system

Abstract

We provide the system. [Solution] A means of collecting biometric information in real time, An artificial intelligence means for analyzing the aforementioned biometric information and generating an optimal sleep plan for each individual user, A means of monitoring the health status of residents in nursing care facilities and notifying caregivers of individually optimized sleep schedules and relaxation activities, A communication means for notifying the user of recommendations based on the sleep plan generated by the artificial intelligence means, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, many people are troubled by poor-quality sleep due to irregular life rhythms, stress, and environmental factors. This is particularly prominent among business people, athletes, and the elderly, and is a cause of adverse effects on health and quality of life. The present invention aims to solve these problems and realize a sustainable and healthy life by providing an optimal sleep schedule and advice for each user.

Means for Solving the Problems

[0005] This invention utilizes a device that collects biological information in real time. This allows for the collection of data such as each user's heart rate, body temperature, and respiratory patterns. Next, artificial intelligence analyzes this collected data to generate a sleep schedule best suited to the user's biological rhythm. Furthermore, personalized advice is efficiently provided by notifying the user of the generated sleep schedule recommendations via communication means such as push notifications. It is also possible to collect feedback on the implementation and effectiveness of the notified advice, enabling continuous improvement of the advice. Through these means, users can improve the quality of their sleep.

[0006] "Biometric information" refers to data obtained from the human body, such as heart rate, body temperature, and breathing patterns.

[0007] "Real-time" refers to data acquisition and processing occurring instantly with virtually no delay.

[0008] "Device" refers to a machine or instrument designed to perform a specific function or task.

[0009] "Analysis" is the process of meticulously examining the obtained data to derive meaning and patterns.

[0010] "User" refers to the individual person who utilizes this invention.

[0011] A "sleep schedule" refers to a plan of bedtime, wake-up time, and sleep duration, which is adjusted to achieve optimal sleep quality.

[0012] Artificial intelligence is a technology that allows computers to mimic tasks that require human intelligence.

[0013] "Communication means" refers to a method or device for sending and receiving information.

[0014] "Push notification" refers to an information message actively sent from a system to a user's device.

[0015] "Recommendation" refers to a proposal for guiding and advising actions and selections that a user should take.

Brief Description of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a system that uses a wearable device to collect a user's biometric information in real time, uses artificial intelligence to analyze that information, generates an individually optimized sleep schedule, and notifies the user of it using communication means.

[0038] Terminal role

[0039] The device (wearable device) continuously monitors biometric information such as heart rate, body temperature, and breathing patterns. The device temporarily stores the collected data and, after a predetermined time or when a certain amount of data is reached, transmits the data to a server via Bluetooth or Wi-Fi communication. This data is tagged individually for each user and transmitted to the server in an identifiable format.

[0040] Server Role

[0041] The server receives biometric information transmitted from the terminal in real time and stores this information in a database. An artificial intelligence for data analysis resides on the server, which analyzes the user's biological rhythm based on the received data. It performs comparative analysis with past data to evaluate for abnormalities or new changes in health status. Based on the analysis results, it generates an optimal sleep schedule for the user and determines sleep recommendations.

[0042] Recommendation notification

[0043] The server-generated recommendations are sent to the user's smartphone app via push notifications. The app visually displays specific advice to the user, such as recommended bedtimes, wake-up times, and relaxation methods. Notifications are sent at an appropriate frequency so as not to overwhelm the user.

[0044] Examples of use and effects

[0045] For example, if a user consistently sleeps less than average and experiences decreased daytime performance, the server's artificial intelligence analyzes this data and recommends going to bed earlier in the following days. Specifically, it might recommend listening to relaxation music at a particular time of night or practicing specific breathing techniques. Users who receive this notification can then act on the advised schedule and provide feedback to the server indicating that their sleep quality has actually improved.

[0046] In this way, the present invention can support the establishment of healthy sleep habits by providing users with continuous and individually optimized sleep improvement measures.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The device continuously monitors the user's biometric information, such as heart rate, body temperature, and breathing patterns, using built-in sensors. This data is collected in real time and stored in a temporary buffer within the device.

[0050] Step 2:

[0051] The device checks the biometric data in the buffer at regular intervals, and when a predetermined amount of data is reached or a certain period of time has elapsed, it starts transferring the data. This data is transmitted to the server via Bluetooth or Wi-Fi communication.

[0052] Step 3:

[0053] The server receives biometric data transmitted from the terminal. The received data is recorded in a database and stored along with a user-specific tag.

[0054] Step 4:

[0055] The server begins analyzing biometric information in the database using artificial intelligence. The AI ​​captures heart rate variability patterns and the associated changes in body temperature to evaluate the user's current biological rhythm.

[0056] Step 5:

[0057] The server's AI analyzes the results and compares them with past data to create the most suitable sleep schedule for the user. This schedule includes recommended bedtimes and wake-up times.

[0058] Step 6:

[0059] The generated sleep schedule and recommendations are pushed from the server to the user's smartphone app. This allows the user to receive advice immediately.

[0060] Step 7:

[0061] Users review the notified sleep schedule and recommendations and practice lifestyle habits according to the advice. Users can also send feedback about their actual experience and the effects to the server through the app.

[0062] Step 8:

[0063] The server receives feedback from users. This feedback is used to improve the accuracy of future analyses and recommendations. The system as a whole can then provide personalized advice tailored to each user.

[0064] (Example 1)

[0065] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0066] Many people today find it difficult to get quality sleep due to stress and irregular lifestyles. This can lead to health problems and decreased performance in daily life. Therefore, there is a need to develop systems that provide optimal sleep plans based on individual biological information and promote quality sleep.

[0067] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0068] In this invention, the server includes means for collecting biometric information in real time and temporarily storing it in a memory device; a machine learning model for analyzing the biometric information received from the device, evaluating the biological rhythm, and generating an optimal sleep plan for an individual user; and communication means for visually displaying the suggested content based on the sleep plan generated by the machine learning model and transmitting it at an appropriate frequency. This makes it possible to provide sleep improvement measures tailored to individual needs.

[0069] "Biometric information" refers to data that indicates the physiological state of each user, such as circulatory function values, body temperature, and respiratory patterns.

[0070] A "device" is a device that collects biological information in real time and temporarily stores it in a memory device.

[0071] A "memory device" is a data storage system used to temporarily store collected biometric information.

[0072] A "machine learning model" is an algorithm that analyzes received biometric information and evaluates circadian rhythms to generate an optimal sleep plan for each individual user.

[0073] "Communication means" refers to technology for visually displaying the proposed content based on the generated sleep plan and transmitting it at an appropriate frequency.

[0074] A "sleep plan" is a schedule that shows optimized sleep onset and wake-up times for each user, based on analyzed biometric information.

[0075] "Biological rhythms" refer to patterns of internal bodily changes over time, analyzed based on the physiological data of individual users.

[0076] "Suggested content" refers to advice and recommendations based on a sleep plan generated by a machine learning model.

[0077] The present invention comprises a system including a wearable device, a data analysis server, and a user-facing display application. The wearable device (terminal) is equipped with sensors that collect biometric information from the user in real time, including heart rate, body temperature, and respiratory patterns. The device temporarily stores this information in its internal memory. Once a certain amount of data has been collected, or after a set time has elapsed, it transmits the information to the server using Bluetooth or Wi-Fi.

[0078] The server immediately processes the received biometric information and stores it in a database. The server has a machine learning model implemented that uses the collected biometric information to analyze the user's circadian rhythm. By comparing this with past health data, the model can generate an optimal sleep plan for each individual user. This plan is effectively designed based on the data predicted by the machine learning model.

[0079] The generated sleep plan and recommendations are pushed to the user's smartphone application via communication. This application visually displays the plan and advice and guides the user in applying it in real life. For example, it may recommend relaxation music to help the user fall asleep at a specific time. This feature enables users to develop healthy sleep habits in their daily lives.

[0080] For example, if a user chronically sleeps too little and lacks attention during the day, the server's machine learning model analyzes this information and recommends that the user fall asleep earlier. This includes specific suggestions such as listening to relaxing music at a particular time of night or practicing calming breathing exercises. If the user who receives this notification follows the advice and successfully improves their sleep quality, they can provide feedback to the server.

[0081] An example of a prompt using a generative AI model is: "Create a prompt to assist in designing a machine learning model that generates an optimal sleep schedule based on the user's biometric information."

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The device collects biometric information such as the user's heart rate, body temperature, and breathing pattern in real time using sensors. The collected data is temporarily stored in a memory device. The input is the user's biometric information, and the output is the stored data. No special data processing is performed at this stage, but initial filtering is carried out to maintain data integrity.

[0085] Step 2:

[0086] The device transmits data to a server via Bluetooth or Wi-Fi once a certain amount of data has accumulated or a pre-set time interval has elapsed. The input for this operation is stored biometric information, and the output is the data transmitted to the server. A user identification tag is attached during transmission, allowing data to be distinguished for each user.

[0087] Step 3:

[0088] The server stores the data received from the terminal in a database. The input is biometric information sent from the terminal, and the output is the data stored in the database. The server converts this data into a format suitable for analysis and performs data cleansing to detect errors and anomalies.

[0089] Step 4:

[0090] The generative AI model implemented on the server analyzes biometric information in the database to evaluate the user's biological rhythm. The input is stored biometric data, and the output is the user's optimal sleep schedule generated through the analysis. This process utilizes statistical inference using historical data and machine learning techniques.

[0091] Step 5:

[0092] The server creates personalized recommendations for the user based on the analysis results. The input is the generated sleep schedule, and the output is the recommendations for the user. The recommendations are further adjusted based on the user's lifestyle and past behavioral data.

[0093] Step 6:

[0094] The server sends push notifications of generated recommendations to the user's smartphone. The input is the recommendations created by the server, and the output is the notification message sent to the user. The timing and frequency of notifications are designed with the user in mind, so as not to be a burden.

[0095] Step 7:

[0096] Users receive notifications via a smartphone app and perform recommended actions. Input is the suggested content from the server, and output is the user's behavioral changes and feedback. The user's actions and results are sent back to the server as feedback from the device and used for future analysis.

[0097] (Application Example 1)

[0098] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0099] Elderly individuals and those requiring health management often face difficulties in maintaining appropriate health conditions. Therefore, there is a need for systems that can provide individually optimized sleep plans and relaxation activities. However, conventional technologies are insufficient for individualized care, and nursing facilities face challenges in efficiently conducting continuous health monitoring and providing information to caregivers.

[0100] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0101] In this invention, the server includes means for collecting biometric information in real time, artificial intelligence means for analyzing the biometric information and generating an optimal sleep plan for each individual user, and means for monitoring the health status of users in nursing care facilities and notifying caregivers of individually optimized sleep schedules and relaxation activities. This enables efficient health management of users in nursing care facilities and allows for individually optimized care.

[0102] "Biometric information" refers to data obtained from within the human body, such as heart rate, body temperature, and breathing patterns.

[0103] "Artificial intelligence" is a technology that uses computer programs to mimic human intellectual activity, analyzes biological information, and generate individually optimized sleep plans.

[0104] A "sleep plan" is a schedule that indicates the optimal bedtime and wake-up time for the user, generated based on the analysis of their biological information.

[0105] A "nursing care facility" is a facility where elderly people and those who require health management live or stay and receive nursing care and health management services.

[0106] "Monitoring" refers to the act of continuously observing and recording biological information in order to understand and manage one's health status.

[0107] "Communication methods" refer to technologies and methods for transmitting information to users and caregivers, and include push notifications.

[0108] "Relaxation activities" are actions and methods aimed at alleviating the mental and physical tension and stress of users and bringing them into a comfortable state.

[0109] One embodiment of this invention is a system that efficiently monitors the health status of users in a nursing care facility and provides individually optimized sleep plans and relaxation activities.

[0110] The server receives data from a terminal that collects biometric information in real time. The terminal continuously monitors the user's biometric information, such as heart rate, body temperature, and respiratory patterns, and transmits the data to the server via Bluetooth or Wi-Fi communication. The server contains a database and an artificial intelligence system, where the collected biometric information is stored. The artificial intelligence analyzes this data and evaluates the user's health status by comparing it with past data.

[0111] Based on the analysis results, the server generates an individually optimized sleep plan. For example, if the user has light sleep and wakes up frequently, adjustments to the sleep environment or the introduction of relaxation music will be suggested. The generated plan and recommendations are pushed to the caregiver's smartphone or tablet via communication means. This allows caregivers to understand the user's health status and provide appropriate care based on that information.

[0112] This system's software can utilize Pandas for data management and Scikit-learn for artificial intelligence analysis. This enables efficient real-time data analysis and personalized health management.

[0113] For example, if a user's sleep quality at night is deemed to be poor, the server recommends going to bed at 10 PM and suggests playing relaxation music. Based on the feedback received, the server can then use a generative AI model to suggest further improvements.

[0114] Examples of prompts for a generative AI model:

[0115] "Please propose measures to improve nighttime sleep for elderly individuals. Obtained biometric data: heart rate 75, body temperature 36.5 degrees Celsius."

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The device collects biometric information such as the user's heart rate, body temperature, and breathing pattern in real time through sensors. This data is temporarily stored in the device's memory. The input is biometric information from the biosensors, and the output is the collected data. In this step, the sensors are attached to the user's body and continuously measure data.

[0119] Step 2:

[0120] The terminal transmits data to the server via Bluetooth or Wi-Fi communication when a certain amount of data has been accumulated or at predetermined intervals. The input is biometric information stored in the terminal, and the output is the transmission of data to the server. In this step, the terminal uses a communication module to establish a connection and transmit data packets.

[0121] Step 3:

[0122] The server stores biometric information received from the terminal in a database. The input is the biometric information sent from the terminal, and the output is its storage in the database. In this step, the server uses a database management system to organize the data and generate indexes as needed.

[0123] Step 4:

[0124] Artificial intelligence on the server analyzes biometric information stored in a database. This analysis detects changes or anomalies in the user's health status by comparing it with past data, and generates an optimal sleep plan for each individual. The input is biometric information stored in the database, and the output is the generated sleep plan. In this step, the artificial intelligence model performs data analysis using a supervised learning algorithm and utilizes a generative AI model to make predictions.

[0125] Step 5:

[0126] The server pushes the generated sleep plan and relaxation activity recommendations to the caregiver's smartphone or tablet. The input is the generated plan and recommendations, and the output is the notification to the caregiver. In this step, the server sends the notification to the device via a communication interface.

[0127] Step 6:

[0128] The user (caregiver) can adjust the care of the user based on the received notification and send feedback back to the server. The input is information obtained via push notification, and the output is feedback data sent to the server. In this step, the caregiver reviews the notification content and makes appropriate decisions.

[0129] The above describes the processing flow of this system.

[0130] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0131] This invention is a system that combines a wearable device and an emotion engine. It collects the user's biometric information in real time and uses artificial intelligence to analyze that information. Furthermore, it uses the emotion engine to recognize the user's emotional state and incorporates the obtained emotional information into the generation of a sleep schedule. The aim is to notify the user of a individually optimized sleep schedule and emotionally-based recommendations.

[0132] Terminal role

[0133] The device (wearable device) continuously monitors the user's biometric information, including heart rate, body temperature, and breathing patterns. This data is temporarily stored within the device and periodically transmitted to a server via Bluetooth or Wi-Fi communication. The device also infers the user's emotional state through an emotion engine. This inference is based on changes in biometric information, the user's voice, facial expressions, and other factors.

[0134] Server Role

[0135] The server receives biometric and emotional data transmitted from the terminal and records it in a database. The artificial intelligence installed on the server analyzes this data to evaluate the user's biological rhythm and emotional state. Based on the analysis results, it generates the sleep schedule best suited to the user's lifestyle and emotions and determines recommendations. In this process, the influence of emotions such as stress and anxiety on sleep is taken into particular consideration.

[0136] Recommendation notification

[0137] The generated sleep schedule and emotion-based recommendations are pushed from the server to the user's smartphone app. The app displays instructions visually and intuitively to the user, presenting suggestions in a way that is easy for the user to follow.

[0138] Examples of use and effects

[0139] For example, if a user complains of insomnia due to work stress, the emotional engine will sense this stress and use that information to suggest a special sleep schedule that includes relaxation techniques. This schedule recommends going to bed early, meditation or breathing exercises before bedtime, and using stress-reducing apps. If the user follows this advice and feels they have achieved deeper sleep than usual, they can provide feedback on the effect through the app.

[0140] This system supports improved sleep quality and enhanced well-being and performance in daily life by providing sleep improvement measures tailored to the user's physical and mental state.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] The device uses built-in sensors to monitor the user's heart rate, body temperature, and breathing patterns in real time. This data is stored within the device and saved to a buffer at regular intervals.

[0144] Step 2:

[0145] The device periodically transmits biometric information stored in a buffer to the server. Transmission occurs via Bluetooth or Wi-Fi, and the transmitted data is tagged to identify each user.

[0146] Step 3:

[0147] The device's emotion engine infers the user's emotional state from biometric data, voice, and facial expressions. For example, it can detect stress from an increased heart rate or specific facial expression patterns.

[0148] Step 4:

[0149] The emotion information inferred by the emotion engine is also sent to the server. The server records this information in a database along with biometric data.

[0150] Step 5:

[0151] The server starts analyzing the accumulated data using artificial intelligence. It analyzes heart rate fluctuations and body temperature patterns, and evaluates the user's biological rhythm, including their emotional state.

[0152] Step 6:

[0153] Based on the evaluation obtained from the analysis, the server generates an optimal sleep schedule that takes into account the user's emotional state. For example, if a high stress level is detected, it recommends going to bed early and relaxing.

[0154] Step 7:

[0155] The generated sleep schedule and recommendations are pushed from the server to the user's smartphone app. The notification includes specific bedtimes and relaxation techniques.

[0156] Step 8:

[0157] Users review and implement recommendations notified through the app. For example, they might practice deep breathing or meditation before going to bed.

[0158] Step 9:

[0159] Users can provide feedback through the app, reporting on the effectiveness of the advice they received and the quality of their sleep.

[0160] Step 10:

[0161] The server receives user feedback and uses it to improve the accuracy of future analyses and recommendations. This ensures that personalized advice is continuously provided to each user.

[0162] (Example 2)

[0163] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0164] The lack of personalized rest plans adapted to each user's physical state and emotional changes leads to an inability to ensure quality sleep, resulting in a decline in the quality of daily life and productivity. Conventional methods only offer general sleep recommendations, failing to provide accurate guidance based on individual circumstances.

[0165] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0166] In this invention, the server includes means for acquiring biometric data in real time, means for analyzing the biometric data to create a rest plan suitable for individual users, and means for inferring emotional states and reflecting them in the creation of the rest plan. This makes it possible to provide individually optimized rest plans based on the user's specific biometric patterns and emotional states.

[0167] "Biometric data" refers to information that indicates the user's physical condition, such as heart rate, temperature, and breathing patterns.

[0168] A "device" is a device worn by a user to collect biometric data, and it acquires data in real time using sensor technology.

[0169] The "intelligent system" is a program that utilizes artificial intelligence to analyze collected biometric data and create personalized rest plans for each user.

[0170] "Information transmission means" refers to methods for communicating rest plans and recommendations created by intelligent systems to users, such as automatic notifications via smartphone apps.

[0171] "Emotional state" refers to information that indicates the user's feelings and mental state, and is inferred based on changes in voice, facial expressions, and other biometric data.

[0172] A "rest plan" is a specific schedule for getting appropriate sleep and rest based on the analysis results.

[0173] This system works by using devices as terminals to collect the user's biometric data in real time and sending the data to a server. Specifically, the hardware used includes wearable devices containing heart rate sensors, temperature sensors, and respiratory monitors. These devices are equipped with Bluetooth or Wi-Fi communication capabilities and encrypt the biometric data before sending it to the server.

[0174] The server records biometric data in a database each time it receives it and analyzes it using an intelligent system. It utilizes a generative AI model to analyze the user's biological rhythms and emotional state. The software used includes artificial intelligence algorithms for data analysis, generating a personalized rest plan. This plan is tailored to the user's lifestyle and emotional patterns.

[0175] Recommendations and rest plans are delivered via push notifications to a smartphone app, allowing users to take specific actions by following visual instructions. For example, a suggested rest plan might include instructions such as "meditate for 5 minutes before going to bed and listen to specific relaxing music." This system enables users to take concrete actions to achieve high-quality sleep, thereby improving their quality of life.

[0176] A concrete example of a prompt message would be sending a request to a generative AI model such as, "The user is experiencing insomnia due to work stress. Please suggest a special sleep schedule based on emotional data and biometric information. Please also consider relaxation techniques, breathing exercises, and the use of stress-reducing apps."

[0177] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0178] Step 1:

[0179] The device collects the user's heart rate, temperature, and breathing patterns in real time. This is done using multiple sensors built into the device. The biometric data acquired as input is temporarily stored within the device. This data is monitored to understand how it changes and to grasp the user's behavior. Specifically, sensors make contact with the user's skin and periodically measure data. The collected data is then prepared to be transmitted to a server.

[0180] Step 2:

[0181] The device periodically sends temporarily stored biometric data to the server. Communication uses Bluetooth or Wi-Fi technology, and the data is transmitted encrypted. Biometric data is securely sent to the server as input, and this is received by the server as output. Specifically, the device activates its communication module at pre-configured intervals, sending data to the server address.

[0182] Step 3:

[0183] The server receives biometric data transmitted from the terminal and records it in a database. Next, this data is analyzed by an intelligent system. It receives biometric and emotional data as input and processes the data using a generative AI model. The output is an evaluation result of the user's biological rhythm and emotional state. In this process, algorithms are used to identify abnormal patterns by comparing them with past data history.

[0184] Step 4:

[0185] The server generates individually optimized rest plans based on the analysis results. Using the analysis results as input, it derives the optimal rest schedule through a specific algorithm. The output includes a detailed rest plan tailored to the user's state. It utilizes a generative AI model to form special recommendations that take the user's emotions into account.

[0186] Step 5:

[0187] The server pushes the generated rest plan and recommendations to the smartphone app. The input is the generated rest plan, which is then communicated to the user through an information transmission method. The output is a visual instruction displayed on the user's smartphone. Specifically, the server sends data directly to the app, allowing the user to implement it immediately.

[0188] Step 6:

[0189] Users follow instructions via a smartphone app and perform designated rest activities. Based on the rest plan received as input, users take corresponding actions. The output is the activities the user actually performed and their results. Specifically, they can experience effective rest by following the provided guidelines and then send feedback to the app.

[0190] (Application Example 2)

[0191] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0192] Conventional sleep improvement systems often propose a uniform rest plan based on the user's biometric data, failing to adequately consider the individual user's living environment and activity schedule. As a result, accurate rest guidance is not provided to users with diverse lifestyles, limiting the effectiveness of health promotion.

[0193] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0194] In this invention, the server includes means for collecting biometric information in real time, an artificial program for analyzing the biometric information and generating an optimal rest schedule for individual users, communication means for notifying the user of recommendations based on the rest schedule generated by the artificial program, and environmental data integration means for adjusting the rest schedule and health suggestions in consideration of the user's living environment information. This makes it possible to propose an optimal rest plan that corresponds to the user's individual living environment and activity schedule.

[0195] "Biometric information" refers to physiological data such as the heart rate, body temperature, and breathing patterns of individual users.

[0196] An "artificial program" is a digital program that generates a rest schedule tailored to each user based on their biometric information.

[0197] "Communication methods" refer to digital communication technologies used to transmit generated rest schedules and recommendations to users.

[0198] An "environmental data integration system" is a system that integrates information about the user's living environment and adjusts rest schedules and health suggestions based on that information.

[0199] "Cardiovascular data" refers to physiological information related to the cardiovascular system, such as heart rate and blood pressure.

[0200] The "information transmission function" is a function that transmits information to users using a communication network.

[0201] The system for realizing this invention utilizes a biological information collection device, an artificial program, communication means, and environmental data integration means.

[0202] First, wearable devices, as terminals, collect biometric information such as the user's heart rate, body temperature, and breathing patterns in real time. This information is periodically transmitted to a smartphone or server via Bluetooth or Wi-Fi. Specific examples of wearable devices include typical smartwatches.

[0203] Next, the server receives the collected biometric information and performs detailed data analysis. This analysis utilizes dedicated servers or cloud platforms to run generative AI models. Based on the analysis results, an artificial program generates an optimal rest schedule for the user. This system incorporates environmental data integration to provide a more accurate schedule by combining the user's individual data with environmental data.

[0204] Furthermore, the system will send push notifications to users' smartphones and other devices containing rest schedules and recommendations generated via communication methods. Users can receive this information in an intuitively easy-to-understand format and put the suggested schedule into action.

[0205] As a concrete example, a system can be implemented that considers weather data for the user's residential area and recommends getting up early and taking a morning walk on sunny days. In this case, by utilizing a generative AI model and using prompt statements such as, "If sunny weather is expected on a weekday morning, encourage the user to get up early and suggest a refreshing morning activity," the advice can be dynamically updated.

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] The wearable device, acting as a terminal, collects the user's heart rate, body temperature, and breathing patterns in real time. This input data provides information about the user's current physiological state. The collected biometric information is temporarily stored within the device.

[0209] Step 2:

[0210] The device transmits the collected biometric information to the smartphone via Bluetooth or Wi-Fi. The input is biometric information on the wearable device, and the output is stored as a data file on the smartphone. Data transfer takes place in this step.

[0211] Step 3:

[0212] The smartphone uploads the received biometric information to a server in the cloud. The input is the biometric information stored on the smartphone, and the output is a dataset available in the cloud. This process makes the data ready for analysis.

[0213] Step 4:

[0214] The server uses a generated AI model to analyze biometric information. The input is a dataset of biometric information stored in the cloud, and the output is an evaluation of the user's current physiological and emotional state as an analysis result. This step specifically involves data analysis and pattern recognition.

[0215] Step 5:

[0216] The server generates an optimal rest schedule for the user based on the analysis results. The input is the evaluation results of the user's physiological and emotional state, and the output is a customized rest schedule. In this step, the generation AI model utilizes prompts to calculate and generate a schedule that suits the user's state.

[0217] Step 6:

[0218] The server pushes the generated rest schedule and recommendations to the user's smartphone via a communication method. The input is the generated rest schedule, and the output is the alert displayed to the user on their smartphone. This step involves information distribution.

[0219] Step 7:

[0220] The user reviews the received rest schedule and incorporates it into their daily life. The input is the schedule displayed on their smartphone, and the output is the improvement in the user's lifestyle rhythm and health status. This step involves applying the schedule to actual lifestyle habits.

[0221] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0222] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0223] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0227] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0228] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0229] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0230] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0231] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0232] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0233] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0234] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0235] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0236] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0237] This invention is a system that uses a wearable device to collect a user's biometric information in real time, uses artificial intelligence to analyze that information, generates an individually optimized sleep schedule, and notifies the user of it using communication means.

[0238] Terminal role

[0239] The device (wearable device) continuously monitors biometric information such as heart rate, body temperature, and breathing patterns. The device temporarily stores the collected data and, after a predetermined time or when a certain amount of data is reached, transmits the data to a server via Bluetooth or Wi-Fi communication. This data is tagged individually for each user and transmitted to the server in an identifiable format.

[0240] Server Role

[0241] The server receives biometric information transmitted from the terminal in real time and stores this information in a database. An artificial intelligence for data analysis resides on the server, which analyzes the user's biological rhythm based on the received data. It performs comparative analysis with past data to evaluate for abnormalities or new changes in health status. Based on the analysis results, it generates an optimal sleep schedule for the user and determines sleep recommendations.

[0242] Recommendation notification

[0243] The server-generated recommendations are sent to the user's smartphone app via push notifications. The app visually displays specific advice to the user, such as recommended bedtimes, wake-up times, and relaxation methods. Notifications are sent at an appropriate frequency so as not to overwhelm the user.

[0244] Examples of use and effects

[0245] For example, if a user consistently sleeps less than average and experiences decreased daytime performance, the server's artificial intelligence analyzes this data and recommends going to bed earlier in the following days. Specifically, it might recommend listening to relaxation music at a particular time of night or practicing specific breathing techniques. Users who receive this notification can then act on the advised schedule and provide feedback to the server indicating that their sleep quality has actually improved.

[0246] In this way, the present invention can support the establishment of healthy sleep habits by providing users with continuous and individually optimized sleep improvement measures.

[0247] The following describes the processing flow.

[0248] Step 1:

[0249] The device continuously monitors the user's biometric information, such as heart rate, body temperature, and breathing patterns, using built-in sensors. This data is collected in real time and stored in a temporary buffer within the device.

[0250] Step 2:

[0251] The device checks the biometric data in the buffer at regular intervals, and when a predetermined amount of data is reached or a certain period of time has elapsed, it starts transferring the data. This data is transmitted to the server via Bluetooth or Wi-Fi communication.

[0252] Step 3:

[0253] The server receives biometric data transmitted from the terminal. The received data is recorded in a database and stored along with a user-specific tag.

[0254] Step 4:

[0255] The server begins analyzing biometric information in the database using artificial intelligence. The AI ​​captures heart rate variability patterns and the associated changes in body temperature to evaluate the user's current biological rhythm.

[0256] Step 5:

[0257] The server's AI analyzes the results and compares them with past data to create the most suitable sleep schedule for the user. This schedule includes recommended bedtimes and wake-up times.

[0258] Step 6:

[0259] The generated sleep schedule and recommendations are pushed from the server to the user's smartphone app. This allows the user to receive advice immediately.

[0260] Step 7:

[0261] Users review the notified sleep schedule and recommendations and practice lifestyle habits according to the advice. Users can also send feedback about their actual experience and the effects to the server through the app.

[0262] Step 8:

[0263] The server receives feedback from users. This feedback is used to improve the accuracy of future analyses and recommendations. The system as a whole can then provide personalized advice tailored to each user.

[0264] (Example 1)

[0265] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0266] Many people today find it difficult to get quality sleep due to stress and irregular lifestyles. This can lead to health problems and decreased performance in daily life. Therefore, there is a need to develop systems that provide optimal sleep plans based on individual biological information and promote quality sleep.

[0267] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0268] In this invention, the server includes means for collecting biometric information in real time and temporarily storing it in a memory device; a machine learning model for analyzing the biometric information received from the device, evaluating the biological rhythm, and generating an optimal sleep plan for an individual user; and communication means for visually displaying the suggested content based on the sleep plan generated by the machine learning model and transmitting it at an appropriate frequency. This makes it possible to provide sleep improvement measures tailored to individual needs.

[0269] "Biometric information" refers to data that indicates the physiological state of each user, such as circulatory function values, body temperature, and respiratory patterns.

[0270] A "device" is a device that collects biological information in real time and temporarily stores it in a memory device.

[0271] A "memory device" is a data storage system used to temporarily store collected biometric information.

[0272] A "machine learning model" is an algorithm that analyzes received biometric information and evaluates circadian rhythms to generate an optimal sleep plan for each individual user.

[0273] "Communication means" refers to technology for visually displaying the proposed content based on the generated sleep plan and transmitting it at an appropriate frequency.

[0274] A "sleep plan" is a schedule that shows optimized sleep onset and wake-up times for each user, based on analyzed biometric information.

[0275] "Biological rhythms" refer to patterns of internal bodily changes over time, analyzed based on the physiological data of individual users.

[0276] "Suggested content" refers to advice and recommendations based on a sleep plan generated by a machine learning model.

[0277] The present invention comprises a system including a wearable device, a data analysis server, and a user-facing display application. The wearable device (terminal) is equipped with sensors that collect biometric information from the user in real time, including heart rate, body temperature, and respiratory patterns. The device temporarily stores this information in its internal memory. Once a certain amount of data has been collected, or after a set time has elapsed, it transmits the information to the server using Bluetooth or Wi-Fi.

[0278] The server immediately processes the received biometric information and stores it in a database. The server has a machine learning model implemented that uses the collected biometric information to analyze the user's circadian rhythm. By comparing this with past health data, the model can generate an optimal sleep plan for each individual user. This plan is effectively designed based on the data predicted by the machine learning model.

[0279] The generated sleep plan and recommendations are pushed to the user's smartphone application via communication. This application visually displays the plan and advice and guides the user in applying it in real life. For example, it may recommend relaxation music to help the user fall asleep at a specific time. This feature enables users to develop healthy sleep habits in their daily lives.

[0280] For example, if a user chronically sleeps too little and lacks attention during the day, the server's machine learning model analyzes this information and recommends that the user fall asleep earlier. This includes specific suggestions such as listening to relaxing music at a particular time of night or practicing calming breathing exercises. If the user who receives this notification follows the advice and successfully improves their sleep quality, they can provide feedback to the server.

[0281] As an example of a prompt sentence using a generative AI model, there is one that says, "Please create a prompt for assisting in the design of a machine learning model that generates an optimal sleep schedule based on the user's biometric information."

[0282] The flow of the specific process in Example 1 will be described using FIG. 11.

[0283] Step 1:

[0284] The terminal collects the user's biometric information such as heart rate, body temperature, and breathing pattern in real time with a sensor. The collected data is temporarily stored in a storage device. The input is the user's biometric information, and the output is the stored data. Although no special data processing is performed at this stage, initial filtering is carried out to maintain data integrity.

[0285] Step 2:

[0286] When a certain amount of data accumulates in the terminal or a pre-set time interval elapses, the terminal transmits the data to the server using Bluetooth or Wi-Fi. The input for this operation is the stored biometric information, and the output is the data transmitted to the server. Since a user identification tag is added during transmission, data can be distinguished for each user.

[0287] Step 3:

[0288] The server stores the data received from the terminal in a database. The input is the biometric information transmitted from the terminal, and the output is the data stored in the database. The server converts this data into a format suitable for analysis and performs data cleansing to detect errors and outliers.

[0289] Step 4:

[0290] The generative AI model implemented on the server analyzes biometric information in the database to evaluate the user's biological rhythm. The input is stored biometric data, and the output is the user's optimal sleep schedule generated through the analysis. This process utilizes statistical inference using historical data and machine learning techniques.

[0291] Step 5:

[0292] The server creates personalized recommendations for the user based on the analysis results. The input is the generated sleep schedule, and the output is the recommendations for the user. The recommendations are further adjusted based on the user's lifestyle and past behavioral data.

[0293] Step 6:

[0294] The server sends push notifications of generated recommendations to the user's smartphone. The input is the recommendations created by the server, and the output is the notification message sent to the user. The timing and frequency of notifications are designed with the user in mind, so as not to be a burden.

[0295] Step 7:

[0296] Users receive notifications via a smartphone app and perform recommended actions. Input is the suggested content from the server, and output is the user's behavioral changes and feedback. The user's actions and results are sent back to the server as feedback from the device and used for future analysis.

[0297] (Application Example 1)

[0298] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0299] Elderly individuals and those requiring health management often face difficulties in maintaining appropriate health conditions. Therefore, there is a need for systems that can provide individually optimized sleep plans and relaxation activities. However, conventional technologies are insufficient for individualized care, and nursing facilities face challenges in efficiently conducting continuous health monitoring and providing information to caregivers.

[0300] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0301] In this invention, the server includes means for collecting biometric information in real time, artificial intelligence means for analyzing the biometric information and generating an optimal sleep plan for each individual user, and means for monitoring the health status of users in nursing care facilities and notifying caregivers of individually optimized sleep schedules and relaxation activities. This enables efficient health management of users in nursing care facilities and allows for individually optimized care.

[0302] "Biometric information" refers to data obtained from within the human body, such as heart rate, body temperature, and breathing patterns.

[0303] "Artificial intelligence" is a technology that uses computer programs to mimic human intellectual activity, analyzes biological information, and generate individually optimized sleep plans.

[0304] A "sleep plan" is a schedule that indicates the optimal bedtime and wake-up time for the user, generated based on the analysis of their biological information.

[0305] A "nursing care facility" is a facility where elderly people and those who require health management live or stay and receive nursing care and health management services.

[0306] "Monitoring" refers to the act of continuously observing and recording biological information in order to understand and manage one's health status.

[0307] "Communication means" refers to technologies and methods for transmitting information to users and caregivers, including push notifications and the like.

[0308] "Relaxation activities" refer to actions and means for alleviating the mental and physical tension and stress of users and bringing about a comfortable state.

[0309] The embodiments for implementing this invention constitute a system that efficiently monitors the health status of users in a care facility and provides individually optimized sleep plans and relaxation activities.

[0310] The server receives data from terminals that collect biometric information in real time. The terminals continuously monitor biometric information such as the user's heart rate, body temperature, and breathing pattern, and transmit the data to the server via Bluetooth or Wi-Fi communication. There is a database and an artificial intelligence system in the server, and the collected biometric information is stored in the database. The artificial intelligence analyzes these data and evaluates the health status of the user by comparing it with past data.

[0311] Based on the analysis results, the server generates an individually optimized sleep plan. For example, when the user's sleep is light and they wake up frequently, adjustments to the sleep environment and the introduction of relaxation music are proposed. The generated plan and recommendations are push-notified to the caregiver's smartphone or tablet using the communication means. As a result, the caregiver can grasp the health status of the user and perform appropriate care based on that information.

[0312] For the software of this system, Pandas can be used for data management and Scikit-learn can be used for artificial intelligence analysis. Thereby, real-time data analysis and individual health management can be efficiently performed.

[0313] For example, if a user's sleep quality at night is deemed to be poor, the server recommends going to bed at 10 PM and suggests playing relaxation music. Based on the feedback received, the server can then use a generative AI model to suggest further improvements.

[0314] Examples of prompts for a generative AI model:

[0315] "Please propose measures to improve nighttime sleep for elderly individuals. Obtained biometric data: heart rate 75, body temperature 36.5 degrees Celsius."

[0316] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0317] Step 1:

[0318] The device collects biometric information such as the user's heart rate, body temperature, and breathing pattern in real time through sensors. This data is temporarily stored in the device's memory. The input is biometric information from the biosensors, and the output is the collected data. In this step, the sensors are attached to the user's body and continuously measure data.

[0319] Step 2:

[0320] The terminal transmits data to the server via Bluetooth or Wi-Fi communication when a certain amount of data has been accumulated or at predetermined intervals. The input is biometric information stored in the terminal, and the output is the transmission of data to the server. In this step, the terminal uses a communication module to establish a connection and transmit data packets.

[0321] Step 3:

[0322] The server stores biometric information received from the terminal in a database. The input is the biometric information sent from the terminal, and the output is its storage in the database. In this step, the server uses a database management system to organize the data and generate indexes as needed.

[0323] Step 4:

[0324] Artificial intelligence on the server analyzes biometric information stored in a database. This analysis detects changes or anomalies in the user's health status by comparing it with past data, and generates an optimal sleep plan for each individual. The input is biometric information stored in the database, and the output is the generated sleep plan. In this step, the artificial intelligence model performs data analysis using a supervised learning algorithm and utilizes a generative AI model to make predictions.

[0325] Step 5:

[0326] The server pushes the generated sleep plan and relaxation activity recommendations to the caregiver's smartphone or tablet. The input is the generated plan and recommendations, and the output is the notification to the caregiver. In this step, the server sends the notification to the device via a communication interface.

[0327] Step 6:

[0328] The user (caregiver) can adjust the care of the user based on the received notification and send feedback back to the server. The input is information obtained via push notification, and the output is feedback data sent to the server. In this step, the caregiver reviews the notification content and makes appropriate decisions.

[0329] The above describes the processing flow of this system.

[0330] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0331] This invention is a system that combines a wearable device and an emotion engine. It collects the user's biometric information in real time and uses artificial intelligence to analyze that information. Furthermore, it uses the emotion engine to recognize the user's emotional state and incorporates the obtained emotional information into the generation of a sleep schedule. The aim is to notify the user of a individually optimized sleep schedule and emotionally-based recommendations.

[0332] Terminal role

[0333] The device (wearable device) continuously monitors the user's biometric information, including heart rate, body temperature, and breathing patterns. This data is temporarily stored within the device and periodically transmitted to a server via Bluetooth or Wi-Fi communication. The device also infers the user's emotional state through an emotion engine. This inference is based on changes in biometric information, the user's voice, facial expressions, and other factors.

[0334] Server Role

[0335] The server receives biometric and emotional data transmitted from the terminal and records it in a database. The artificial intelligence installed on the server analyzes this data to evaluate the user's biological rhythm and emotional state. Based on the analysis results, it generates the sleep schedule best suited to the user's lifestyle and emotions and determines recommendations. In this process, the influence of emotions such as stress and anxiety on sleep is taken into particular consideration.

[0336] Recommendation notification

[0337] The generated sleep schedule and emotion-based recommendations are pushed from the server to the user's smartphone app. The app displays instructions visually and intuitively to the user, presenting suggestions in a way that is easy for the user to follow.

[0338] Examples of use and effects

[0339] For example, if a user complains of insomnia due to work stress, the emotional engine will sense this stress and use that information to suggest a special sleep schedule that includes relaxation techniques. This schedule recommends going to bed early, meditation or breathing exercises before bedtime, and using stress-reducing apps. If the user follows this advice and feels they have achieved deeper sleep than usual, they can provide feedback on the effect through the app.

[0340] This system supports improved sleep quality and enhanced well-being and performance in daily life by providing sleep improvement measures tailored to the user's physical and mental state.

[0341] The following describes the processing flow.

[0342] Step 1:

[0343] The device uses built-in sensors to monitor the user's heart rate, body temperature, and breathing patterns in real time. This data is stored within the device and saved to a buffer at regular intervals.

[0344] Step 2:

[0345] The device periodically transmits biometric information stored in a buffer to the server. Transmission occurs via Bluetooth or Wi-Fi, and the transmitted data is tagged to identify each user.

[0346] Step 3:

[0347] The device's emotion engine infers the user's emotional state from biometric data, voice, and facial expressions. For example, it can detect stress from an increased heart rate or specific facial expression patterns.

[0348] Step 4:

[0349] The emotion information inferred by the emotion engine is also sent to the server. The server records this information in a database along with biometric data.

[0350] Step 5:

[0351] The server starts analyzing the accumulated data using artificial intelligence. It analyzes heart rate fluctuations and body temperature patterns, and evaluates the user's biological rhythm, including their emotional state.

[0352] Step 6:

[0353] Based on the evaluation obtained from the analysis, the server generates an optimal sleep schedule that takes into account the user's emotional state. For example, if a high stress level is detected, it recommends going to bed early and relaxing.

[0354] Step 7:

[0355] The generated sleep schedule and recommendations are pushed from the server to the user's smartphone app. The notification includes specific bedtimes and relaxation techniques.

[0356] Step 8:

[0357] Users review and implement recommendations notified through the app. For example, they might practice deep breathing or meditation before going to bed.

[0358] Step 9:

[0359] Users can provide feedback through the app, reporting on the effectiveness of the advice they received and the quality of their sleep.

[0360] Step 10:

[0361] The server receives user feedback and uses it to improve the accuracy of future analyses and recommendations. This ensures that personalized advice is continuously provided to each user.

[0362] (Example 2)

[0363] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0364] The lack of personalized rest plans adapted to each user's physical state and emotional changes leads to an inability to ensure quality sleep, resulting in a decline in the quality of daily life and productivity. Conventional methods only offer general sleep recommendations, failing to provide accurate guidance based on individual circumstances.

[0365] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0366] In this invention, the server includes means for acquiring biometric data in real time, means for analyzing the biometric data to create a rest plan suitable for individual users, and means for inferring emotional states and reflecting them in the creation of the rest plan. This makes it possible to provide individually optimized rest plans based on the user's specific biometric patterns and emotional states.

[0367] "Biometric data" refers to information that indicates the user's physical condition, such as heart rate, temperature, and breathing patterns.

[0368] A "device" is a device worn by a user to collect biometric data, and it acquires data in real time using sensor technology.

[0369] The "intelligent system" is a program that utilizes artificial intelligence to analyze collected biometric data and create personalized rest plans for each user.

[0370] "Information transmission means" refers to methods for communicating rest plans and recommendations created by intelligent systems to users, such as automatic notifications via smartphone apps.

[0371] "Emotional state" refers to information that indicates the user's feelings and mental state, and is inferred based on changes in voice, facial expressions, and other biometric data.

[0372] A "rest plan" is a specific schedule for getting appropriate sleep and rest based on the analysis results.

[0373] This system works by using devices as terminals to collect the user's biometric data in real time and sending the data to a server. Specifically, the hardware used includes wearable devices containing heart rate sensors, temperature sensors, and respiratory monitors. These devices are equipped with Bluetooth or Wi-Fi communication capabilities and encrypt the biometric data before sending it to the server.

[0374] The server records biometric data in a database each time it receives it and analyzes it using an intelligent system. It utilizes a generative AI model to analyze the user's biological rhythms and emotional state. The software used includes artificial intelligence algorithms for data analysis, generating a personalized rest plan. This plan is tailored to the user's lifestyle and emotional patterns.

[0375] Recommendations and rest plans are delivered via push notifications to a smartphone app, allowing users to take specific actions by following visual instructions. For example, a suggested rest plan might include instructions such as "meditate for 5 minutes before going to bed and listen to specific relaxing music." This system enables users to take concrete actions to achieve high-quality sleep, thereby improving their quality of life.

[0376] A concrete example of a prompt message would be sending a request to a generative AI model such as, "The user is experiencing insomnia due to work stress. Please suggest a special sleep schedule based on emotional data and biometric information. Please also consider relaxation techniques, breathing exercises, and the use of stress-reducing apps."

[0377] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0378] Step 1:

[0379] The device collects the user's heart rate, temperature, and breathing patterns in real time. This is done using multiple sensors built into the device. The biometric data acquired as input is temporarily stored within the device. This data is monitored to understand how it changes and to grasp the user's behavior. Specifically, sensors make contact with the user's skin and periodically measure data. The collected data is then prepared to be transmitted to a server.

[0380] Step 2:

[0381] The device periodically sends temporarily stored biometric data to the server. Communication uses Bluetooth or Wi-Fi technology, and the data is transmitted encrypted. Biometric data is securely sent to the server as input, and this is received by the server as output. Specifically, the device activates its communication module at pre-configured intervals, sending data to the server address.

[0382] Step 3:

[0383] The server receives biometric data transmitted from the terminal and records it in a database. Next, this data is analyzed by an intelligent system. It receives biometric and emotional data as input and processes the data using a generative AI model. The output is an evaluation result of the user's biological rhythm and emotional state. In this process, algorithms are used to identify abnormal patterns by comparing them with past data history.

[0384] Step 4:

[0385] The server generates individually optimized rest plans based on the analysis results. Using the analysis results as input, it derives the optimal rest schedule through a specific algorithm. The output includes a detailed rest plan tailored to the user's state. It utilizes a generative AI model to form special recommendations that take the user's emotions into account.

[0386] Step 5:

[0387] The server pushes the generated rest plan and recommendations to the smartphone app. The input is the generated rest plan, which is then communicated to the user through an information transmission method. The output is a visual instruction displayed on the user's smartphone. Specifically, the server sends data directly to the app, allowing the user to implement it immediately.

[0388] Step 6:

[0389] Users follow instructions via a smartphone app and perform designated rest activities. Based on the rest plan received as input, users take corresponding actions. The output is the activities the user actually performed and their results. Specifically, they can experience effective rest by following the provided guidelines and then send feedback to the app.

[0390] (Application Example 2)

[0391] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0392] Conventional sleep improvement systems often propose a uniform rest plan based on the user's biometric data, failing to adequately consider the individual user's living environment and activity schedule. As a result, accurate rest guidance is not provided to users with diverse lifestyles, limiting the effectiveness of health promotion.

[0393] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0394] In this invention, the server includes means for collecting biometric information in real time, an artificial program for analyzing the biometric information and generating an optimal rest schedule for individual users, communication means for notifying the user of recommendations based on the rest schedule generated by the artificial program, and environmental data integration means for adjusting the rest schedule and health suggestions in consideration of the user's living environment information. This makes it possible to propose an optimal rest plan that corresponds to the user's individual living environment and activity schedule.

[0395] "Biometric information" refers to physiological data such as the heart rate, body temperature, and breathing patterns of individual users.

[0396] An "artificial program" is a digital program that generates a rest schedule tailored to each user based on their biometric information.

[0397] "Communication methods" refer to digital communication technologies used to transmit generated rest schedules and recommendations to users.

[0398] An "environmental data integration system" is a system that integrates information about the user's living environment and adjusts rest schedules and health suggestions based on that information.

[0399] "Cardiovascular data" refers to physiological information related to the cardiovascular system, such as heart rate and blood pressure.

[0400] The "information transmission function" is a function that transmits information to users using a communication network.

[0401] The system for realizing this invention utilizes a biological information collection device, an artificial program, communication means, and environmental data integration means.

[0402] First, wearable devices, as terminals, collect biometric information such as the user's heart rate, body temperature, and breathing patterns in real time. This information is periodically transmitted to a smartphone or server via Bluetooth or Wi-Fi. Specific examples of wearable devices include typical smartwatches.

[0403] Next, the server receives the collected biometric information and performs detailed data analysis. This analysis utilizes dedicated servers or cloud platforms to run generative AI models. Based on the analysis results, an artificial program generates an optimal rest schedule for the user. This system incorporates environmental data integration to provide a more accurate schedule by combining the user's individual data with environmental data.

[0404] Furthermore, the system will send push notifications to users' smartphones and other devices containing rest schedules and recommendations generated via communication methods. Users can receive this information in an intuitively easy-to-understand format and put the suggested schedule into action.

[0405] As a concrete example, a system can be implemented that considers weather data for the user's residential area and recommends getting up early and taking a morning walk on sunny days. In this case, by utilizing a generative AI model and using prompt statements such as, "If sunny weather is expected on a weekday morning, encourage the user to get up early and suggest a refreshing morning activity," the advice can be dynamically updated.

[0406] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0407] Step 1:

[0408] The wearable device, acting as a terminal, collects the user's heart rate, body temperature, and breathing patterns in real time. This input data provides information about the user's current physiological state. The collected biometric information is temporarily stored within the device.

[0409] Step 2:

[0410] The device transmits the collected biometric information to the smartphone via Bluetooth or Wi-Fi. The input is biometric information on the wearable device, and the output is stored as a data file on the smartphone. Data transfer takes place in this step.

[0411] Step 3:

[0412] The smartphone uploads the received biometric information to a server in the cloud. The input is the biometric information stored on the smartphone, and the output is a dataset available in the cloud. This process makes the data ready for analysis.

[0413] Step 4:

[0414] The server uses a generated AI model to analyze biometric information. The input is a dataset of biometric information stored in the cloud, and the output is an evaluation of the user's current physiological and emotional state as an analysis result. This step specifically involves data analysis and pattern recognition.

[0415] Step 5:

[0416] The server generates an optimal rest schedule for the user based on the analysis results. The input is the evaluation results of the user's physiological and emotional state, and the output is a customized rest schedule. In this step, the generation AI model utilizes prompts to calculate and generate a schedule that suits the user's state.

[0417] Step 6:

[0418] The server pushes the generated rest schedule and recommendations to the user's smartphone via a communication method. The input is the generated rest schedule, and the output is the alert displayed to the user on their smartphone. This step involves information distribution.

[0419] Step 7:

[0420] The user reviews the received rest schedule and incorporates it into their daily life. The input is the schedule displayed on their smartphone, and the output is the improvement in the user's lifestyle rhythm and health status. This step involves applying the schedule to actual lifestyle habits.

[0421] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0422] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0423] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0424] [Third Embodiment]

[0425] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0426] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0427] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0428] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0429] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0430] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0431] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0432] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0433] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0434] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0435] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0436] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0437] This invention is a system that uses a wearable device to collect a user's biometric information in real time, uses artificial intelligence to analyze that information, generates an individually optimized sleep schedule, and notifies the user of it using communication means.

[0438] Terminal role

[0439] The device (wearable device) continuously monitors biometric information such as heart rate, body temperature, and breathing patterns. The device temporarily stores the collected data and, after a predetermined time or when a certain amount of data is reached, transmits the data to a server via Bluetooth or Wi-Fi communication. This data is tagged individually for each user and transmitted to the server in an identifiable format.

[0440] Server Role

[0441] The server receives biometric information transmitted from the terminal in real time and stores this information in a database. An artificial intelligence for data analysis resides on the server, which analyzes the user's biological rhythm based on the received data. It performs comparative analysis with past data to evaluate for abnormalities or new changes in health status. Based on the analysis results, it generates an optimal sleep schedule for the user and determines sleep recommendations.

[0442] Recommendation notification

[0443] The server-generated recommendations are sent to the user's smartphone app via push notifications. The app visually displays specific advice to the user, such as recommended bedtimes, wake-up times, and relaxation methods. Notifications are sent at an appropriate frequency so as not to overwhelm the user.

[0444] Examples of use and effects

[0445] For example, if a user consistently sleeps less than average and experiences decreased daytime performance, the server's artificial intelligence analyzes this data and recommends going to bed earlier in the following days. Specifically, it might recommend listening to relaxation music at a particular time of night or practicing specific breathing techniques. Users who receive this notification can then act on the advised schedule and provide feedback to the server indicating that their sleep quality has actually improved.

[0446] In this way, the present invention can support the establishment of healthy sleep habits by providing users with continuous and individually optimized sleep improvement measures.

[0447] The following describes the processing flow.

[0448] Step 1:

[0449] The device continuously monitors the user's biometric information, such as heart rate, body temperature, and breathing patterns, using built-in sensors. This data is collected in real time and stored in a temporary buffer within the device.

[0450] Step 2:

[0451] The device checks the biometric data in the buffer at regular intervals, and when a predetermined amount of data is reached or a certain period of time has elapsed, it starts transferring the data. This data is transmitted to the server via Bluetooth or Wi-Fi communication.

[0452] Step 3:

[0453] The server receives biometric data transmitted from the terminal. The received data is recorded in a database and stored along with a user-specific tag.

[0454] Step 4:

[0455] The server begins analyzing biometric information in the database using artificial intelligence. The AI ​​captures heart rate variability patterns and the associated changes in body temperature to evaluate the user's current biological rhythm.

[0456] Step 5:

[0457] The server's AI analyzes the results and compares them with past data to create the most suitable sleep schedule for the user. This schedule includes recommended bedtimes and wake-up times.

[0458] Step 6:

[0459] The generated sleep schedule and recommendations are pushed from the server to the user's smartphone app. This allows the user to receive advice immediately.

[0460] Step 7:

[0461] Users review the notified sleep schedule and recommendations and practice lifestyle habits according to the advice. Users can also send feedback about their actual experience and the effects to the server through the app.

[0462] Step 8:

[0463] The server receives feedback from users. This feedback is used to improve the accuracy of future analyses and recommendations. The system as a whole can then provide personalized advice tailored to each user.

[0464] (Example 1)

[0465] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0466] Many people today find it difficult to get quality sleep due to stress and irregular lifestyles. This can lead to health problems and decreased performance in daily life. Therefore, there is a need to develop systems that provide optimal sleep plans based on individual biological information and promote quality sleep.

[0467] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0468] In this invention, the server includes means for collecting biometric information in real time and temporarily storing it in a memory device; a machine learning model for analyzing the biometric information received from the device, evaluating the biological rhythm, and generating an optimal sleep plan for an individual user; and communication means for visually displaying the suggested content based on the sleep plan generated by the machine learning model and transmitting it at an appropriate frequency. This makes it possible to provide sleep improvement measures tailored to individual needs.

[0469] "Biometric information" refers to data that indicates the physiological state of each user, such as circulatory function values, body temperature, and respiratory patterns.

[0470] A "device" is a device that collects biological information in real time and temporarily stores it in a memory device.

[0471] A "memory device" is a data storage system used to temporarily store collected biometric information.

[0472] A "machine learning model" is an algorithm that analyzes received biometric information and evaluates circadian rhythms to generate an optimal sleep plan for each individual user.

[0473] "Communication means" refers to technology for visually displaying the proposed content based on the generated sleep plan and transmitting it at an appropriate frequency.

[0474] A "sleep plan" is a schedule that shows optimized sleep onset and wake-up times for each user, based on analyzed biometric information.

[0475] "Biological rhythms" refer to patterns of internal bodily changes over time, analyzed based on the physiological data of individual users.

[0476] "Suggested content" refers to advice and recommendations based on a sleep plan generated by a machine learning model.

[0477] The present invention comprises a system including a wearable device, a data analysis server, and a user-facing display application. The wearable device (terminal) is equipped with sensors that collect biometric information from the user in real time, including heart rate, body temperature, and respiratory patterns. The device temporarily stores this information in its internal memory. Once a certain amount of data has been collected, or after a set time has elapsed, it transmits the information to the server using Bluetooth or Wi-Fi.

[0478] The server immediately processes the received biometric information and stores it in a database. The server has a machine learning model implemented that uses the collected biometric information to analyze the user's circadian rhythm. By comparing this with past health data, the model can generate an optimal sleep plan for each individual user. This plan is effectively designed based on the data predicted by the machine learning model.

[0479] The generated sleep plan and recommendations are pushed to the user's smartphone application via communication. This application visually displays the plan and advice and guides the user in applying it in real life. For example, it may recommend relaxation music to help the user fall asleep at a specific time. This feature enables users to develop healthy sleep habits in their daily lives.

[0480] For example, if a user chronically sleeps too little and lacks attention during the day, the server's machine learning model analyzes this information and recommends that the user fall asleep earlier. This includes specific suggestions such as listening to relaxing music at a particular time of night or practicing calming breathing exercises. If the user who receives this notification follows the advice and successfully improves their sleep quality, they can provide feedback to the server.

[0481] An example of a prompt using a generative AI model is: "Create a prompt to assist in designing a machine learning model that generates an optimal sleep schedule based on the user's biometric information."

[0482] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0483] Step 1:

[0484] The device collects biometric information such as the user's heart rate, body temperature, and breathing pattern in real time using sensors. The collected data is temporarily stored in a memory device. The input is the user's biometric information, and the output is the stored data. No special data processing is performed at this stage, but initial filtering is carried out to maintain data integrity.

[0485] Step 2:

[0486] The device transmits data to a server via Bluetooth or Wi-Fi once a certain amount of data has accumulated or a pre-set time interval has elapsed. The input for this operation is stored biometric information, and the output is the data transmitted to the server. A user identification tag is attached during transmission, allowing data to be distinguished for each user.

[0487] Step 3:

[0488] The server stores the data received from the terminal in a database. The input is biometric information sent from the terminal, and the output is the data stored in the database. The server converts this data into a format suitable for analysis and performs data cleansing to detect errors and anomalies.

[0489] Step 4:

[0490] The generative AI model implemented on the server analyzes biometric information in the database to evaluate the user's biological rhythm. The input is stored biometric data, and the output is the user's optimal sleep schedule generated through the analysis. This process utilizes statistical inference using historical data and machine learning techniques.

[0491] Step 5:

[0492] The server creates personalized recommendations for the user based on the analysis results. The input is the generated sleep schedule, and the output is the recommendations for the user. The recommendations are further adjusted based on the user's lifestyle and past behavioral data.

[0493] Step 6:

[0494] The server sends push notifications of generated recommendations to the user's smartphone. The input is the recommendations created by the server, and the output is the notification message sent to the user. The timing and frequency of notifications are designed with the user in mind, so as not to be a burden.

[0495] Step 7:

[0496] Users receive notifications via a smartphone app and perform recommended actions. Input is the suggested content from the server, and output is the user's behavioral changes and feedback. The user's actions and results are sent back to the server as feedback from the device and used for future analysis.

[0497] (Application Example 1)

[0498] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0499] Elderly individuals and those requiring health management often face difficulties in maintaining appropriate health conditions. Therefore, there is a need for systems that can provide individually optimized sleep plans and relaxation activities. However, conventional technologies are insufficient for individualized care, and nursing facilities face challenges in efficiently conducting continuous health monitoring and providing information to caregivers.

[0500] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0501] In this invention, the server includes means for collecting biometric information in real time, artificial intelligence means for analyzing the biometric information and generating an optimal sleep plan for each individual user, and means for monitoring the health status of users in nursing care facilities and notifying caregivers of individually optimized sleep schedules and relaxation activities. This enables efficient health management of users in nursing care facilities and allows for individually optimized care.

[0502] "Biometric information" refers to data obtained from within the human body, such as heart rate, body temperature, and breathing patterns.

[0503] "Artificial intelligence" is a technology that uses computer programs to mimic human intellectual activity, analyzes biological information, and generate individually optimized sleep plans.

[0504] A "sleep plan" is a schedule that indicates the optimal bedtime and wake-up time for the user, generated based on the analysis of their biological information.

[0505] A "nursing care facility" is a facility where elderly people and those who require health management live or stay and receive nursing care and health management services.

[0506] "Monitoring" refers to the act of continuously observing and recording biological information in order to understand and manage one's health status.

[0507] "Communication methods" refer to technologies and methods for transmitting information to users and caregivers, and include push notifications.

[0508] "Relaxation activities" are actions and methods aimed at alleviating the mental and physical tension and stress of users and bringing them into a comfortable state.

[0509] One embodiment of this invention is a system that efficiently monitors the health status of users in a nursing care facility and provides individually optimized sleep plans and relaxation activities.

[0510] The server receives data from a terminal that collects biometric information in real time. The terminal continuously monitors the user's biometric information, such as heart rate, body temperature, and respiratory patterns, and transmits the data to the server via Bluetooth or Wi-Fi communication. The server contains a database and an artificial intelligence system, where the collected biometric information is stored. The artificial intelligence analyzes this data and evaluates the user's health status by comparing it with past data.

[0511] Based on the analysis results, the server generates an individually optimized sleep plan. For example, if the user has light sleep and wakes up frequently, adjustments to the sleep environment or the introduction of relaxation music will be suggested. The generated plan and recommendations are pushed to the caregiver's smartphone or tablet via communication means. This allows caregivers to understand the user's health status and provide appropriate care based on that information.

[0512] This system's software can utilize Pandas for data management and Scikit-learn for artificial intelligence analysis. This enables efficient real-time data analysis and personalized health management.

[0513] For example, if a user's sleep quality at night is deemed to be poor, the server recommends going to bed at 10 PM and suggests playing relaxation music. Based on the feedback received, the server can then use a generative AI model to suggest further improvements.

[0514] Examples of prompts for a generative AI model:

[0515] "Please propose measures to improve nighttime sleep for elderly individuals. Obtained biometric data: heart rate 75, body temperature 36.5 degrees Celsius."

[0516] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0517] Step 1:

[0518] The device collects biometric information such as the user's heart rate, body temperature, and breathing pattern in real time through sensors. This data is temporarily stored in the device's memory. The input is biometric information from the biosensors, and the output is the collected data. In this step, the sensors are attached to the user's body and continuously measure data.

[0519] Step 2:

[0520] The terminal transmits data to the server via Bluetooth or Wi-Fi communication when a certain amount of data has been accumulated or at predetermined intervals. The input is biometric information stored in the terminal, and the output is the transmission of data to the server. In this step, the terminal uses a communication module to establish a connection and transmit data packets.

[0521] Step 3:

[0522] The server stores biometric information received from the terminal in a database. The input is the biometric information sent from the terminal, and the output is its storage in the database. In this step, the server uses a database management system to organize the data and generate indexes as needed.

[0523] Step 4:

[0524] Artificial intelligence on the server analyzes biometric information stored in a database. This analysis detects changes or anomalies in the user's health status by comparing it with past data, and generates an optimal sleep plan for each individual. The input is biometric information stored in the database, and the output is the generated sleep plan. In this step, the artificial intelligence model performs data analysis using a supervised learning algorithm and utilizes a generative AI model to make predictions.

[0525] Step 5:

[0526] The server pushes the generated sleep plan and relaxation activity recommendations to the caregiver's smartphone or tablet. The input is the generated plan and recommendations, and the output is the notification to the caregiver. In this step, the server sends the notification to the device via a communication interface.

[0527] Step 6:

[0528] The user (caregiver) can adjust the care of the user based on the received notification and send feedback back to the server. The input is information obtained via push notification, and the output is feedback data sent to the server. In this step, the caregiver reviews the notification content and makes appropriate decisions.

[0529] The above describes the processing flow of this system.

[0530] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0531] This invention is a system that combines a wearable device and an emotion engine. It collects the user's biometric information in real time and uses artificial intelligence to analyze that information. Furthermore, it uses the emotion engine to recognize the user's emotional state and incorporates the obtained emotional information into the generation of a sleep schedule. The aim is to notify the user of a individually optimized sleep schedule and emotionally-based recommendations.

[0532] Terminal role

[0533] The device (wearable device) continuously monitors the user's biometric information, including heart rate, body temperature, and breathing patterns. This data is temporarily stored within the device and periodically transmitted to a server via Bluetooth or Wi-Fi communication. The device also infers the user's emotional state through an emotion engine. This inference is based on changes in biometric information, the user's voice, facial expressions, and other factors.

[0534] Server Role

[0535] The server receives biometric and emotional data transmitted from the terminal and records it in a database. The artificial intelligence installed on the server analyzes this data to evaluate the user's biological rhythm and emotional state. Based on the analysis results, it generates the sleep schedule best suited to the user's lifestyle and emotions and determines recommendations. In this process, the influence of emotions such as stress and anxiety on sleep is taken into particular consideration.

[0536] Recommendation notification

[0537] The generated sleep schedule and emotion-based recommendations are pushed from the server to the user's smartphone app. The app displays instructions visually and intuitively to the user, presenting suggestions in a way that is easy for the user to follow.

[0538] Examples of use and effects

[0539] For example, if a user complains of insomnia due to work stress, the emotional engine will sense this stress and use that information to suggest a special sleep schedule that includes relaxation techniques. This schedule recommends going to bed early, meditation or breathing exercises before bedtime, and using stress-reducing apps. If the user follows this advice and feels they have achieved deeper sleep than usual, they can provide feedback on the effect through the app.

[0540] This system supports improved sleep quality and enhanced well-being and performance in daily life by providing sleep improvement measures tailored to the user's physical and mental state.

[0541] The following describes the processing flow.

[0542] Step 1:

[0543] The device uses built-in sensors to monitor the user's heart rate, body temperature, and breathing patterns in real time. This data is stored within the device and saved to a buffer at regular intervals.

[0544] Step 2:

[0545] The device periodically transmits biometric information stored in a buffer to the server. Transmission occurs via Bluetooth or Wi-Fi, and the transmitted data is tagged to identify each user.

[0546] Step 3:

[0547] The device's emotion engine infers the user's emotional state from biometric data, voice, and facial expressions. For example, it can detect stress from an increased heart rate or specific facial expression patterns.

[0548] Step 4:

[0549] The emotion information inferred by the emotion engine is also sent to the server. The server records this information in a database along with biometric data.

[0550] Step 5:

[0551] The server starts analyzing the accumulated data using artificial intelligence. It analyzes heart rate fluctuations and body temperature patterns, and evaluates the user's biological rhythm, including their emotional state.

[0552] Step 6:

[0553] Based on the evaluation obtained from the analysis, the server generates an optimal sleep schedule that takes into account the user's emotional state. For example, if a high stress level is detected, it recommends going to bed early and relaxing.

[0554] Step 7:

[0555] The generated sleep schedule and recommendations are pushed from the server to the user's smartphone app. The notification includes specific bedtimes and relaxation techniques.

[0556] Step 8:

[0557] Users review and implement recommendations notified through the app. For example, they might practice deep breathing or meditation before going to bed.

[0558] Step 9:

[0559] Users can provide feedback through the app, reporting on the effectiveness of the advice they received and the quality of their sleep.

[0560] Step 10:

[0561] The server receives user feedback and uses it to improve the accuracy of future analyses and recommendations. This ensures that personalized advice is continuously provided to each user.

[0562] (Example 2)

[0563] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0564] The lack of personalized rest plans adapted to each user's physical state and emotional changes leads to an inability to ensure quality sleep, resulting in a decline in the quality of daily life and productivity. Conventional methods only offer general sleep recommendations, failing to provide accurate guidance based on individual circumstances.

[0565] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0566] In this invention, the server includes means for acquiring biometric data in real time, means for analyzing the biometric data to create a rest plan suitable for individual users, and means for inferring emotional states and reflecting them in the creation of the rest plan. This makes it possible to provide individually optimized rest plans based on the user's specific biometric patterns and emotional states.

[0567] "Biometric data" refers to information that indicates the user's physical condition, such as heart rate, temperature, and breathing patterns.

[0568] A "device" is a device worn by a user to collect biometric data, and it acquires data in real time using sensor technology.

[0569] The "intelligent system" is a program that utilizes artificial intelligence to analyze collected biometric data and create personalized rest plans for each user.

[0570] "Information transmission means" refers to methods for communicating rest plans and recommendations created by intelligent systems to users, such as automatic notifications via smartphone apps.

[0571] "Emotional state" refers to information that indicates the user's feelings and mental state, and is inferred based on changes in voice, facial expressions, and other biometric data.

[0572] A "rest plan" is a specific schedule for getting appropriate sleep and rest based on the analysis results.

[0573] This system works by using devices as terminals to collect the user's biometric data in real time and sending the data to a server. Specifically, the hardware used includes wearable devices containing heart rate sensors, temperature sensors, and respiratory monitors. These devices are equipped with Bluetooth or Wi-Fi communication capabilities and encrypt the biometric data before sending it to the server.

[0574] The server records biometric data in a database each time it receives it and analyzes it using an intelligent system. It utilizes a generative AI model to analyze the user's biological rhythms and emotional state. The software used includes artificial intelligence algorithms for data analysis, generating a personalized rest plan. This plan is tailored to the user's lifestyle and emotional patterns.

[0575] Recommendations and rest plans are delivered via push notifications to a smartphone app, allowing users to take specific actions by following visual instructions. For example, a suggested rest plan might include instructions such as "meditate for 5 minutes before going to bed and listen to specific relaxing music." This system enables users to take concrete actions to achieve high-quality sleep, thereby improving their quality of life.

[0576] A concrete example of a prompt message would be sending a request to a generative AI model such as, "The user is experiencing insomnia due to work stress. Please suggest a special sleep schedule based on emotional data and biometric information. Please also consider relaxation techniques, breathing exercises, and the use of stress-reducing apps."

[0577] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0578] Step 1:

[0579] The device collects the user's heart rate, temperature, and breathing patterns in real time. This is done using multiple sensors built into the device. The biometric data acquired as input is temporarily stored within the device. This data is monitored to understand how it changes and to grasp the user's behavior. Specifically, sensors make contact with the user's skin and periodically measure data. The collected data is then prepared to be transmitted to a server.

[0580] Step 2:

[0581] The device periodically sends temporarily stored biometric data to the server. Communication uses Bluetooth or Wi-Fi technology, and the data is transmitted encrypted. Biometric data is securely sent to the server as input, and this is received by the server as output. Specifically, the device activates its communication module at pre-configured intervals, sending data to the server address.

[0582] Step 3:

[0583] The server receives biometric data transmitted from the terminal and records it in a database. Next, this data is analyzed by an intelligent system. It receives biometric and emotional data as input and processes the data using a generative AI model. The output is an evaluation result of the user's biological rhythm and emotional state. In this process, algorithms are used to identify abnormal patterns by comparing them with past data history.

[0584] Step 4:

[0585] The server generates individually optimized rest plans based on the analysis results. Using the analysis results as input, it derives the optimal rest schedule through a specific algorithm. The output includes a detailed rest plan tailored to the user's state. It utilizes a generative AI model to form special recommendations that take the user's emotions into account.

[0586] Step 5:

[0587] The server pushes the generated rest plan and recommendations to the smartphone app. The input is the generated rest plan, which is then communicated to the user through an information transmission method. The output is a visual instruction displayed on the user's smartphone. Specifically, the server sends data directly to the app, allowing the user to implement it immediately.

[0588] Step 6:

[0589] Users follow instructions via a smartphone app and perform designated rest activities. Based on the rest plan received as input, users take corresponding actions. The output is the activities the user actually performed and their results. Specifically, they can experience effective rest by following the provided guidelines and then send feedback to the app.

[0590] (Application Example 2)

[0591] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0592] Conventional sleep improvement systems often propose a uniform rest plan based on the user's biometric data, failing to adequately consider the individual user's living environment and activity schedule. As a result, accurate rest guidance is not provided to users with diverse lifestyles, limiting the effectiveness of health promotion.

[0593] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0594] In this invention, the server includes means for collecting biometric information in real time, an artificial program for analyzing the biometric information and generating an optimal rest schedule for individual users, communication means for notifying the user of recommendations based on the rest schedule generated by the artificial program, and environmental data integration means for adjusting the rest schedule and health suggestions in consideration of the user's living environment information. This makes it possible to propose an optimal rest plan that corresponds to the user's individual living environment and activity schedule.

[0595] "Biometric information" refers to physiological data such as the heart rate, body temperature, and breathing patterns of individual users.

[0596] An "artificial program" is a digital program that generates a rest schedule tailored to each user based on their biometric information.

[0597] "Communication methods" refer to digital communication technologies used to transmit generated rest schedules and recommendations to users.

[0598] An "environmental data integration system" is a system that integrates information about the user's living environment and adjusts rest schedules and health suggestions based on that information.

[0599] "Cardiovascular data" refers to physiological information related to the cardiovascular system, such as heart rate and blood pressure.

[0600] The "information transmission function" is a function that transmits information to users using a communication network.

[0601] The system for realizing this invention utilizes a biological information collection device, an artificial program, communication means, and environmental data integration means.

[0602] First, wearable devices, as terminals, collect biometric information such as the user's heart rate, body temperature, and breathing patterns in real time. This information is periodically transmitted to a smartphone or server via Bluetooth or Wi-Fi. Specific examples of wearable devices include typical smartwatches.

[0603] Next, the server receives the collected biometric information and performs detailed data analysis. This analysis utilizes dedicated servers or cloud platforms to run generative AI models. Based on the analysis results, an artificial program generates an optimal rest schedule for the user. This system incorporates environmental data integration to provide a more accurate schedule by combining the user's individual data with environmental data.

[0604] Furthermore, the system will send push notifications to users' smartphones and other devices containing rest schedules and recommendations generated via communication methods. Users can receive this information in an intuitively easy-to-understand format and put the suggested schedule into action.

[0605] As a concrete example, a system can be implemented that considers weather data for the user's residential area and recommends getting up early and taking a morning walk on sunny days. In this case, by utilizing a generative AI model and using prompt statements such as, "If sunny weather is expected on a weekday morning, encourage the user to get up early and suggest a refreshing morning activity," the advice can be dynamically updated.

[0606] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0607] Step 1:

[0608] The wearable device, acting as a terminal, collects the user's heart rate, body temperature, and breathing patterns in real time. This input data provides information about the user's current physiological state. The collected biometric information is temporarily stored within the device.

[0609] Step 2:

[0610] The device transmits the collected biometric information to the smartphone via Bluetooth or Wi-Fi. The input is biometric information on the wearable device, and the output is stored as a data file on the smartphone. Data transfer takes place in this step.

[0611] Step 3:

[0612] The smartphone uploads the received biometric information to a server in the cloud. The input is the biometric information stored on the smartphone, and the output is a dataset available in the cloud. This process makes the data ready for analysis.

[0613] Step 4:

[0614] The server uses a generated AI model to analyze biometric information. The input is a dataset of biometric information stored in the cloud, and the output is an evaluation of the user's current physiological and emotional state as an analysis result. This step specifically involves data analysis and pattern recognition.

[0615] Step 5:

[0616] The server generates an optimal rest schedule for the user based on the analysis results. The input is the evaluation results of the user's physiological and emotional state, and the output is a customized rest schedule. In this step, the generation AI model utilizes prompts to calculate and generate a schedule that suits the user's state.

[0617] Step 6:

[0618] The server pushes the generated rest schedule and recommendations to the user's smartphone via a communication method. The input is the generated rest schedule, and the output is the alert displayed to the user on their smartphone. This step involves information distribution.

[0619] Step 7:

[0620] The user reviews the received rest schedule and incorporates it into their daily life. The input is the schedule displayed on their smartphone, and the output is the improvement in the user's lifestyle rhythm and health status. This step involves applying the schedule to actual lifestyle habits.

[0621] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0622] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0623] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0624] [Fourth Embodiment]

[0625] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0626] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0627] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0628] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0629] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0630] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0631] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0632] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0633] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0634] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0635] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0636] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0637] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0638] This invention is a system that uses a wearable device to collect a user's biometric information in real time, uses artificial intelligence to analyze that information, generates an individually optimized sleep schedule, and notifies the user of it using communication means.

[0639] Terminal role

[0640] The device (wearable device) continuously monitors biometric information such as heart rate, body temperature, and breathing patterns. The device temporarily stores the collected data and, after a predetermined time or when a certain amount of data is reached, transmits the data to a server via Bluetooth or Wi-Fi communication. This data is tagged individually for each user and transmitted to the server in an identifiable format.

[0641] Server Role

[0642] The server receives biometric information transmitted from the terminal in real time and stores this information in a database. An artificial intelligence for data analysis resides on the server, which analyzes the user's biological rhythm based on the received data. It performs comparative analysis with past data to evaluate for abnormalities or new changes in health status. Based on the analysis results, it generates an optimal sleep schedule for the user and determines sleep recommendations.

[0643] Recommendation notification

[0644] The server-generated recommendations are sent to the user's smartphone app via push notifications. The app visually displays specific advice to the user, such as recommended bedtimes, wake-up times, and relaxation methods. Notifications are sent at an appropriate frequency so as not to overwhelm the user.

[0645] Examples of use and effects

[0646] For example, if a user consistently sleeps less than average and experiences decreased daytime performance, the server's artificial intelligence analyzes this data and recommends going to bed earlier in the following days. Specifically, it might recommend listening to relaxation music at a particular time of night or practicing specific breathing techniques. Users who receive this notification can then act on the advised schedule and provide feedback to the server indicating that their sleep quality has actually improved.

[0647] In this way, the present invention can support the establishment of healthy sleep habits by providing users with continuous and individually optimized sleep improvement measures.

[0648] The following describes the processing flow.

[0649] Step 1:

[0650] The device continuously monitors the user's biometric information, such as heart rate, body temperature, and breathing patterns, using built-in sensors. This data is collected in real time and stored in a temporary buffer within the device.

[0651] Step 2:

[0652] The device checks the biometric data in the buffer at regular intervals, and when a predetermined amount of data is reached or a certain period of time has elapsed, it starts transferring the data. This data is transmitted to the server via Bluetooth or Wi-Fi communication.

[0653] Step 3:

[0654] The server receives biometric data transmitted from the terminal. The received data is recorded in a database and stored along with a user-specific tag.

[0655] Step 4:

[0656] The server begins analyzing biometric information in the database using artificial intelligence. The AI ​​captures heart rate variability patterns and the associated changes in body temperature to evaluate the user's current biological rhythm.

[0657] Step 5:

[0658] The server's AI analyzes the results and compares them with past data to create the most suitable sleep schedule for the user. This schedule includes recommended bedtimes and wake-up times.

[0659] Step 6:

[0660] The generated sleep schedule and recommendations are pushed from the server to the user's smartphone app. This allows the user to receive advice immediately.

[0661] Step 7:

[0662] Users review the notified sleep schedule and recommendations and practice lifestyle habits according to the advice. Users can also send feedback about their actual experience and the effects to the server through the app.

[0663] Step 8:

[0664] The server receives feedback from users. This feedback is used to improve the accuracy of future analyses and recommendations. The system as a whole can then provide personalized advice tailored to each user.

[0665] (Example 1)

[0666] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0667] Many people today find it difficult to get quality sleep due to stress and irregular lifestyles. This can lead to health problems and decreased performance in daily life. Therefore, there is a need to develop systems that provide optimal sleep plans based on individual biological information and promote quality sleep.

[0668] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0669] In this invention, the server includes means for collecting biometric information in real time and temporarily storing it in a memory device; a machine learning model for analyzing the biometric information received from the device, evaluating the biological rhythm, and generating an optimal sleep plan for an individual user; and communication means for visually displaying the suggested content based on the sleep plan generated by the machine learning model and transmitting it at an appropriate frequency. This makes it possible to provide sleep improvement measures tailored to individual needs.

[0670] "Biometric information" refers to data that indicates the physiological state of each user, such as circulatory function values, body temperature, and respiratory patterns.

[0671] A "device" is a device that collects biological information in real time and temporarily stores it in a memory device.

[0672] A "memory device" is a data storage system used to temporarily store collected biometric information.

[0673] A "machine learning model" is an algorithm that analyzes received biometric information and evaluates circadian rhythms to generate an optimal sleep plan for each individual user.

[0674] "Communication means" refers to technology for visually displaying the proposed content based on the generated sleep plan and transmitting it at an appropriate frequency.

[0675] A "sleep plan" is a schedule that shows optimized sleep onset and wake-up times for each user, based on analyzed biometric information.

[0676] "Biological rhythms" refer to patterns of internal bodily changes over time, analyzed based on the physiological data of individual users.

[0677] "Suggested content" refers to advice and recommendations based on a sleep plan generated by a machine learning model.

[0678] The present invention comprises a system including a wearable device, a data analysis server, and a user-facing display application. The wearable device (terminal) is equipped with sensors that collect biometric information from the user in real time, including heart rate, body temperature, and respiratory patterns. The device temporarily stores this information in its internal memory. Once a certain amount of data has been collected, or after a set time has elapsed, it transmits the information to the server using Bluetooth or Wi-Fi.

[0679] The server immediately processes the received biometric information and stores it in a database. The server has a machine learning model implemented that uses the collected biometric information to analyze the user's circadian rhythm. By comparing this with past health data, the model can generate an optimal sleep plan for each individual user. This plan is effectively designed based on the data predicted by the machine learning model.

[0680] The generated sleep plan and recommendations are pushed to the user's smartphone application via communication. This application visually displays the plan and advice and guides the user in applying it in real life. For example, it may recommend relaxation music to help the user fall asleep at a specific time. This feature enables users to develop healthy sleep habits in their daily lives.

[0681] For example, if a user chronically sleeps too little and lacks attention during the day, the server's machine learning model analyzes this information and recommends that the user fall asleep earlier. This includes specific suggestions such as listening to relaxing music at a particular time of night or practicing calming breathing exercises. If the user who receives this notification follows the advice and successfully improves their sleep quality, they can provide feedback to the server.

[0682] An example of a prompt using a generative AI model is: "Create a prompt to assist in designing a machine learning model that generates an optimal sleep schedule based on the user's biometric information."

[0683] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0684] Step 1:

[0685] The device collects biometric information such as the user's heart rate, body temperature, and breathing pattern in real time using sensors. The collected data is temporarily stored in a memory device. The input is the user's biometric information, and the output is the stored data. No special data processing is performed at this stage, but initial filtering is carried out to maintain data integrity.

[0686] Step 2:

[0687] The device transmits data to a server via Bluetooth or Wi-Fi once a certain amount of data has accumulated or a pre-set time interval has elapsed. The input for this operation is stored biometric information, and the output is the data transmitted to the server. A user identification tag is attached during transmission, allowing data to be distinguished for each user.

[0688] Step 3:

[0689] The server stores the data received from the terminal in a database. The input is biometric information sent from the terminal, and the output is the data stored in the database. The server converts this data into a format suitable for analysis and performs data cleansing to detect errors and anomalies.

[0690] Step 4:

[0691] The generative AI model implemented on the server analyzes biometric information in the database to evaluate the user's biological rhythm. The input is stored biometric data, and the output is the user's optimal sleep schedule generated through the analysis. This process utilizes statistical inference using historical data and machine learning techniques.

[0692] Step 5:

[0693] The server creates personalized recommendations for the user based on the analysis results. The input is the generated sleep schedule, and the output is the recommendations for the user. The recommendations are further adjusted based on the user's lifestyle and past behavioral data.

[0694] Step 6:

[0695] The server sends push notifications of generated recommendations to the user's smartphone. The input is the recommendations created by the server, and the output is the notification message sent to the user. The timing and frequency of notifications are designed with the user in mind, so as not to be a burden.

[0696] Step 7:

[0697] Users receive notifications via a smartphone app and perform recommended actions. Input is the suggested content from the server, and output is the user's behavioral changes and feedback. The user's actions and results are sent back to the server as feedback from the device and used for future analysis.

[0698] (Application Example 1)

[0699] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0700] Elderly individuals and those requiring health management often face difficulties in maintaining appropriate health conditions. Therefore, there is a need for systems that can provide individually optimized sleep plans and relaxation activities. However, conventional technologies are insufficient for individualized care, and nursing facilities face challenges in efficiently conducting continuous health monitoring and providing information to caregivers.

[0701] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0702] In this invention, the server includes means for collecting biometric information in real time, artificial intelligence means for analyzing the biometric information and generating an optimal sleep plan for each individual user, and means for monitoring the health status of users in nursing care facilities and notifying caregivers of individually optimized sleep schedules and relaxation activities. This enables efficient health management of users in nursing care facilities and allows for individually optimized care.

[0703] "Biometric information" refers to data obtained from within the human body, such as heart rate, body temperature, and breathing patterns.

[0704] "Artificial intelligence" is a technology that uses computer programs to mimic human intellectual activity, analyzes biological information, and generate individually optimized sleep plans.

[0705] A "sleep plan" is a schedule that indicates the optimal bedtime and wake-up time for the user, generated based on the analysis of their biological information.

[0706] A "nursing care facility" is a facility where elderly people and those who require health management live or stay and receive nursing care and health management services.

[0707] "Monitoring" refers to the act of continuously observing and recording biological information in order to understand and manage one's health status.

[0708] "Communication methods" refer to technologies and methods for transmitting information to users and caregivers, and include push notifications.

[0709] "Relaxation activities" are actions and methods aimed at alleviating the mental and physical tension and stress of users and bringing them into a comfortable state.

[0710] One embodiment of this invention is a system that efficiently monitors the health status of users in a nursing care facility and provides individually optimized sleep plans and relaxation activities.

[0711] The server receives data from a terminal that collects biometric information in real time. The terminal continuously monitors the user's biometric information, such as heart rate, body temperature, and respiratory patterns, and transmits the data to the server via Bluetooth or Wi-Fi communication. The server contains a database and an artificial intelligence system, where the collected biometric information is stored. The artificial intelligence analyzes this data and evaluates the user's health status by comparing it with past data.

[0712] Based on the analysis results, the server generates an individually optimized sleep plan. For example, if the user has light sleep and wakes up frequently, adjustments to the sleep environment or the introduction of relaxation music will be suggested. The generated plan and recommendations are pushed to the caregiver's smartphone or tablet via communication means. This allows caregivers to understand the user's health status and provide appropriate care based on that information.

[0713] This system's software can utilize Pandas for data management and Scikit-learn for artificial intelligence analysis. This enables efficient real-time data analysis and personalized health management.

[0714] For example, if a user's sleep quality at night is deemed to be poor, the server recommends going to bed at 10 PM and suggests playing relaxation music. Based on the feedback received, the server can then use a generative AI model to suggest further improvements.

[0715] Examples of prompts for a generative AI model:

[0716] "Please propose measures to improve nighttime sleep for elderly individuals. Obtained biometric data: heart rate 75, body temperature 36.5 degrees Celsius."

[0717] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0718] Step 1:

[0719] The device collects biometric information such as the user's heart rate, body temperature, and breathing pattern in real time through sensors. This data is temporarily stored in the device's memory. The input is biometric information from the biosensors, and the output is the collected data. In this step, the sensors are attached to the user's body and continuously measure data.

[0720] Step 2:

[0721] The terminal transmits data to the server via Bluetooth or Wi-Fi communication when a certain amount of data has been accumulated or at predetermined intervals. The input is biometric information stored in the terminal, and the output is the transmission of data to the server. In this step, the terminal uses a communication module to establish a connection and transmit data packets.

[0722] Step 3:

[0723] The server stores biometric information received from the terminal in a database. The input is the biometric information sent from the terminal, and the output is its storage in the database. In this step, the server uses a database management system to organize the data and generate indexes as needed.

[0724] Step 4:

[0725] Artificial intelligence on the server analyzes biometric information stored in a database. This analysis detects changes or anomalies in the user's health status by comparing it with past data, and generates an optimal sleep plan for each individual. The input is biometric information stored in the database, and the output is the generated sleep plan. In this step, the artificial intelligence model performs data analysis using a supervised learning algorithm and utilizes a generative AI model to make predictions.

[0726] Step 5:

[0727] The server pushes the generated sleep plan and relaxation activity recommendations to the caregiver's smartphone or tablet. The input is the generated plan and recommendations, and the output is the notification to the caregiver. In this step, the server sends the notification to the device via a communication interface.

[0728] Step 6:

[0729] The user (caregiver) can adjust the care of the user based on the received notification and send feedback back to the server. The input is information obtained via push notification, and the output is feedback data sent to the server. In this step, the caregiver reviews the notification content and makes appropriate decisions.

[0730] The above describes the processing flow of this system.

[0731] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0732] This invention is a system that combines a wearable device and an emotion engine. It collects the user's biometric information in real time and uses artificial intelligence to analyze that information. Furthermore, it uses the emotion engine to recognize the user's emotional state and incorporates the obtained emotional information into the generation of a sleep schedule. The aim is to notify the user of a individually optimized sleep schedule and emotionally-based recommendations.

[0733] Terminal role

[0734] The device (wearable device) continuously monitors the user's biometric information, including heart rate, body temperature, and breathing patterns. This data is temporarily stored within the device and periodically transmitted to a server via Bluetooth or Wi-Fi communication. The device also infers the user's emotional state through an emotion engine. This inference is based on changes in biometric information, the user's voice, facial expressions, and other factors.

[0735] Server Role

[0736] The server receives biometric and emotional data transmitted from the terminal and records it in a database. The artificial intelligence installed on the server analyzes this data to evaluate the user's biological rhythm and emotional state. Based on the analysis results, it generates the sleep schedule best suited to the user's lifestyle and emotions and determines recommendations. In this process, the influence of emotions such as stress and anxiety on sleep is taken into particular consideration.

[0737] Recommendation notification

[0738] The generated sleep schedule and emotion-based recommendations are pushed from the server to the user's smartphone app. The app displays instructions visually and intuitively to the user, presenting suggestions in a way that is easy for the user to follow.

[0739] Examples of use and effects

[0740] For example, if a user complains of insomnia due to work stress, the emotional engine will sense this stress and use that information to suggest a special sleep schedule that includes relaxation techniques. This schedule recommends going to bed early, meditation or breathing exercises before bedtime, and using stress-reducing apps. If the user follows this advice and feels they have achieved deeper sleep than usual, they can provide feedback on the effect through the app.

[0741] This system supports improved sleep quality and enhanced well-being and performance in daily life by providing sleep improvement measures tailored to the user's physical and mental state.

[0742] The following describes the processing flow.

[0743] Step 1:

[0744] The device uses built-in sensors to monitor the user's heart rate, body temperature, and breathing patterns in real time. This data is stored within the device and saved to a buffer at regular intervals.

[0745] Step 2:

[0746] The device periodically transmits biometric information stored in a buffer to the server. Transmission occurs via Bluetooth or Wi-Fi, and the transmitted data is tagged to identify each user.

[0747] Step 3:

[0748] The device's emotion engine infers the user's emotional state from biometric data, voice, and facial expressions. For example, it can detect stress from an increased heart rate or specific facial expression patterns.

[0749] Step 4:

[0750] The emotion information inferred by the emotion engine is also sent to the server. The server records this information in a database along with biometric data.

[0751] Step 5:

[0752] The server starts analyzing the accumulated data using artificial intelligence. It analyzes heart rate fluctuations and body temperature patterns, and evaluates the user's biological rhythm, including their emotional state.

[0753] Step 6:

[0754] Based on the evaluation obtained from the analysis, the server generates an optimal sleep schedule that takes into account the user's emotional state. For example, if a high stress level is detected, it recommends going to bed early and relaxing.

[0755] Step 7:

[0756] The generated sleep schedule and recommendations are pushed from the server to the user's smartphone app. The notification includes specific bedtimes and relaxation techniques.

[0757] Step 8:

[0758] Users review and implement recommendations notified through the app. For example, they might practice deep breathing or meditation before going to bed.

[0759] Step 9:

[0760] Users can provide feedback through the app, reporting on the effectiveness of the advice they received and the quality of their sleep.

[0761] Step 10:

[0762] The server receives user feedback and uses it to improve the accuracy of future analyses and recommendations. This ensures that personalized advice is continuously provided to each user.

[0763] (Example 2)

[0764] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0765] The lack of personalized rest plans adapted to each user's physical state and emotional changes leads to an inability to ensure quality sleep, resulting in a decline in the quality of daily life and productivity. Conventional methods only offer general sleep recommendations, failing to provide accurate guidance based on individual circumstances.

[0766] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0767] In this invention, the server includes means for acquiring biometric data in real time, means for analyzing the biometric data to create a rest plan suitable for individual users, and means for inferring emotional states and reflecting them in the creation of the rest plan. This makes it possible to provide individually optimized rest plans based on the user's specific biometric patterns and emotional states.

[0768] "Biometric data" refers to information that indicates the user's physical condition, such as heart rate, temperature, and breathing patterns.

[0769] A "device" is a device worn by a user to collect biometric data, and it acquires data in real time using sensor technology.

[0770] The "intelligent system" is a program that utilizes artificial intelligence to analyze collected biometric data and create personalized rest plans for each user.

[0771] "Information transmission means" refers to methods for communicating rest plans and recommendations created by intelligent systems to users, such as automatic notifications via smartphone apps.

[0772] "Emotional state" refers to information that indicates the user's feelings and mental state, and is inferred based on changes in voice, facial expressions, and other biometric data.

[0773] A "rest plan" is a specific schedule for getting appropriate sleep and rest based on the analysis results.

[0774] This system works by using devices as terminals to collect the user's biometric data in real time and sending the data to a server. Specifically, the hardware used includes wearable devices containing heart rate sensors, temperature sensors, and respiratory monitors. These devices are equipped with Bluetooth or Wi-Fi communication capabilities and encrypt the biometric data before sending it to the server.

[0775] The server records biometric data in a database each time it receives it and analyzes it using an intelligent system. It utilizes a generative AI model to analyze the user's biological rhythms and emotional state. The software used includes artificial intelligence algorithms for data analysis, generating a personalized rest plan. This plan is tailored to the user's lifestyle and emotional patterns.

[0776] Recommendations and rest plans are delivered via push notifications to a smartphone app, allowing users to take specific actions by following visual instructions. For example, a suggested rest plan might include instructions such as "meditate for 5 minutes before going to bed and listen to specific relaxing music." This system enables users to take concrete actions to achieve high-quality sleep, thereby improving their quality of life.

[0777] A concrete example of a prompt message would be sending a request to a generative AI model such as, "The user is experiencing insomnia due to work stress. Please suggest a special sleep schedule based on emotional data and biometric information. Please also consider relaxation techniques, breathing exercises, and the use of stress-reducing apps."

[0778] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0779] Step 1:

[0780] The device collects the user's heart rate, temperature, and breathing patterns in real time. This is done using multiple sensors built into the device. The biometric data acquired as input is temporarily stored within the device. This data is monitored to understand how it changes and to grasp the user's behavior. Specifically, sensors make contact with the user's skin and periodically measure data. The collected data is then prepared to be transmitted to a server.

[0781] Step 2:

[0782] The device periodically sends temporarily stored biometric data to the server. Communication uses Bluetooth or Wi-Fi technology, and the data is transmitted encrypted. Biometric data is securely sent to the server as input, and this is received by the server as output. Specifically, the device activates its communication module at pre-configured intervals, sending data to the server address.

[0783] Step 3:

[0784] The server receives biometric data transmitted from the terminal and records it in a database. Next, this data is analyzed by an intelligent system. It receives biometric and emotional data as input and processes the data using a generative AI model. The output is an evaluation result of the user's biological rhythm and emotional state. In this process, algorithms are used to identify abnormal patterns by comparing them with past data history.

[0785] Step 4:

[0786] The server generates individually optimized rest plans based on the analysis results. Using the analysis results as input, it derives the optimal rest schedule through a specific algorithm. The output includes a detailed rest plan tailored to the user's state. It utilizes a generative AI model to form special recommendations that take the user's emotions into account.

[0787] Step 5:

[0788] The server pushes the generated rest plan and recommendations to the smartphone app. The input is the generated rest plan, which is then communicated to the user through an information transmission method. The output is a visual instruction displayed on the user's smartphone. Specifically, the server sends data directly to the app, allowing the user to implement it immediately.

[0789] Step 6:

[0790] Users follow instructions via a smartphone app and perform designated rest activities. Based on the rest plan received as input, users take corresponding actions. The output is the activities the user actually performed and their results. Specifically, they can experience effective rest by following the provided guidelines and then send feedback to the app.

[0791] (Application Example 2)

[0792] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0793] Conventional sleep improvement systems often propose a uniform rest plan based on the user's biometric data, failing to adequately consider the individual user's living environment and activity schedule. As a result, accurate rest guidance is not provided to users with diverse lifestyles, limiting the effectiveness of health promotion.

[0794] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0795] In this invention, the server includes means for collecting biometric information in real time, an artificial program for analyzing the biometric information and generating an optimal rest schedule for individual users, communication means for notifying the user of recommendations based on the rest schedule generated by the artificial program, and environmental data integration means for adjusting the rest schedule and health suggestions in consideration of the user's living environment information. This makes it possible to propose an optimal rest plan that corresponds to the user's individual living environment and activity schedule.

[0796] "Biometric information" refers to physiological data such as the heart rate, body temperature, and breathing patterns of individual users.

[0797] An "artificial program" is a digital program that generates a rest schedule tailored to each user based on their biometric information.

[0798] "Communication methods" refer to digital communication technologies used to transmit generated rest schedules and recommendations to users.

[0799] An "environmental data integration system" is a system that integrates information about the user's living environment and adjusts rest schedules and health suggestions based on that information.

[0800] "Cardiovascular data" refers to physiological information related to the cardiovascular system, such as heart rate and blood pressure.

[0801] The "information transmission function" is a function that transmits information to users using a communication network.

[0802] The system for realizing this invention utilizes a biological information collection device, an artificial program, communication means, and environmental data integration means.

[0803] First, wearable devices, as terminals, collect biometric information such as the user's heart rate, body temperature, and breathing patterns in real time. This information is periodically transmitted to a smartphone or server via Bluetooth or Wi-Fi. Specific examples of wearable devices include typical smartwatches.

[0804] Next, the server receives the collected biometric information and performs detailed data analysis. This analysis utilizes dedicated servers or cloud platforms to run generative AI models. Based on the analysis results, an artificial program generates an optimal rest schedule for the user. This system incorporates environmental data integration to provide a more accurate schedule by combining the user's individual data with environmental data.

[0805] Furthermore, the system will send push notifications to users' smartphones and other devices containing rest schedules and recommendations generated via communication methods. Users can receive this information in an intuitively easy-to-understand format and put the suggested schedule into action.

[0806] As a concrete example, a system can be implemented that considers weather data for the user's residential area and recommends getting up early and taking a morning walk on sunny days. In this case, by utilizing a generative AI model and using prompt statements such as, "If sunny weather is expected on a weekday morning, encourage the user to get up early and suggest a refreshing morning activity," the advice can be dynamically updated.

[0807] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0808] Step 1:

[0809] The wearable device, acting as a terminal, collects the user's heart rate, body temperature, and breathing patterns in real time. This input data provides information about the user's current physiological state. The collected biometric information is temporarily stored within the device.

[0810] Step 2:

[0811] The device transmits the collected biometric information to the smartphone via Bluetooth or Wi-Fi. The input is biometric information on the wearable device, and the output is stored as a data file on the smartphone. Data transfer takes place in this step.

[0812] Step 3:

[0813] The smartphone uploads the received biometric information to a server in the cloud. The input is the biometric information stored on the smartphone, and the output is a dataset available in the cloud. This process makes the data ready for analysis.

[0814] Step 4:

[0815] The server uses a generated AI model to analyze biometric information. The input is a dataset of biometric information stored in the cloud, and the output is an evaluation of the user's current physiological and emotional state as an analysis result. This step specifically involves data analysis and pattern recognition.

[0816] Step 5:

[0817] The server generates an optimal rest schedule for the user based on the analysis results. The input is the evaluation results of the user's physiological and emotional state, and the output is a customized rest schedule. In this step, the generation AI model utilizes prompts to calculate and generate a schedule that suits the user's state.

[0818] Step 6:

[0819] The server pushes the generated rest schedule and recommendations to the user's smartphone via a communication method. The input is the generated rest schedule, and the output is the alert displayed to the user on their smartphone. This step involves information distribution.

[0820] Step 7:

[0821] The user reviews the received rest schedule and incorporates it into their daily life. The input is the schedule displayed on their smartphone, and the output is the improvement in the user's lifestyle rhythm and health status. This step involves applying the schedule to actual lifestyle habits.

[0822] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0823] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0824] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0825] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0826] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0827] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0828] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0829] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0830] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0831] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0832] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0833] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0834] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0835] 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.

[0836] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0837] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0838] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0839] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0840] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0841] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0842] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0843] The following is further disclosed regarding the embodiments described above.

[0844] (Claim 1)

[0845] A device that collects biological information in real time,

[0846] An artificial intelligence that analyzes the aforementioned biometric information and generates an optimal sleep schedule for each individual user,

[0847] A communication means for notifying the user of recommendations based on the sleep schedule generated by the aforementioned artificial intelligence,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, wherein the aforementioned biological information includes heart rate, body temperature, and respiratory pattern.

[0851] (Claim 3)

[0852] The system according to claim 1, wherein the communication means uses push notifications.

[0853] "Example 1"

[0854] (Claim 1)

[0855] A device that collects biometric information in real time and temporarily stores it in a memory device,

[0856] A machine learning model that analyzes biometric information received from the aforementioned device, evaluates circadian rhythms, and generates an optimal sleep plan for each individual user,

[0857] A communication means for visually displaying and transmitting, at an appropriate frequency, the sleep plan proposed by the aforementioned machine learning model.

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, wherein the biological information includes circulatory function values, body temperature, and respiratory pattern.

[0861] (Claim 3)

[0862] The system according to claim 1, wherein the communication means uses notification technology.

[0863] "Application Example 1"

[0864] (Claim 1)

[0865] A means of collecting biometric information in real time,

[0866] An artificial intelligence means for analyzing the aforementioned biometric information and generating an optimal sleep plan for each individual user,

[0867] A means of monitoring the health status of residents in nursing care facilities and notifying caregivers of individually optimized sleep schedules and relaxation activities,

[0868] A communication means for notifying the user of recommendations based on the sleep plan generated by the artificial intelligence means,

[0869] A system that includes this.

[0870] (Claim 2)

[0871] The system according to claim 1, wherein the aforementioned biological information includes heart rate, body temperature, and respiratory pattern.

[0872] (Claim 3)

[0873] The system according to claim 1, wherein the communication means uses push notifications.

[0874] "Example 2 of combining an emotion engine"

[0875] (Claim 1)

[0876] A device that acquires biometric data in real time,

[0877] An intelligent system that analyzes the aforementioned biometric data and creates a rest plan tailored to individual users,

[0878] Information transmission means for conveying instructions to the user based on the rest plan created by the intelligent system,

[0879] A means of inferring emotional states and incorporating that information into the creation of rest plans,

[0880] A means of analyzing collected biometric and emotional data to determine recommendations tailored to the user's lifestyle patterns and emotions,

[0881] A system that includes this.

[0882] (Claim 2)

[0883] The system according to claim 1, wherein the biometric data includes heart rate, temperature, and respiratory pattern.

[0884] (Claim 3)

[0885] The system according to claim 1, wherein the information transmission means uses automatic notification.

[0886] "Application example 2 when combining with an emotional engine"

[0887] (Claim 1)

[0888] A means of collecting biometric information in real time,

[0889] An artificial program that analyzes the aforementioned biometric information and generates an optimal rest schedule for each individual user,

[0890] A communication means for notifying the user of recommendations based on the rest schedule generated by the artificial program,

[0891] An environmental data integration means that adjusts the rest schedule and health suggestions while taking into account the user's living environment information,

[0892] A system that includes this.

[0893] (Claim 2)

[0894] The system according to claim 1, wherein the biological information includes circulatory system data, body temperature, and respiratory pattern.

[0895] (Claim 3)

[0896] The system according to claim 1, wherein the communication means uses an information transmission function. [Explanation of Symbols]

[0897] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting biometric information in real time, An artificial intelligence means for analyzing the aforementioned biometric information and generating an optimal sleep plan for each individual user, A means of monitoring the health status of residents in nursing care facilities and notifying caregivers of individually optimized sleep schedules and relaxation activities, A communication means for notifying the user of recommendations based on the sleep plan generated by the artificial intelligence means, A system that includes this.

2. The system according to claim 1, wherein the aforementioned biological information includes heart rate, body temperature, and respiratory pattern.

3. The system according to claim 1, wherein the communication means uses push notifications.

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