor environmental data, which is compared with past data to evaluate sleep quality.

[1099] Step 8:

[1100] The server will provide feedback to the next environment settings based on the sleep score, which will result in more accurate settings from the next time onwards, further optimizing the user's sleep environment.

[1101] Step 9:

[1102] When a user enters a question or concern about sleep via voice or text, the device receives it and sends it to the server. For example, "I can't fall asleep easily."

[1103] Step 10:

[1104] The server analyzes the received questions and concerns and generates specific advice and action suggestions, such as "Avoid using your smartphone before going to bed."

[1105] Step 11:

[1106] The device notifies the user of any advice or suggested actions received from the server, which are played aloud through the voice assistant or displayed as text through the app's chat function.

[1107] These are the specific processing steps of this system. At each step, the server, device, and user each play their necessary roles and work together to optimize the sleep environment.

[1108] Example 1

[1109] 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."

[1110] In modern living environments, many people find it difficult to get a good night's sleep, which negatively impacts their health and quality of life. Conventional technologies lack systems that can collect and analyze users' vital signs and indoor environmental data in real time and provide optimal sleeping environments based on that data. Furthermore, they lack a means to provide appropriate action suggestions in response to sleep-related questions and problems, making it difficult for users to efficiently create a comfortable sleeping environment.

[1111] 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.

[1112] In this invention, the server includes means for collecting vital data from the wearable device, means for collecting indoor environmental data from a detector placed in the room, means for analyzing the collected vital data and indoor environmental data and generating optimal environmental settings, means for transmitting instructions for applying the generated environmental settings, and means for receiving the instructions and automatically adjusting the indoor environment. This enables comprehensive analysis of the user's vital data and indoor environmental data and automatic configuration of the optimal environmental settings in real time. It is also possible to receive inquiries from the user and provide specific action suggestions based on the analysis results, thereby improving the user's sleep quality.

[1113] A "wearable device" is an electronic device that can collect vital data such as heart rate, body temperature, and respiratory rate in real time by being worn by the user.

[1114] "Detector" refers to a sensor installed to collect environmental data such as indoor temperature, humidity, light, noise, and wind speed.

[1115] "Vital data" refers to biometric information related to the user's health status, such as heart rate, body temperature, and respiratory rate.

[1116] "Indoor environment data" refers to data necessary to create a comfortable sleeping environment for the user, such as the temperature, humidity, illuminance, noise, and wind speed in the room.

[1117] "Analysis" refers to the process of comprehensively evaluating the collected vital data and indoor environmental data to derive optimal environmental settings.

[1118] "Environmental settings" refers to adjusting settings such as air conditioning, humidifiers, and lighting to help users sleep comfortably.

[1119] An "instruction" is a specific operation command for transmitting the environmental settings generated by the server to the terminal.

[1120] The "sleep evaluation value" is a numerical value that represents the quality of the user's sleep, calculated from the collected vital data and indoor environment data.

[1121] "Action suggestions" are suggestions based on the analysis results for specific actions that users can take to get a good night's sleep.

[1122] The present invention provides a system for improving the quality of a user's sleep by collecting and analyzing data in real time using a wearable device and sensors placed in a room, and providing an optimal sleeping environment. Specific embodiments are described below.

[1123] First, the user puts on the wearable device before going to bed. The device collects real-time vital data such as the user's heart rate, body temperature, and respiratory rate and uploads it to a server. Additionally, temperature, humidity, light, noise, and wind speed sensors placed in the room collect indoor environmental data. All of this data is sent to the server.

[1124] The server analyzes the received vital data and indoor environmental data. For example, if the user's heart rate is stable, it determines that the user is likely to fall into deep sleep and sets the room temperature to 22 degrees. It also generates instructions to turn on the humidifier if the humidity is too low, or to turn off the lights if the brightness is too high.

[1125] The server then sends instructions to the device to apply the generated environmental settings, and the device automatically adjusts the room's appliances, such as the air conditioner, humidifier, and lighting, based on the received instructions, allowing the user to sleep in a comfortable environment.

[1126] The server then calculates a sleep score from the collected vital data and indoor environmental data. This sleep score is reflected as feedback in future environment settings. Specifically, by fine-tuning the environment settings based on past data, a more accurate environment can be provided.

[1127] When a user inputs a question or concern about sleep, the device sends the information to the server. The server then analyzes the information using a generative AI model and generates specific action suggestions based on the analysis results. For example, specific advice such as "don't use your smartphone before bed" or "try breathing exercises to relax" can be generated and provided to the user via the device.

[1128] As a concrete example, consider the case where a user wakes up in the middle of the night. When an increase in heart rate is detected from the wearable device worn by the user, the server immediately analyzes this data and sends an instruction to the device to lower the temperature by one degree to help the user fall into deep sleep again. The device receives this instruction and automatically adjusts the air conditioning settings, allowing the user to sleep in a comfortable environment again.

[1129] Here is an example prompt:

[1130] "Generate specific action suggestions to help the user enter deep sleep. For example, avoid using your phone before bed or try relaxing breathing exercises."

[1131] As described above, the present invention provides a specific system for improving sleep quality by setting optimal environmental settings in real time based on the user's vital data and indoor environmental data.

[1132] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1133] Step 1:

[1134] Users wear a wearable device that collects vital data such as heart rate, body temperature, and respiratory rate in real time.

[1135] Input: Real-time data of heart rate, body temperature, and respiratory rate

[1136] Output: Vital data sent from the wearable device

[1137] Specific operation: Device data is sent from the device worn on the user's wrist to the server via wireless communication such as Bluetooth.

[1138] Step 2:

[1139] Sensors placed in the room collect indoor environmental data such as temperature, humidity, illuminance, noise, and wind speed and send it to a server.

[1140] Input: Real-time data of temperature, humidity, light, noise, and wind speed

[1141] Output: Indoor environment data sent from the sensor

[1142] Specific operation: The sensor collects data and sends it to a server via Wi-Fi or other wireless communication.

[1143] Step 3:

[1144] The server receives and analyzes vital data from the wearable device and indoor environment data from sensors in the room.

[1145] Input: vital data, indoor environment data

[1146] Output: Recommended environment settings for analysis results

[1147] How it works: The server analyzes the data, estimates the depth of sleep from the user's heart rate and body temperature, and calculates the optimal temperature, humidity, and light settings.

[1148] Step 4:

[1149] The server generates instructions for optimal environment settings based on the analysis results and sends them to the device.

[1150] Input: Analysis results

[1151] Output: Setting instructions for air conditioners, humidifiers, lighting, etc.

[1152] Specific actions: The server generates specific instructions such as setting the air conditioner to 22 degrees, turning on the humidifier, or turning off the lights.

[1153] Step 5:

[1154] The device automatically adjusts the air conditioner, humidifier, lighting, etc. based on the environmental setting instructions received from the server.

[1155] Input: Setting instructions from the server

[1156] Output: Adjusted indoor environment

[1157] Specific actions: The device sets the air conditioner temperature to 22 degrees, turns on the humidifier, and turns off the lights.

[1158] Step 6:

[1159] The server calculates a sleep score from the collected vital data and indoor environmental data and provides feedback for the next environmental settings.

[1160] Input: vital data, indoor environment data

[1161] Output: Sleep score, next setting data

[1162] Specific operation: The server uses an algorithm to calculate a sleep score based on past data and reflects the result in the next environment settings.

[1163] Step 7:

[1164] Users enter their sleep-related questions and concerns into the device, which then sends the information to the server.

[1165] Input: User text input

[1166] Output: The question data sent to the server

[1167] Specific operation: The user enters a question into the device, which then sends it to the server.

[1168] Step 8:

[1169] The server generates action suggestions for the user's questions and concerns and sends them to the device.

[1170] Input: Question data from users, analysis results of the generative AI model

[1171] Output: Specific action suggestions

[1172] Specific operation: The server analyzes the question using the generative AI model and sends suggested actions to the device, such as "don't use your smartphone before bed" or "try breathing exercises to relax."

[1173] Through these steps, the system provides an optimal sleeping environment in real time based on the user's vital data and indoor environmental data, and suggests specific actions to take in response to the user's questions.

[1174] (Application example 1)

[1175] 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."

[1176] Thanks to recent technological advances, autonomous vehicles are becoming more commonplace, but technology for providing a comfortable in-vehicle sleeping environment is still in its infancy, and there is a need for improved comfort during long-distance travel. To provide an optimal sleeping environment, it is necessary to collect and analyze the user's vital signs and the vehicle's internal environmental data in real time, and automatically adjust the environmental settings based on the results. However, such a system has not yet been realized. Therefore, the challenge is to develop a system that provides the optimal in-vehicle environmental settings for users to achieve quality sleep.

[1177] 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.

[1178] In this invention, the server includes means for collecting vital data, means for collecting interior environment data, means for analyzing the collected vital data and interior environment data and generating optimal environment settings, means for sending instructions for applying the generated environment settings, means for receiving the instructions and adjusting the interior environment, means for collecting environmental data inside the vehicle, and means for adjusting the air conditioning, lighting, and seat reclining angle inside the vehicle, thereby making it possible to provide optimal environment settings for the user to sleep comfortably in the vehicle.

[1179] "Vital data" refers to data related to a user's vital activities, such as heart rate, body temperature, and respiratory rate.

[1180] "Indoor environment data" refers to data relating to the indoor environment, such as indoor temperature, humidity, illuminance, noise, and wind speed.

[1181] "Means for analyzing and generating optimal environmental settings" refers to algorithms or programs that automatically generate optimal environmental settings for the user based on collected vital data and indoor environmental data.

[1182] "Means for sending instructions to apply the generated configuration" refers to a communication function for sending instructions to operate the configuration from the server to the appropriate device or equipment.

[1183] "Means for adjusting the indoor environment" refers to control devices and software for adjusting indoor equipment such as air conditioners, humidifiers, and lighting.

[1184] "Interior vehicle environmental data" refers to data related to the in-vehicle environment, such as the temperature, humidity, lighting intensity, and seat reclining angle inside an autonomous vehicle.

[1185] "Means for adjusting the air conditioning, lighting, and seat reclining angle inside the vehicle" refers to the control systems and software that automatically adjust the air conditioning, lighting, and seat position inside the vehicle.

[1186] "Means for inputting prompts to the generative AI model" refers to an interface or program for inputting necessary instructions or questions to the artificial intelligence model.

[1187] "Means for calculating sleep score" refers to algorithms or software that quantify a user's sleep quality based on collected vital data and indoor environmental data.

[1188] This invention is a system that collects and analyzes vital data and internal vehicle environmental data in real time to provide an optimal sleeping environment for users in autonomous vehicles, in order to improve their sleep quality. This system consists of the following main components:

[1189] 1. Vital data collection means: Vital data such as heart rate, body temperature, and respiratory rate are collected in real time from a wearable device (e.g., a smart watch) worn by the user.

[1190] 2. Means for collecting environmental data inside the vehicle: Various sensors such as temperature sensors, humidity sensors, and illuminance sensors are used to collect environmental data such as temperature, humidity, lighting intensity, and noise inside the vehicle.

[1191] 3. Data analysis and optimal environment generation: The server analyzes the collected vital and environmental data and generates optimal environmental settings for the user (for example, setting the temperature to 22°C and the humidity to 40%). At this time, it also calculates a sleep score based on past data and uses this as feedback to reflect in future settings.

[1192] 4. Instruction transmission means: The server transmits instructions to the vehicle's internal control system to apply the generated configuration. This communication is performed using wireless communication technologies such as Wi-Fi or Bluetooth.

[1193] 5. Environmental adjustment means: Automatically adjust the air conditioning, humidifier, lighting, seat reclining angle, etc. inside the vehicle, allowing users to enjoy an optimal sleeping environment inside the autonomous vehicle.

[1194] 6. Means for receiving and analyzing inquiries from users: Questions and concerns about sleep entered by users through their smartphone or vehicle interface are received and analyzed on the server.

[1195] 7. Means for generating and providing action suggestions: The server generates action suggestions based on the analysis results and provides them to the user according to the prompts entered into the generative AI model. For example, it provides specific advice such as "Don't use your smartphone before going to bed" or "Try breathing exercises to relax."

[1196] Specific examples

[1197] Suppose a user wears a smartwatch and reclines their seat to rest in an autonomous vehicle. The system detects that the user's heart rate begins to steadily decrease immediately after lying down. Analyzing this data in real time, the server sends instructions to adjust the interior temperature to 22 degrees. If the humidity is low, it also sends instructions to activate the humidifier. This allows the user to sleep in an optimal environment.

[1198] Example prompt for a generative AI model:

[1199] To ensure a user can sleep comfortably in an autonomous vehicle, calculate the optimal environmental settings based on the following data: Vital data includes heart rate, body temperature, and respiratory rate, while environmental data includes temperature, humidity, light intensity, noise, and wind speed. For example, if the heart rate is stable, the room temperature should be set to 22°C; if the humidity is too low, the humidifier should be turned on; and if the light intensity is high, the lights should be turned off. Output the appropriate environmental settings.

[1200] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1201] Step 1:

[1202] The user wears a smartwatch and sits in a reclining seat in an autonomous vehicle. At this point, the smartwatch measures vital data such as heart rate, body temperature, and respiratory rate in real time. This vital data is then transmitted from the wearable device to a terminal.

[1203] Input: Heart rate, body temperature, respiration rate (from wearable device)

[1204] Output: Real-time vital data

[1205] Step 2:

[1206] Temperature, humidity, and light sensors installed inside the vehicle measure environmental data inside the vehicle, which is then transmitted to the terminal.

[1207] Input: Temperature, humidity, illuminance (from in-car sensors)

[1208] Output: Real-time environmental data

[1209] Step 3:

[1210] The device sends the collected vital and environmental data to a server, which analyzes the data and determines the user's current condition.

[1211] Input: Vital data, environmental data (from terminal)

[1212] Output: Analysis data (user status)

[1213] Step 4:

[1214] Based on the analyzed data, the server inputs prompts into the generative AI model to generate optimal environment settings, taking into account feedback based on previous data.

[1215] Input: Analysis data (user status), prompt statement

[1216] Output: Optimal Environment Settings

[1217] Step 5:

[1218] The server sends the generated configuration to the device as instructions to apply it, which the device receives and sends to the in-car control system.

[1219] Input: Optimal environment settings (from server)

[1220] Output: Environmental adjustment instructions (from terminal to in-car control system)

[1221] Step 6:

[1222] Actuators inside the vehicle for air conditioning, lighting, humidifiers, seat reclining angles, etc. receive instructions from the terminal and automatically adjust to the specified settings.

[1223] Input: Environment adjustment instructions (from terminal)

[1224] Output: Controlled environment (temperature, humidity, light, seat angle)

[1225] Step 7:

[1226] Users input their sleep-related questions and concerns through a smartphone or vehicle interface, and the device then sends this information to a server.

[1227] Input: User's question or concern

[1228] Output: Query data (to server)

[1229] Step 8:

[1230] The server analyzes the user's query and generates appropriate action suggestions, also driven by a generative AI model, such as breathing exercises to help with relaxation.

[1231] Input: Question data (from user), analyzed by generative AI model

[1232] Output: Action suggestions

[1233] Step 9:

[1234] The server then sends the generated action suggestions to the device and provides them to the user, who can then take action to further improve their sleep quality.

[1235] Input: Action proposal (from server)

[1236] Output: Feedback to the user

[1237] 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.

[1238] This invention combines a system that collects and analyzes the user's vital data and indoor environmental data in real time to provide the optimal sleeping environment with an emotion engine that recognizes and analyzes the user's emotions. This system makes it possible to set environmental settings and suggest actions that take into account not only the user's physiological data but also their emotional state.

[1239] First, before the user goes to bed, the system collects the data necessary to create a comfortable indoor environment for that person. Vital data (heart rate, body temperature, respiratory rate, etc.) is received in real time from the wearable device worn by the user and uploaded to the server. In addition, indoor environmental data is collected from sensors placed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor).

[1240] The server analyzes this data and generates optimal environmental settings. For example, if the heart rate is stable, it determines that the user is likely to fall into deep sleep and sets the room temperature to 22 degrees. If the humidity is too low, it turns on the humidifier, and if the brightness is too high, it turns off the lights.

[1241] The system then takes the user's emotions into account. The emotion engine analyzes the user's voice and facial expressions to determine their emotions, and sends the results to the server. For example, if the system determines that the user is feeling stressed, it will generate relaxing environmental settings (such as lowering the temperature slightly or enabling silent mode).

[1242] The server then sends instructions to the device to apply these environmental settings, and the device automatically adjusts the room's air conditioner, humidifier, lighting, etc. based on the received instructions, allowing the user to sleep in a comfortable environment.

[1243] Furthermore, the server calculates a sleep score from the collected vital data and indoor environmental data, and uses the results as feedback to reflect in future environmental settings. For example, by fine-tuning the next settings based on the optimal environmental settings obtained from past data, a more accurate environment can be provided.

[1244] When a user inputs questions or concerns about sleep, the device sends the information to a server, which analyzes the data and generates specific recommendations for action, such as "don't use your smartphone before bed" or "try breathing exercises to relax," which are then provided to the user via the device.

[1245] As a concrete example, consider the case where a user wakes up in the middle of the night. Because the user is wearing a wearable device, the server immediately detects an increase in heart rate, and the emotion engine detects the user's anxiety. The server analyzes this data and generates and sends to the device an instruction to lower the temperature by one degree to help the user fall back into deep sleep. The device receives this instruction and automatically adjusts the air conditioning settings, allowing the user to return to sleep in a comfortable environment.

[1246] As described above, the present invention provides an integrated system that improves sleep quality by analyzing the user's emotional state using an emotion engine in addition to the user's vital data and indoor environmental data, and by optimally configuring the environment in real time.

[1247] The processing flow will be explained below.

[1248] Step 1:

[1249] The server receives vital data (heart rate, body temperature, respiratory rate, etc.) from the user's wearable device every 10 minutes, using data communication via Bluetooth or Wi-Fi.

[1250] Step 2:

[1251] The server receives indoor environment data every minute from sensors installed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor). The data from each sensor is acquired via API and stored in a database.

[1252] Step 3:

[1253] The server analyzes the collected vital data and indoor environmental data to evaluate the user's current condition. For example, if the user's heart rate is high, it may determine that the user is feeling stressed.

[1254] Step 4:

[1255] The emotion engine analyzes the user's emotions from their voice and facial expressions and sends the results to the server. For example, it can detect stress or anxiety from the user's tone of voice and choice of words.

[1256] Step 5:

[1257] The server integrates vital data, indoor environment data, and emotional data to generate optimal environmental settings (e.g., temperature 22°C, humidity 45%, illuminance 0 lumens), allowing users to achieve the optimal sleeping environment.

[1258] Step 6:

[1259] The server sends instructions to the terminal to apply the generated environment settings. For example, it sends instructions to set the air conditioner temperature to 22 degrees and the humidifier humidity to 45%.

[1260] Step 7:

[1261] The device adjusts indoor devices such as air conditioners, humidifiers, and lighting based on the received instructions, for example, changing the temperature setting of the air conditioner and turning off the lights.

[1262] Step 8:

[1263] The server calculates a sleep score from the collected vital data and indoor environmental data, which is compared with past data to evaluate sleep quality.

[1264] Step 9:

[1265] The server will provide feedback to the next environment settings based on the sleep score, which will result in more accurate settings from the next time onwards, further optimizing the user's sleep environment.

[1266] Step 10:

[1267] When users input their sleep-related questions or concerns by voice or text, the device receives them and sends them to the server. For example, "I can't fall asleep easily" or "I'm not sleeping well."

[1268] Step 11:

[1269] The server analyzes the received questions and concerns and generates specific advice and action suggestions, such as "don't use electronic devices before bed" or "try breathing exercises to relax."

[1270] Step 12:

[1271] The device notifies the user of any advice or suggested actions received from the server, which are played aloud through the voice assistant or displayed as text through the app's chat function.

[1272] As a concrete example, consider the case where a user wakes up in the middle of the night. Because the user is wearing a wearable device, the server immediately detects an increase in heart rate, and the emotion engine detects the user's anxiety. The server analyzes this data and generates and sends to the device an instruction to lower the temperature by one degree to help the user fall back into deep sleep. The device receives this instruction and automatically adjusts the air conditioning settings, allowing the user to return to sleep in a comfortable environment.

[1273] These are the specific processing steps of this system. At each step, the server, device, and user each play their necessary roles and work together to optimize the sleep environment.

[1274] Example 2

[1275] 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."

[1276] Conventional sleep environment improvement systems collect users' vital data and indoor environmental data to provide an optimal environment, but do not take their emotional state into account when making adjustments, which means they are unable to fully improve the quality of their sleep.In addition, they lack the functionality to properly analyze users' emotional state and sleep-related questions and concerns, and to suggest actions, which means they are unable to achieve comprehensive sleep improvement.

[1277] 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.

[1278] In this invention, the server includes means for collecting vital data, means for collecting indoor environmental data, means for analyzing the collected vital data and indoor environmental data and generating optimal environmental settings, means for sending instructions for applying the generated environmental settings, means for receiving the instructions and adjusting the indoor environment, means for collecting user voice and facial expression data and analyzing emotions, and means for fine-tuning the environmental settings based on the analyzed emotional data. This enables real-time environmental settings that take the user's emotional state into consideration, thereby improving overall sleep quality.

[1279] "Vital data" refers to data that indicates the physiological state of the user, such as heart rate, body temperature, and respiratory rate.

[1280] "Indoor environment data" refers to data that indicates the environmental conditions of the room where the user is located, such as temperature, humidity, illuminance, noise, and wind speed.

[1281] "Analysis" is the process of evaluating the current situation based on collected data and deriving optimal actions and settings.

[1282] "Environmental settings" are instructions for adjusting the operating conditions of home appliances such as air conditioners, humidifiers, and lighting to achieve a comfortable environment for the user.

[1283] "Means for sending instructions" refers to the processes and techniques for transmitting the environmental settings generated by the server to terminals such as air conditioners, humidifiers, and lights.

[1284] "Emotional data" is data that indicates the user's emotions, obtained by analyzing the user's voice and facial expressions.

[1285] The "emotion engine" is a system that uses deep learning and machine learning algorithms to analyze emotions based on the user's voice and facial expression data.

[1286] The "sleep score" is an index used to evaluate a user's sleep quality based on collected vital data and indoor environmental data.

[1287] "Action suggestions" provide specific advice and recommended actions to help users get a good night's sleep.

[1288] "Feedback" is the process of using past data and results to provide information to further optimize the next environment settings.

[1289] This invention combines a system that collects and analyzes the user's vital data and indoor environmental data in real time to provide the optimal sleeping environment with an emotion engine that recognizes and analyzes the user's emotions. This system makes it possible to set environmental settings and suggest actions that take into account not only the user's physiological data but also their emotional state.

[1290] First, before the user goes to bed, the system collects the data necessary to create a comfortable indoor environment for that person. Vital data (heart rate, body temperature, respiratory rate, etc.) is received in real time from the wearable device worn by the user and sent to the terminal. The terminal collects this data and also collects indoor environment data from sensors placed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor). This collected data is uploaded to a server via the Internet.

[1291] The server analyzes the received vital data and indoor environmental data using machine learning algorithms. For example, if the heart rate is stable, it determines that the user is likely to fall into deep sleep and sets the room temperature to 22 degrees. It also adjusts the environment by turning on the humidifier if the humidity is too low, or by turning off the lights if the brightness is too high.

[1292] Next, the server also collects the user's emotional data. The user's voice and facial expression data are collected via the device and analyzed by the emotion engine. This emotion engine uses a deep learning model to recognize the user's emotions (happiness, sadness, stress, etc.). The analysis results are sent to the server, and the environment settings are fine-tuned based on the emotional data. For example, if it is determined that the user is feeling stressed, relaxing environment settings (such as slightly lowering the temperature or enabling silent mode) will be generated.

[1293] The generated environmental settings are sent from the server to the device, which then automatically adjusts the room's air conditioner, humidifier, lighting, etc. based on the received instructions, allowing the user to sleep in a comfortable environment.

[1294] The server then calculates a sleep score from the collected vital data and indoor environmental data. The sleep score is compared with past data and reflected in the next environment settings. This feedback allows for more accurate environment settings.

[1295] When users input their sleep-related questions or concerns into the device, the information is sent to a server. The server then uses a generative AI model to analyze the input information, generate specific action suggestions, and provide them to the user via the device. For example, specific advice such as "don't use your smartphone before bed" or "try breathing exercises to relax" is provided.

[1296] For example, if a user wakes up in the middle of the night, the server will immediately detect an increase in heart rate because the user is wearing a wearable device, and the emotion engine will detect the user's anxiety. The server will analyze this data and generate an instruction to lower the temperature by one degree to help the user fall back into deep sleep, which will be sent to the device. The device will then receive this instruction and automatically adjust the air conditioning settings, allowing the user to return to sleep in a comfortable environment.

[1297] Examples of prompts include:

[1298] "I've been waking up a lot lately in the middle of the night. How can I get into a deep sleep?"

[1299] As described above, the present invention provides an integrated system that analyzes the user's emotional state using an emotion engine in addition to the user's vital data and indoor environmental data, thereby optimizing the environment settings in real time and improving sleep quality.

[1300] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1301] Step 1:

[1302] The user puts on the wearable device before going to bed, and the device measures vital data such as heart rate, body temperature, and respiratory rate in real time and transmits them to a device via Bluetooth.

[1303] Input: Vital data from the user

[1304] Output: Vital data sent to the device

[1305] Specific operation: Heart rate and body temperature data are measured at regular intervals and sent to the device via Bluetooth communication.

[1306] Step 2:

[1307] The device collects indoor environment data from sensors placed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor), and this data is streamed to the device.

[1308] Input: Environmental data from indoor sensors

[1309] Output: Environmental data aggregated on the device

[1310] Specific operation: Environmental data is acquired from each sensor at regular intervals and stored in the device's data storage.

[1311] Step 3:

[1312] The device uploads the collected vital data and indoor environmental data to a server via the Internet. This data is transmitted encrypted.

[1313] Input: Vital and environmental data stored on the device

[1314] Output: Data uploaded to the server

[1315] Specific operation: The data is packaged in a batch process and sent to the server using the SSL / TLS protocol.

[1316] Step 4:

[1317] The server analyzes the received vital data and indoor environmental data using machine learning algorithms, for example, to estimate the user's sleep depth from heart rate fluctuations.

[1318] Input: Uploaded vital and environmental data

[1319] Output: Optimal Environment Settings

[1320] Specific operation: Based on the collected data, a machine learning model is used to calculate environmental adjustment values ​​(temperature, humidity, etc.).

[1321] Step 5:

[1322] The device collects the user's voice and facial expression data. Voice data is acquired through a microphone, and facial expression data is acquired through a camera. These data are pre-processed on the device and sent to the emotion engine.

[1323] Input: Voice data and facial expression data from the user

[1324] Output: Preprocessed speech and facial expression data

[1325] Specific operations: Performs preprocessing such as removing noise from the audio and extracting facial features, preparing it for transmission to the emotion engine.

[1326] Step 6:

[1327] The server analyzes the user's emotions using an emotion engine, classifies emotions (such as joy, sadness, stress) using a deep learning model, and sends the results to the server.

[1328] Input: Preprocessed speech and facial expression data

[1329] Output: Parsed emotion data

[1330] What it does: Runs a deep learning emotion classification model to identify emotional states.

[1331] Step 7:

[1332] The server then uses the emotional data to fine-tune environmental settings, for example, lowering the room temperature slightly or enabling quiet mode if the user is feeling stressed.

[1333] Input: Parsed emotion data

[1334] Output: Tweaked preferences

[1335] Specific behavior: Further adjust existing environment settings taking into account sentiment analysis results.

[1336] Step 8:

[1337] The server sends the generated environmental settings to the device, which then automatically controls the air conditioner, humidifier, lighting, etc. based on the received instructions.

[1338] Input: Generated environment settings

[1339] Output: Operation instructions for home appliances via terminal

[1340] Specific operation: Sends control signals to each home appliance and changes its settings.

[1341] Step 9:

[1342] The server calculates the user's sleep score based on vital and environmental data collected throughout the night, and this score is fed back as the basis for the next environmental setting.

[1343] Input: overnight vital and environmental data

[1344] Output: Sleep score and feedback for next settings

[1345] Specific operation: Analyzes data, evaluates sleep quality, and calculates feedback values ​​to be reflected in the next settings.

[1346] Step 10:

[1347] When users input their sleep-related questions or concerns, the information is sent from the device to a server, which then uses a generative AI model to analyze the input information and generate and provide specific action suggestions.

[1348] Input: Questions and concerns from users

[1349] Output: Specific action suggestions

[1350] Specific operation: The generative AI model analyzes the prompt sentence, generates appropriate advice, and provides it to the user.

[1351] Example prompt sentence:

[1352] "I've been waking up a lot lately in the middle of the night. How can I get into a deep sleep?"

[1353] (Application example 2)

[1354] 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."

[1355] There is a need to provide a comfortable environment for customers in physical stores, but conventional systems have not been able to set the environment taking into account the customer's vital data or emotional state.Furthermore, they have not incorporated feedback on the environment settings or action suggestions based on emotional data analysis, which has limited the comfort they can provide.

[1356] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting vital data, means for collecting indoor environmental data, means for analyzing the collected vital data and indoor environmental data and generating optimal environmental settings, means for sending instructions for applying the generated environmental settings, means for receiving the instructions and adjusting the indoor environment, means for analyzing emotional data, means for generating environmental settings based on the customer's emotional state, and means for applying the generated environmental settings to lighting, sound, temperature, and humidity settings in the store. This makes it possible to perform optimal environmental settings in real time based on the customer's vital data and emotional data. Furthermore, by providing feedback to the next environmental settings based on the comfort score, it is possible to continuously provide an optimal comfortable environment and further increase customer satisfaction by suggesting actions based on customer inquiries.

[1357] "Vital data" refers to physiological data such as a customer's heart rate, body temperature, and respiratory rate.

[1358] "Indoor environment data" refers to data related to indoor environmental parameters such as temperature, humidity, lighting, noise, and wind speed.

[1359] "Environmental settings" refers to various settings to provide an optimal indoor environment based on collected and analyzed vital data and indoor environmental data.

[1360] "Emotional data" refers to data about a customer's emotional state (e.g., stress, relaxation, etc.) analyzed from voice and facial expressions.

[1361] "Emotional state" refers to the emotions and psychological state that a customer is currently experiencing.

[1362] "Lighting" refers to artificial lighting used to adjust the brightness of a room.

[1363] "Acoustics" refers to the settings for music and other sounds played in the room.

[1364] "Temperature and humidity settings" refers to settings for adjusting the temperature and humidity in the room.

[1365] "Comfort score" refers to a numerical value that indicates the comfort felt by a customer, calculated based on vital data and indoor environmental data.

[1366] "Action suggestions" refer to specific action suggestions provided to customers based on data analysis.

[1367] An embodiment of the present invention is a system for providing a comfortable environment for customers in a physical store. This system is implemented in the following manner.

[1368] First, vital data (heart rate, body temperature, respiratory rate, etc.) is collected from the wearable device worn by the customer. This data is uploaded to a server in real time. At the same time, indoor environmental data is collected from sensors placed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor).

[1369] The server analyzes the collected vital data and indoor environmental data to generate optimal environmental settings using data analysis tools such as Python or R. For example, if the heart rate is high, it determines that the customer is feeling stressed and generates instructions to lower the room temperature. It also activates the humidifier if the humidity is too low, and turns off the lights if the brightness is too high.

[1370] In addition, the server uses an emotion engine to take into account the customer's emotions. The emotion engine analyzes emotions from the customer's voice and facial expressions and sends the results to the server. For example, if it determines that the customer is feeling stressed, it generates relaxing environmental settings (such as lowering the temperature slightly or enabling quiet mode). The emotion engine uses voice analysis software (such as Google Speech-to-Text API) and facial expression analysis software (such as OpenFace).

[1371] Next, the server sends instructions to the device to apply these environmental settings. The device automatically adjusts the room's air conditioner, humidifier, lighting, etc. based on the received instructions. This is done using an IoT device management platform (such as AWS IoT Core). This allows customers to spend their time in a comfortable environment.

[1372] Furthermore, the server calculates a comfort score from the collected vital data and indoor environmental data, and uses the result as feedback to reflect in future environmental settings. For example, by fine-tuning the next settings based on the optimal environmental settings obtained from past data, a more accurate environment can be provided.

[1373] When a customer inputs a question or concern about comfort, the device sends the information to the server, which analyzes it and generates specific action suggestions, such as "take breaks at regular intervals" or "try stretching to relax," which are provided to the customer via the device.

[1374] Examples and prompts

[1375] As a specific application example, the following prompt sentence can be used to analyze the customer's condition using an AI model and set the optimal environment.

[1376] Specific examples

[1377] Suppose a customer wearing a wearable device enters a store. The customer says, "It's a little hot in this store," and their face looks tired. In this case, the system analyzes the customer's voice and facial expression and sets the following environment settings:

[1378] Set the temperature to 22 degrees.

[1379] Adjust the lighting to be slightly darker.

[1380] Play relaxation music.

[1381] Prompt statement

[1382] "Analyze the customer's emotional state from their voice and facial expressions, and then set the optimal indoor environment based on the results. For example, if the customer says they're hot, lower the temperature. If they look tired, dim the lights and play relaxing music."

[1383] This allows us to build a system that maximizes customer comfort in brick-and-mortar stores. By analyzing customers' vital signs and emotional data in real time and setting the optimal environment, we can continuously provide a high level of comfort.

[1384] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1385] Step 1:

[1386] Vital data (heart rate, body temperature, respiratory rate, etc.) is collected from the wearable device worn by the user. This data is uploaded to a server in real time. The input is the vital data obtained from the wearable device, and this data is temporarily stored as an output that is accumulated on the server.

[1387] Step 2:

[1388] Indoor environment data is collected from sensors placed in the room (temperature sensor, humidity sensor, illuminance sensor, noise sensor, wind speed sensor). These data are sent to the server and serve as input to understand the current state of environmental parameters. As an output, the collected indoor environment data is stored on the server.

[1389] Step 3:

[1390] The server analyzes the collected vital data and indoor environment data to generate optimal environmental settings. The analysis is performed using data analysis tools (such as Python or R). The input is the data collected in steps 1 and 2, and the output is the environmental settings generated based on this data. For example, if the heart rate is high, the output will be to lower the room temperature.

[1391] Step 4:

[1392] The server uses an emotion engine to analyze the user's emotional data. It identifies the user's emotional state by analyzing their voice and facial expressions. The input is emotion data obtained from voice analysis software (such as Google Speech-to-Text API) and facial expression analysis software (such as OpenFace). The output is the analyzed emotional state, such as whether the user is stressed or relaxed.

[1393] Step 5:

[1394] The server comprehensively analyzes vital data, indoor environment data, and emotional data to regenerate optimal environmental settings. The input is the data obtained at each step, and the output is the optimal environmental settings determined comprehensively. For example, the output may set the room temperature to 22 degrees, dim the lights, and play relaxation music.

[1395] Step 6:

[1396] The server sends instructions to the device to apply the generated configuration. The input is the configuration generated in step 5, and the output is data that indicates the configuration. This is done using an IoT device management platform (such as AWS IoT Core).

[1397] Step 7:

[1398] The device automatically adjusts the room's air conditioner, humidifier, lighting, etc. based on instructions received from the server. The input is the environmental setting instructions received from the server, and the output is the actual adjustment of the physical device. This operation is performed using the connected IoT device.

[1399] Step 8:

[1400] The server receives inquiries from users and analyzes their content. Natural language processing (NLP) technology is used for the analysis. The input is data related to the user's questions and concerns, and the output is the analysis results. For example, specific action suggestions, such as how to take breaks, are generated.

[1401] Step 9:

[1402] The server calculates a comfort score from the collected vital data and indoor environmental data, and reflects the results in future environmental settings. The input is the data collected up to that point, and the output is feedback information that will be used in the next environmental settings. For example, optimal room temperature and humidity settings can be fine-tuned based on past data.

[1403] Step 10:

[1404] The server provides the user with action suggestions. The input is the action suggestion data analyzed in step 8, and the output is the specific suggestion to be conveyed to the user. For example, advice such as "take a break at regular intervals" or "try stretching to relax" is provided.

[1405] In this way, the system of the present invention can implement a series of processes to maximize customer comfort.

[1406] 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.

[1407] 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.

[1408] 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.

[1409] 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.

[1410] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1411] 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.

[1412] 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).

[1413] 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.

[1414] 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."

[1415] 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.

[1416] 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).

[1417] 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.

[1418] 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.

[1419] 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.

[1420] 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.

[1421] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1422] 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.

[1423] 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.

[1424] 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.

[1425] 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.

[1426] 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.

[1427] The following is further disclosed regarding the above embodiment.

[1428] (Claim 1)

[1429] a means for collecting vital data;

[1430] a means for collecting indoor environmental data;

[1431] A means for analyzing the collected vital data and indoor environment data and generating optimal environment settings;

[1432] means for transmitting instructions to apply the generated configuration;

[1433] means for receiving the instruction and adjusting the indoor environment;

[1434] A system including:

[1435] (Claim 2)

[1436] a means for calculating a sleep score from the collected vital data and indoor environment data;

[1437] The sleep score is further provided with a means for providing feedback to the next environmental setting.

[1438] 10. The system of claim 1.

[1439] (Claim 3)

[1440] means for receiving and analyzing queries from said users;

[1441] means for generating action suggestions based on the analysis results;

[1442] and means for providing the suggested actions to the user.

[1443] 10. The system of claim 1.

[1444] "Example 1"

[1445] Claims

[1446] (Claim 1)

[1447] a means for collecting vital data from the wearable device;

[1448] means for collecting indoor environmental data from a detector disposed in the room;

[1449] A means for analyzing the collected vital data and indoor environment data and generating optimal environment settings;

[1450] means for transmitting instructions to apply the generated configuration;

[1451] means for receiving the instruction and automatically adjusting the indoor environment;

[1452] A system including:

[1453] (Claim 2)

[1454] a means for calculating a sleep evaluation value from the collected vital data and indoor environment data;

[1455] The sleep evaluation value is used to provide feedback to the next environmental setting.

[1456] 10. The system of claim 1.

[1457] (Claim 3)

[1458] means for receiving and analyzing queries from said users;

[1459] means for generating action suggestions based on the analysis results;

[1460] and means for providing the suggested actions to the user.

[1461] 10. The system of claim 1.

[1462] "Application Example 1"

[1463] (Claim 1)

[1464] a means for collecting vital data;

[1465] a means for collecting indoor environmental data;

[1466] A means for analyzing the collected vital data and indoor environment data and generating optimal environment settings;

[1467] means for transmitting instructions to apply the generated configuration;

[1468] means for receiving the instruction and adjusting the indoor environment;

[1469] means for collecting environmental data within the vehicle;

[1470] A means for adjusting the air conditioning, lighting, and seat reclining angle inside the vehicle;

[1471] A system including:

[1472] (Claim 2)

[1473] a means for calculating a sleep score from the collected vital data and indoor environment data;

[1474] A means for providing feedback to the next environmental setting based on the sleep score;

[1475] The device further includes a means for calculating a sleep score from the collected environmental data inside the vehicle.

[1476] 10. The system of claim 1.

[1477] (Claim 3)

[1478] means for receiving and analyzing queries from said users;

[1479] means for generating action suggestions based on the analysis results;

[1480] means for providing said suggested actions to a user;

[1481] and further including means for inputting prompts to the generative AI model.

[1482] 10. The system of claim 1.

[1483] "Example 2: Combining Emotion Engines"

[1484] (Claim 1)

[1485] a means for collecting vital data;

[1486] a means for collecting indoor environmental data;

[1487] A means for analyzing the collected vital data and indoor environment data and generating optimal environment settings;

[1488] means for transmitting instructions to apply the generated configuration;

[1489] means for receiving the instruction and adjusting the indoor environment;

[1490] A means of collecting user voice and facial expression data and analyzing emotions,

[1491] A means to fine-tune the environment settings based on the analyzed emotional data; and

[1492] A system including:

[1493] (Claim 2)

[1494] A means for calculating a sleep score from the collected vital data and indoor environment data;

[1495] A means for providing feedback to the next environmental setting based on the sleep score;

[1496] Also includes

[1497] 10. The system of claim 1.

[1498] (Claim 3)

[1499] means for receiving and analyzing user inquiries;

[1500] means for generating action suggestions based on the analysis results;

[1501] means for providing said suggested actions to a user;

[1502] Also includes

[1503] 10. The system of claim 1.

[1504] "Application example 2 when combining emotion engines"

[1505] (Claim 1)

[1506] a means for collecting vital data;

[1507] a means for collecting indoor environmental data;

[1508] A means for analyzing the collected vital data and indoor environment data and generating optimal environment settings;

[1509] means for transmitting instructions to apply the generated configuration;

[1510] means for receiving the instruction and adjusting the indoor environment;

[1511] a means for analyzing emotion data;

[1512] means for generating a preference based on the emotional state of the customer;

[1513] A means for applying the generated environmental settings to lighting, sound, temperature and humidity settings in the store;

[1514] A system including:

[1515] (Claim 2)

[1516] A means for calculating a comfort score from the collected vital data and indoor environment data;

[1517] The device further includes a means for providing feedback to the next environmental setting based on the comfort score.

[1518] 10. The system of claim 1.

[1519] (Claim 3)

[1520] means for receiving and analyzing inquiries from said customers;

[1521] means for generating action suggestions based on the analysis results;

[1522] and means for providing said action suggestions to the customer.

[1523] 10. The system of claim 1. [Explanation of symbols]

[1524] 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 vital data; a means for collecting indoor environmental data; A means for analyzing the collected vital data and indoor environment data and generating optimal environment settings; means for transmitting instructions to apply the generated configuration; means for receiving the instruction and adjusting the indoor environment; A system including:

2. a means for calculating a sleep score from the collected vital data and indoor environment data; The sleep score is further provided with a means for providing feedback to the next environmental setting. The system of claim 1 .

3. means for receiving and analyzing queries from said users; means for generating action suggestions based on the analysis results; and means for providing the suggested actions to the user. The system of claim 1 .

Images