System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting sleep data from a user; means for analyzing the collected sleep data; means for generating a personalized recommendation and action plan based on the analysis; and means for providing the generated recommendation and action plan to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, lack of sleep and poor sleep quality have become major problems. Insufficient sleep can negatively impact mental and physical health, potentially leading to serious health problems, especially in the long term. The present invention aims to address these issues, enabling users to get good quality sleep and improve their overall well-being. Specifically, the present invention aims to provide a system that improves sleep quality by collecting and analyzing users' sleep data and providing personalized advice and action plans. [Means for solving the problem]

[0005] The present invention provides a system including the following means: means for collecting sleep data from a user, means for analyzing the collected sleep data, means for generating personalized recommendations and action plans based on the analysis results, and means for providing the generated recommendations and action plans to the user. The system also includes means for generating detailed action plans based on goals set by the user, such as health, stress management, and exercise, and providing the generated action plans to the user. The system further includes means for adjusting the user's daily schedule and supporting efficient time management, and means for notifying the user of the adjusted schedule. This allows the user to ensure good quality sleep and promote healthy lifestyle habits.

[0006] "User" means an individual or organization that uses the system.

[0007] "Sleep data" is data that indicates the user's sleep state, and specifically includes information such as the start time of sleep, the end time, the depth of sleep, and whether or not the user is snoring.

[0008] A "collection means" is a device, software, sensor, or combination thereof for obtaining sleep data from a user.

[0009] An "analyzing means" is an algorithm or software that processes the collected sleep data to evaluate its content and measure the user's sleep performance.

[0010] "Recommendations" means specific advice or suggestions to improve the user's sleep quality.

[0011] An "action plan" is a specific action plan that a user can follow daily to promote good quality sleep and improve their health.

[0012] A "means for providing" is a device, software, application, or interface for informing a user of the generated recommendations and action plans.

[0013] A "goal" is a specific goal set by a user, such as health, stress management, exercise, etc.

[0014] An "action plan" is a detailed execution plan built around the goals set by the user.

[0015] "Schedule adjustment" is a time management technique for optimizing a user's daily schedule.

[0016] A "notification means" is a device, software, application, or interface that alerts or messages a user about adjusted schedules and other information.

[0017] The "System" refers to a set of hardware and software combinations that includes the aforementioned means, and is intended to improve the user's sleep quality and overall well-being. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6]FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The present invention relates to a system for helping users ensure good quality sleep and improve their overall health and well-being. The system collects sleep data from users, analyzes the data, and provides personalized advice and action plans. Specific embodiments of the system are described below.

[0040] System configuration

[0041] This system consists of a smartphone (terminal) used by the user, a server for processing data, and an application for linking these.

[0042] 1. Smartphone (device)

[0043] The device includes hardware and software for collecting the user's sleep data, including the following:

[0044] Accelerometer: Detects the user's movements and records movement data while sleeping.

[0045] Microphone: Records sounds while you sleep (snoring and surrounding noises).

[0046] Application: Collects and transmits sleep data, and displays recommendations and action plans from the server.

[0047] 2. Server

[0048] The server is the central processing unit for receiving and analyzing the collected data. This server includes the following functions:

[0049] Data Analysis: Run algorithms to analyze the collected sleep data.

[0050] Recommendation generation: Generates advice and action plans appropriate for the user based on the analysis results.

[0051] Goal setting support: Generates an action plan based on the health goals set by the user.

[0052] 3. Applications (Software)

[0053] The system application provides an interface for users to operate on their smartphones and collect, send, receive, and display data. Specific functions are as follows:

[0054] Data Collection: Sleep data is collected from the user and sent to the server.

[0055] Recommendation display: Displaying advice and action plans from the server to the user.

[0056] Goal setting: Users can set goals for health, stress management, exercise, etc. and send them to the server.

[0057] Program processing

[0058] Sleep data collection and transmission

[0059] User: At night, launch the smartphone app and set it to sleep mode, which prepares the device to record motion data and sounds while sleeping.

[0060] Device: When the user goes to sleep, the accelerometer and microphone continuously collect data and store it in a buffer.

[0061] Receiving and analyzing data

[0062] Device: The next morning, when the user starts up their smartphone, the collected data is sent to the server.

[0063] Server: Receives the collected data and first verifies the data integrity, then analyzes the data using specific algorithms to calculate metrics such as total sleep time, deep sleep time, light sleep time, and snoring frequency.

[0064] Generate recommendations and action plans

[0065] Server: Based on the analysis results, the server generates recommendations that are unique to each user. These recommendations include specific advice such as "stretch before bed" or "adjust the humidity in your bedroom."

[0066] Server: Generates a detailed action plan based on the goals set by the user. For example, for the goal of "exercising 30 minutes every day", the server suggests the type and timing of exercise.

[0067] User notification and assistance

[0068] Device: Displays the recommendations and action plans received from the server to the user. It also suggests optimal ways to spend time based on the user's daily schedule and sets reminders.

[0069] User: Review and act on the recommendations and action plans provided.

[0070] Specific examples

[0071] At 11 PM, the user launches a smartphone app and sets it to sleep mode. The device collects movement data and sounds throughout the night, and when the user reopens the app at 7 AM the next morning, the data is sent to the server. The server analyzes the total sleep time to be 7 hours, the deep sleep time to be 2 hours and 30 minutes, and the snoring frequency to be 10 times. As a result, the server generates recommendations to the user, such as "stretching before going to bed at night" and "using a humidifier to maintain humidity in the bedroom." The user follows the advice provided to improve their lifestyle habits and gradually achieve their goals.

[0072] This allows users to get better quality sleep and improve their overall health and well-being. The present invention provides a system for achieving such multifaceted health management based on the claims.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] The user opens an app on their smartphone and sets Bedtime mode, then checks the permissions the app needs to use to function properly (such as using the microphone or accelerometer).

[0076] Step 2:

[0077] The device checks the user's Bedtime setting and activates the accelerometer and microphone, preparing to record the user's movements and sounds in real time.

[0078] Step 3:

[0079] The device continuously collects user movement data and environmental sounds while the user sleeps. The accelerometer records the user's movements and movements, and the microphone records snoring and environmental sounds. This data is stored in a buffer at regular intervals (e.g., every second).

[0080] Step 4:

[0081] When the user wakes up the next morning and opens their smartphone, the app will self-check the collected data and prepare it to be sent to the server. The user can then tap the "Send Data" button to send the collected data to the server.

[0082] Step 5:

[0083] The server receives the data sent by the user and checks the data for completeness and consistency. If there are no missing or inconsistent data, it proceeds to the next step.

[0084] Step 6:

[0085] The server runs a data analysis algorithm to analyze the collected sleep data, specifically calculating the following metrics:

[0086] Total sleep time

[0087] Deep sleep time

[0088] Light sleep duration

[0089] The frequency and frequency of snoring

[0090] Step 7:

[0091] The server then generates a report for each user based on the analysis results, which includes the aforementioned metrics and provides an overall assessment of the user's sleep performance.

[0092] Step 8:

[0093] The server uses the generated report to generate specific recommendations and action plans for the user, which, depending on the analysis results, may include advice such as:

[0094] Relaxation techniques to do before bed

[0095] Adjusting the bedroom environment (temperature, humidity, sound)

[0096] Daytime activities (exercise, caffeine intake, etc.)

[0097] Step 9:

[0098] The server receives the user's goal settings and generates a customized action plan based on them. For example, if the goal is "30 minutes of exercise every day," the server will suggest the type of exercise and timing.

[0099] Step 10:

[0100] The device displays the reports, recommendations, and action plans received from the server to the user. The app notifies the user that a new report is available.

[0101] Step 11:

[0102] The user opens the app, reviews the reports and recommendations provided, and plans specific steps to implement the proposed action plan.

[0103] Step 12:

[0104] The device manages the user's daily schedule and sets reminders to help them use their time efficiently. For example, if the user sets a goal of "going to bed at 10 p.m.", the device will display a notification such as "take some time to relax at 9:30 p.m."

[0105] Through these processing steps, users receive specific advice and action plans to improve their sleep quality and build healthy lifestyle habits.

[0106] Example 1

[0107] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0108] In modern society, many people suffer from poor sleep quality due to excessive stress and irregular lifestyles. Furthermore, it is often difficult to obtain specific advice and action plans tailored to individual health conditions, making it difficult to find effective solutions. This invention aims to improve users' sleep quality and overall health by collecting and analyzing their sleep and health data to provide more effective, individually tailored advice and action plans.

[0109] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0110] In this invention, the server includes a device for collecting biometric data from a user, a device for analyzing the collected biometric data, and a device for generating individual advice and action plans based on the analysis results, thereby making it possible to provide optimal advice and action plans to the user.

[0111] "User" refers to an individual who uses this system.

[0112] "Biometric data" is a general term for various data that indicate the user's sleep state and health condition, and specifically includes sleep time, deep sleep time, snoring frequency, and the like.

[0113] "Device" refers to hardware or software for performing a particular function.

[0114] "Analysis" refers to the process of processing collected biometric data and converting it into meaningful information (e.g., indicators of sleep quality or health status).

[0115] "Advice" refers to specific advice based on the analysis results to improve the user's sleep and health.

[0116] An "action plan" refers to a specific implementation plan created based on the user's health status and goals, and includes, for example, daily exercise routines and pre-sleep routines.

[0117] "Providing" refers to the act of informing or displaying advice or a course of action to a user.

[0118] The present invention relates to a system for helping users achieve better quality sleep and improve their overall health and well-being by collecting biometric data from the user, analyzing that data, and providing personalized advice and action plans.

[0119] System configuration

[0120] This system consists of a terminal (smartphone) used by the user, a server for processing data, and an application for linking these.

[0121] 1. Device (smartphone)

[0122] The terminal includes hardware and software for collecting biometric data of the user, specifically the following elements:

[0123] Accelerometer: Detects the user's movements and records movement data while sleeping.

[0124] Microphone: Records sounds while you sleep (snoring and surrounding noises).

[0125] Application: Collects and transmits biometric data and displays advice and action plans from the server.

[0126] 2. Server

[0127] The server is a central processing unit for receiving and analyzing the collected data. The server includes the following functions:

[0128] Data analysis: The system runs algorithms that analyze the collected biometric data, which calculates things like total sleep time, deep sleep time, and snoring frequency.

[0129] Advice generation: Based on the analysis results, appropriate advice and action plans are generated for the user, including specific advice such as "stretch before going to bed" and "adjust the humidity in your bedroom."

[0130] Goal setting support: Generates a detailed action plan based on the health goals set by the user. For example, for a goal of "exercising 30 minutes daily," the system suggests the type and timing of exercise.

[0131] 3. Applications (Software)

[0132] The system application provides an interface for users to operate on their smartphones and collect, send, receive, and display data. Specific functions are as follows:

[0133] Data collection: Biometric data is collected from the user and sent to the server.

[0134] Advice display: displays advice and action plans from the server to the user.

[0135] Goal setting: Users can set goals for health, stress management, exercise, etc. and send them to the server.

[0136] Specific examples

[0137] At 11 PM, the user launches a smartphone app and sets it to sleep mode. The device collects movement data and sounds throughout the night, and when the user reopens the app at 7 AM the next morning, the collected data is sent to the server. The server obtains the analysis results of 7 hours of total sleep, 2.5 hours of deep sleep, and 10 snoring episodes. As a result, the server generates and notifies the user of advice such as "stretching before going to bed at night" and "using a humidifier to maintain humidity in the bedroom." The user follows the advice to improve their lifestyle habits and gradually achieve their goals.

[0138] Prompt Sentence Examples

[0139] Explain the processing procedure of the following system, and write it so that the subject is either the server, the terminal, or the user. Also, add a concrete example.

[0140] (system):

[0141] This system helps users ensure good quality sleep and improve their overall health and well-being. The system collects biometric data from users, analyzes that data, and provides personalized advice and action plans. The system consists of:

[0142] 1. Device (smartphone)

[0143] Acceleration sensor

[0144] microphone

[0145] application

[0146] 2. Server

[0147] Data analysis

[0148] Advice Generation

[0149] Goal setting support

[0150] 3. Applications (Software)

[0151] Data collection

[0152] Advisory Display

[0153] goal setting

[0154] (Example):

[0155] At 11 PM, the user launches a smartphone app and sets it to sleep mode. The device collects movement data and sounds throughout the night, and when the user reopens the app at 7 AM the next morning, the data is sent to the server. The server analyzes the total sleep time to be 7 hours, the deep sleep time to be 2 hours and 30 minutes, and the snoring frequency to be 10 times. As a result, the server generates and notifies the user of advice such as "stretching before going to bed at night" and "using a humidifier to maintain humidity in the bedroom." The user follows the advice provided to improve their lifestyle habits and gradually achieve their goals.

[0156] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0157] Step 1:

[0158] User: At night, the user launches the smartphone app and sets it to sleep mode. At this point, the app activates the accelerometer and microphone, and is ready to record data.

[0159] Input: User operation (launching an app, setting sleep mode)

[0160] Output: The device's sensors and microphone enter data collection mode.

[0161] What it does: When the user taps the app's "Bedtime Mode" button, the app activates the accelerometer and microphone, preparing to start recording data.

[0162] Step 2:

[0163] Device: When the user goes to sleep, the accelerometer records the user's movements and the microphone records sounds (snoring and sounds from the surrounding environment). These data are stored in a buffer at regular intervals.

[0164] Input: User movement, sounds while sleeping

[0165] Output: Buffered motion and audio data

[0166] How it works: The accelerometer detects user movement every minute and records the time and intensity of any movement. The microphone measures the decibels of sound every second and records an event if the decibel level exceeds a certain level.

[0167] Step 3:

[0168] Device: The next morning, when the user turns on their smartphone and opens the app, all the data collected overnight is sent to the server.

[0169] Input: Sleep motion and voice data collected overnight

[0170] Output: Data sent to the server

[0171] Specific operation: When the user taps the "Send Data" button in the app, the app will upload the data to the server via an Internet connection.

[0172] Step 4:

[0173] Server: Receives data from the device and first checks the data integrity. It checks the data volume and whether there are any missing data, and issues an alert if there is a shortage.

[0174] Input: Data sent from the terminal

[0175] Output: Data integrity check results, alerts if there are any missing data

[0176] What happens: The server checks the format and size of the data to make sure there is no invalid data mixed in. If there is a problem, it generates an error log.

[0177] Step 5:

[0178] Server: Analyzes the received data using batch processing, identifies each stage of sleep (deep sleep, light sleep, REM sleep, etc.), and calculates total sleep time, deep sleep time, snoring frequency, etc.

[0179] Input: Integrity-checked biometric data

[0180] Output: Analysis results (total sleep time, deep sleep time, snoring frequency, etc.)

[0181] How it works: The analysis algorithm analyzes the data over time and calculates each indicator. The results are stored in a database.

[0182] Step 6:

[0183] Server: Based on the analysis results, it generates personalized advice. For example, if deep sleep time is short, it recommends stretching.

[0184] Input: Analysis results

[0185] Output: personalized advice

[0186] Specific operation: The advice generation algorithm selects the most appropriate advice based on the analysis results and creates an advice list.

[0187] Step 7:

[0188] Server: Generates a detailed action plan based on the health goals set by the user, suggesting daily activities such as stretching and meditation.

[0189] Input: User's health goals and analysis results

[0190] Output: Detailed action plan

[0191] Specific operation: The goal management module refers to the user's goals, and the plan generation algorithm creates an individual action plan.

[0192] Step 8:

[0193] Terminal: Receives advice and action plans from the server and notifies the user using alarms and pop-up messages.

[0194] Input: Advice and action plan sent from the server

[0195] Output: User notification

[0196] What it does: The app receives a push notification, displays it as a pop-up on the screen, and notifies you by adding a reminder to your calendar.

[0197] Step 9:

[0198] User: Review the provided advice and action plan and act as instructed. You can also record your action history in the app.

[0199] Input: Notification content from the terminal (advice and action plan)

[0200] Output: Record of user actions and action history

[0201] Specific operation: The user taps the "Action Completed" button to record the day's achievements in the app, allowing the server to monitor progress.

[0202] (Application example 1)

[0203] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0204] In modern society, many people suffer from poor sleep quality and irregular lifestyles. This leads to issues such as a deterioration in health and a lower quality of life. In addition, there is a lack of appropriate meal plans based on sleep and health status, making it difficult to consume meals tailored to individual health conditions. To solve these issues, a system that utilizes sleep data to achieve comprehensive health management is needed.

[0205] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0206] In this invention, the server includes means for collecting sleep data from a user, means for analyzing the collected sleep data, means for generating individual recommendations and action plans based on the analysis results, means for providing the generated recommendations and action plans to the user, means for proposing a meal plan suitable for the user based on the analysis results, and means for providing foods and dishes according to the proposed meal plan, thereby enabling health management based on sleep data and provision of meal plans linked thereto.

[0207] "Sleep data" refers to general information related to the user's sleep, and specifically includes data such as total sleep time, deep sleep time, and frequency of snoring.

[0208] "Analysis results" refer to conclusions or information about the user's sleep quality or health condition obtained by analyzing the collected sleep data.

[0209] "Recommendations" are specific advice or improvement measures provided to users based on the analysis results.

[0210] An "action plan" is a specific action or plan that a user should take based on a recommendation.

[0211] A "meal plan" is a nutritionally balanced and appropriate meal plan designed based on the user's health status.

[0212] "Means for providing food or meals" refers to a method or system that physically delivers food or meals appropriate to the user according to the proposed meal plan.

[0213] "Health goal" refers to a health goal that a user wishes to achieve, such as weight management, stress reduction, or establishing an exercise habit.

[0214] The "daily schedule" refers to the user's daily plans and timetable, and by adjusting this, guidance is given on how to use time efficiently.

[0215] The present invention provides a system for helping users achieve better quality sleep and improve their overall health and well-being. The system collects sleep data from users, analyzes the data, and provides personalized advice, action plans, and even appropriate diet plans. Specific embodiments of the system are described below.

[0216] System configuration

[0217] This system consists of a terminal used by a user, a server for processing data, and an application for linking these.

[0218] Terminal

[0219] The device includes hardware and software for collecting the user's sleep data, including the following:

[0220] Accelerometer: Detects the user's movements and records movement data while sleeping.

[0221] Microphone: Records sounds while you sleep (snoring and surrounding noises).

[0222] Application: Collects and transmits sleep data, and displays recommendations, action plans, and meal plans from the server.

[0223] server

[0224] The server is the central processing unit for receiving and analyzing the collected data. This server includes the following functions:

[0225] Data Analysis: Run an algorithm to analyze the collected sleep data, for example using Python.

[0226] Recommendation generation: Generates advice and action plans appropriate for the user based on the analysis results.

[0227] Meal plan generation: Proposes a nutritionally balanced meal plan based on the user's health status.

[0228] Delivery Order: A means of delivering food and dishes according to a proposed meal plan.

[0229] Application (software)

[0230] The system application provides an interface for users to operate the terminal and collect, send, receive, and display data. The specific functions are as follows:

[0231] Data Collection: Sleep data is collected from the user and sent to the server.

[0232] Recommendation display: Advice, action plans, and meal plans from the server are displayed to the user.

[0233] Goal setting: Users can set goals for health, stress management, exercise, etc. and send them to the server.

[0234] Delivery Order Management: Use delivery services based on proposed meal plans.

[0235] Program processing

[0236] Data collection and transmission

[0237] The user launches the app on their device at night and activates sleep mode. This allows the device to collect sleep data using the accelerometer and microphone, and store it in a buffer. When the user reopens the app the next morning, the collected data is sent to the server.

[0238] Receiving and analyzing data

[0239] The server receives the collected data, first verifies its integrity, and then analyzes it using specific algorithms to calculate metrics such as total sleep time, deep sleep time, and snoring frequency.

[0240] Generate recommendations and meal plans

[0241] The server generates personalized recommendations based on the analysis results, and also suggests nutritionally balanced meal plans based on the user's health status, for example, suggesting a high-protein breakfast to a sleep-deprived user.

[0242] Fulfilling delivery orders

[0243] The application automatically places food and meal delivery orders based on the generated meal plan, allowing the user to receive the food and meals according to the proposed meal plan.

[0244] Examples of concrete examples and prompts

[0245] As a specific example, a user launches an app on their device at 11 PM and sets it to sleep mode. The device collects movement data and sounds throughout the night, and when the user reopens the app at 7 AM the next morning, the data is sent to the server. The server analyzes the total sleep time to be 7 hours, the deep sleep time to be 2 hours and 30 minutes, and the snoring frequency to be 10 times. As a result, it generates recommendations to the user, such as "stretching before going to bed at night" and "using a humidifier to maintain humidity in the bedroom." In addition, based on the user's health condition, it recommends a "high-protein breakfast," and appropriate food is delivered.

[0246] Example prompt sentence:

[0247] The user's sleep data for the past 7 days is as follows: Total sleep time: 6 hours on average, Deep sleep time: 2 hours on average, Snoring frequency: 15 times on average. Based on this, provide appropriate diet and lifestyle recommendations to maintain good health.

[0248] This not only allows users to get quality sleep, but also allows them to eat a nutritionally balanced diet that is appropriate for their health condition.

[0249] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0250] Step 1:

[0251] Bedtime mode settings

[0252] At night, the user launches the app on their device and sets it to sleep mode, which prepares the device to start collecting motion data and sounds.

[0253] Input: User launches app and sets Bedtime mode

[0254] Output: The device switches to data collection mode

[0255] Step 2:

[0256] Data collection

[0257] The device's accelerometer and microphone continuously collect motion data and sounds (such as snoring and ambient noise) and store them in a buffer.

[0258] Input: User motion data and sound

[0259] Output: Buffered sleep data (motion data and sound)

[0260] Step 3:

[0261] Sending data

[0262] The next morning, when the user reopens the app, the saved sleep data is sent to the server.

[0263] Input: Buffered sleep data

[0264] Output: Sleep data sent to the server

[0265] Step 4:

[0266] Data Receipt and Validation

[0267] The server checks the integrity of the received data, verifying that there are no missing data or outliers.

[0268] Input: Sleep data sent to the server

[0269] Output: Verified sleep data

[0270] Step 5:

[0271] Data analysis

[0272] The server analyzes the verified data using a specific algorithm, calculating indicators such as total sleep time, deep sleep time, and snoring frequency to evaluate the user's sleep quality.

[0273] Input: Verified sleep data

[0274] Output: Analysis results (total sleep time, deep sleep time, snoring frequency, etc.)

[0275] Step 6:

[0276] Generating recommendations

[0277] Based on the analysis results, the server generates personalized recommendations and action plans, such as "avoid caffeine late at night" or "adjust the humidity in your bedroom."

[0278] Input: Analysis results

[0279] Output: personalized recommendations and action plans

[0280] Step 7:

[0281] Generate a meal plan

[0282] Based on the analysis results, the server proposes a suitable meal plan for the user, for example, recommending a high-protein breakfast for a sleep-deprived user.

[0283] Input: Analysis results

[0284] Output: Suitable meal plan

[0285] Step 8:

[0286] View recommendations and meal plans

[0287] The terminal displays the recommendations and meal plans received from the server to the user.

[0288] Input: Recommendations and Meal Plans

[0289] Output: Recommendations and meal plans displayed on the device screen

[0290] Step 9:

[0291] Fulfilling delivery orders

[0292] Once the user agrees to the meal plan, the device automatically sends a delivery order to the server, and the food or meal is delivered to the user.

[0293] Input: User consent

[0294] Output: Sending delivery orders and delivering food and dishes

[0295] Through these steps, users can ensure they get good quality sleep and eat a diet that is optimal for their health.

[0296] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0297] The present invention provides a system for ensuring a user's quality sleep and improving their overall health and well-being. The system collects and analyzes sleep and emotional data from the user, and provides personalized advice and action plans based on the collected data. Specific embodiments are described in detail below.

[0298] System configuration

[0299] This system consists of the following elements:

[0300] 1. Smartphone (device)

[0301] The terminal includes hardware and software for collecting the user's sleep and emotion data.

[0302] Acceleration sensor: Detects the user's movements and records movement data while sleeping.

[0303] Microphone: Records sounds while you sleep (snoring and surrounding noises).

[0304] Camera: Collects user emotion data through facial expression analysis.

[0305] Application: Collects and transmits sleep and emotional data, and displays recommendations and action plans from the server.

[0306] 2. Server

[0307] The server is a central processing unit for receiving and analyzing the collected data.

[0308] Data Analysis: Running algorithms to process sleep and emotion data.

[0309] Recommendation generation: Generates advice and action plans appropriate for the user based on the analysis results.

[0310] Emotion Engine: Recognizes user emotions and performs emotion-based analysis and suggestions.

[0311] 3. Applications (Software)

[0312] It provides an interface on a smartphone operated by the user, and collects, sends, receives, and displays data.

[0313] Data collection: Sleep data and emotion data are collected from the user and sent to the server.

[0314] Recommendation display: Displaying advice and action plans from the server to the user.

[0315] Goal setting: Users can set goals for health, stress management, exercise, etc. and send them to the server.

[0316] Program processing

[0317] Collection and transmission of sleep and emotional data

[0318] User: The smartphone app is started at night, sleep mode is set, facial expression analysis function is enabled, and the emotion engine is running.

[0319] Device: The acceleration sensor and microphone are activated to continuously collect data while the user is sleeping. The camera analyzes the user's facial expressions and collects emotional data.

[0320] Receiving and analyzing data

[0321] On the device: The next morning, when the user wakes up and reopens the app, the collected data is sent to the server.

[0322] Server: Receives the collected data and first verifies the data integrity. Then, it analyzes the data using algorithms to calculate metrics such as total sleep time, deep sleep time, light sleep time, and snoring frequency. In addition, it analyzes the user's emotional data using an emotion engine.

[0323] Generate recommendations and action plans

[0324] Server: Based on the analysis results, it generates different recommendations for each user. For example, if a user snores a lot, it recommends using a humidifier to maintain appropriate humidity in the bedroom. Based on emotional data, if the user is under a lot of stress, it suggests meditating to relax before going to bed at night.

[0325] Server: Generates a detailed action plan based on the goals set by the user. For example, if the goal is to exercise 30 minutes a day, the server will suggest specific exercise content and timing.

[0326] User notification and assistance

[0327] Device: Displays the recommendations and action plans received from the server to the user. It also suggests optimal time management based on the user's daily schedule and sets reminders.

[0328] User: Opens the app, reviews the recommendations and action plans provided, and acts on them.

[0329] Specific examples

[0330] The user enables the emotion engine and sets the bedtime mode at 10 p.m. The device collects motion, sound, and emotion data throughout the night, and when the user reopens the app at 7 a.m. the next morning, the data is sent to the server. The server analyzes the data and determines that the total sleep time is 7 hours, the deep sleep time is 2 hours and 30 minutes, the snoring frequency is 10 times, and the stress level is high. As a result, the server generates and notifies the user of the recommendation to "meditate before bed and use a humidifier to maintain humidity in the bedroom." The user follows the suggested advice, improving their sleep quality and promoting their overall health.

[0331] This allows users to combine emotional data to receive more accurate advice and action plans to build healthy lifestyle habits.The present invention provides a comprehensive system for supporting users' sleep and emotional management based on the claims.

[0332] The processing flow will be explained below.

[0333] Step 1:

[0334] A user opens a smartphone app, sets the sleep mode, enables the facial expression analysis function, and runs the emotion engine. At this time, the app checks for necessary permissions (such as access to the microphone, accelerometer, and camera).

[0335] Step 2:

[0336] The device checks the user's Bedtime setting and activates the accelerometer, microphone, and camera, ready to record the user's movements, sounds, and facial expressions in real time.

[0337] Step 3:

[0338] The device continuously collects motion and sound data from the user while they sleep. The acceleration sensor records the user's movements and movements, and the microphone records snoring and environmental sounds. The camera also records the user's facial expressions, which the emotion engine analyzes to generate emotion data.

[0339] Step 4:

[0340] The data collected by the terminal is stored in a buffer at regular intervals (for example, every second), which ensures data consistency and integrity.

[0341] Step 5:

[0342] When the user wakes up the next morning and opens their smartphone, the app prepares to send the data collected during the night to the server. The user taps the "Send Data" button to send the collected data to the server.

[0343] Step 6:

[0344] The server receives the data sent by the user and checks the data for completeness and consistency. If there are any missing or inconsistent data, it notifies the user and asks them to resubmit.

[0345] Step 7:

[0346] The server runs a data analysis algorithm and emotion engine to analyze the collected sleep and emotion data, and calculates the following metrics:

[0347] Total sleep time

[0348] Deep sleep time

[0349] Light sleep duration

[0350] The frequency and frequency of snoring

[0351] Emotional fluctuation patterns

[0352] Step 8:

[0353] The server then generates a report for each user based on the analysis results, which includes the aforementioned metrics and provides an overall assessment of the user's sleep performance.

[0354] Step 9:

[0355] Based on the generated report, the server generates specific recommendations and action plans suited to the user. For example, if the user snores a lot, it may suggest using a humidifier to maintain appropriate humidity, or if the user is under high stress based on emotional data, it may advise them to practice meditation to relax before going to bed at night.

[0356] Step 10:

[0357] The server receives the user's goal setting and generates a customized action plan accordingly. For example, if the goal is set to "exercise 30 minutes every day," the server will suggest the type and timing of exercise.

[0358] Step 11:

[0359] The device displays the reports, recommendations, and action plans received from the server to the user, and also sets reminders based on daily schedules to help users use their time efficiently.

[0360] Step 12:

[0361] The user opens the app, reviews the reports and recommendations provided, and plans specific steps to implement the proposed action plan and incorporate it into their daily activities.

[0362] Step 13:

[0363] The device periodically records the user's daily progress and emotional data and sends it to the server, which can then use the latest data to provide further recommendations and refine the action plan.

[0364] In this way, the system of the present invention collects and analyzes the user's sleep and emotional data, and provides personalized advice and action plans to improve the user's sleep quality and overall health. By implementing specific processing steps, the user can effectively manage their health.

[0365] Example 2

[0366] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0367] Conventional sleep management systems only collect and analyze users' sleep data and are unable to provide comprehensive recommendations that take into account the user's emotional state. As a result, users are unable to receive support in both appropriate sleep advice and emotional management, resulting in insufficient benefits for improving their overall health and well-being.

[0368] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0369] In this invention, the server includes means for collecting sleep data and emotional data from a user, means for verifying the completeness of the collected data, means for analyzing the data for total sleep time, deep sleep time, light sleep time, snoring frequency, and emotional state, means for generating individual recommendations and action plans based on the analysis results, and means for providing the generated recommendations and action plans to the user and setting reminders. This makes it possible to comprehensively analyze the user's sleep data and emotional data and provide optimal recommendations tailored to individual needs.

[0370] "User" refers to an individual who utilizes the system to provide sleep and emotional data.

[0371] "Sleep data" refers to information such as movement data, total sleep time, deep sleep time, light sleep time, and snoring frequency collected while the user is sleeping.

[0372] "Emotion data" refers to data on the user's emotional state obtained by analyzing their facial expressions or using an emotion engine.

[0373] "Means of collection" refers to the acceleration sensor, microphone, camera, and software that controls these devices installed on the user's device.

[0374] "Data Integrity Verification Measures" refers to the processes and algorithms used to verify that data received by the server is complete, free of errors and omissions.

[0375] "Means for analyzing" refers to algorithms or software for analyzing the collected sleep data and emotional data and assessing total sleep time, deep sleep time, light sleep time, snoring frequency, and emotional state.

[0376] "Recommendations" refers to specific lifestyle and behavioral advice provided to users based on the analysis results.

[0377] An "action plan" refers to a plan that includes specific actions and schedules that a user will take.

[0378] "Reminder" refers to a function that notifies users at the appropriate time to execute action plans or recommendations set by the user.

[0379] The present invention is a system for ensuring users' quality sleep and improving their overall health and well-being. The system collects and analyzes sleep and emotional data from users, and provides personalized advice and action plans based on the collected data.

[0380] System configuration

[0381] This system consists of the following elements:

[0382] 1. Smartphone (device)

[0383] The terminal includes hardware and software for collecting the user's sleep and emotion data.

[0384] Acceleration sensor: Detects the user's movements and records movement data while sleeping.

[0385] Microphone: Records sounds while you sleep (snoring and surrounding noises).

[0386] Camera: Collects user emotion data through facial expression analysis.

[0387] Application: Collects and transmits sleep and emotional data, and displays recommendations and action plans from the server.

[0388] 2. Server

[0389] The server is the central processing unit that receives and analyzes the collected data and provides specific recommendations to the user.

[0390] Data Analysis: Running algorithms to process sleep and emotion data.

[0391] Recommendation generation: Generates advice and action plans appropriate for the user based on the analysis results.

[0392] Emotion Engine: Recognizes user emotions and performs emotion-based analysis and suggestions.

[0393] 3. Applications (Software)

[0394] It provides an interface on the smartphone operated by the user, and collects, sends, receives, and displays data.

[0395] Data collection: Sleep data and emotion data are collected from the user and sent to the server.

[0396] Recommendation display: Displaying advice and action plans from the server to the user.

[0397] Goal setting: Users can set goals for health, stress management, exercise, etc. and send them to the server.

[0398] Specific examples

[0399] The user enables the emotion engine and sets the sleep mode at 10 p.m. The device collects motion, sound, and emotion data throughout the night, and when the user reopens the app at 7 a.m. the next morning, the data is sent to the server. The server verifies the completeness of the data and analyzes it. For example, the server determines that the total sleep time is 7 hours, the deep sleep time is 2 hours and 30 minutes, the snoring frequency is 10 times, and the stress level is high. As a result, the server generates and notifies the user of the recommendation to "meditate before bed and use a humidifier to maintain humidity in the bedroom." The user follows the suggested advice, improving their sleep quality and promoting their overall health.

[0400] Prompt Sentence Examples

[0401] "The user enables the emotion engine and sets bedtime mode at 10 p.m. The device collects motion data, sound data, and emotion data throughout the night. When the user reopens the app at 7 a.m. the next morning, the data is sent to the server. The server analyzes the data and determines that the total sleep time is 7 hours, the deep sleep time is 2 hours and 30 minutes, the snoring frequency is 10 times, and the stress level is high. As a result, the server generates a recommendation to 'meditate before going to bed at night and use a humidifier to maintain humidity in the bedroom' and notifies the user."

[0402] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0403] Step 1:

[0404] User sets Bedtime mode

[0405] Input: User input operations

[0406] How it works: A user sets up bedtime mode on a smartphone app and enables the emotion engine and facial expression analysis functions. Specifically, they tap "Bedtime mode" on the app's settings screen and turn on the emotion engine and facial expression analysis.

[0407] Output: The device is ready to collect data.

[0408] Step 2:

[0409] The device collects data

[0410] Input: User-defined bedtime mode

[0411] Operation: The device activates the accelerometer, microphone, and camera. The accelerometer continuously records the user's movements, and the microphone collects sounds in the bedroom. The camera analyzes the user's facial expressions and collects emotional data. This data is stored in temporary storage on the device.

[0412] Output: Collected sleep and emotion data

[0413] Step 3:

[0414] The device sends the data to the server

[0415] Input: Collected sleep and emotion data

[0416] How it works: The next morning, when the user reopens the smartphone app, data transmission begins. The device sends the data stored in its internal storage to the server. This transmission is encrypted for security reasons.

[0417] Output: Data sent to the server

[0418] Step 4:

[0419] The server verifies the integrity of the data

[0420] Input: Data sent from the terminal

[0421] Behavior: The server verifies the integrity of the data it receives, checking for missing or corrupted data and requesting retransmissions if necessary.

[0422] Output: Data with integrity checked

[0423] Step 5:

[0424] The server analyzes the data

[0425] Input: Integrity checked data

[0426] How it works: The server applies algorithms to analyze the data. First, it calculates sleep metrics such as total sleep time, deep sleep time, light sleep time, and snoring frequency. Second, it uses an emotion engine to analyze the user's emotional state.

[0427] Output: Analysis results of sleep data and emotion data

[0428] Step 6:

[0429] The server generates recommendations and action plans

[0430] Input: Analysis results

[0431] How it works: The server generates personalized recommendations for each user based on the analysis results. For example, if the deep sleep time is short, it may suggest "We recommend meditating every night before going to bed." If the user snores a lot, it may recommend "Using a humidifier to adjust the humidity in the bedroom." It also generates a detailed action plan based on the goals set by the user.

[0432] Output: Generated recommendations and action plans

[0433] Step 7:

[0434] The device notifies the user

[0435] Input: Generated recommendations and action plans

[0436] How it works: The device will notify you of the recommendations and action plans it receives from the server. The notification will include specific advice and instructions on how to implement it. Detailed instructions will also be provided within the app.

[0437] Output: User notification

[0438] Step 8:

[0439] The user executes the advice

[0440] Input: Notifications from your device

[0441] Action: The user sees the notification, opens the app to see the provided recommendation and action plan, and follows through on the specific advice offered (e.g., meditating or using a humidifier).

[0442] Output: Improved user health and sleep quality

[0443] (Application example 2)

[0444] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0445] In traditional food delivery services, the sleep and emotional state of delivery staff often have a significant impact on the quality of service. However, currently, there is no system that effectively utilizes this data to optimize staff health and performance. Furthermore, the lack of work shift and rest plans based on sleep and emotional data makes it difficult for delivery staff to manage stress and perform their work efficiently. Therefore, there is a need for a system that collects and analyzes sleep and emotional data and provides appropriate recommendations and action plans based on this data to improve staff health and service quality.

[0446] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting sleep data and emotion data from the user, means for analyzing the collected sleep data and emotion data, and means for generating individual recommendations and action plans based on the analysis results. This makes it possible to generate work shift and rest plans that optimize staff performance based on the collected data. The server also includes means for providing the generated recommendations and action plans to the user, means for generating work shift and rest plans to optimize the user's performance, and means for notifying the user of the generated work shift and rest plans. This solves the problem of improving the health and service quality of delivery staff.

[0447] "User" refers to a person who uses the system to provide data and receive recommendations and action plans.

[0448] "Sleep data" refers to data that includes information about a user's sleep patterns, sleep duration, deep sleep duration, light sleep duration, and other sleep-related information.

[0449] "Emotion data" is data that represents the user's emotional state, and includes information such as stress level and happiness level collected by facial expression analysis.

[0450] "Recommendations" refer to advice or specific instructions for action provided to users based on analysis results.

[0451] An "action plan" is a plan outlining specific actions or steps a user should take to optimize their health or performance.

[0452] A "work shift" refers to the allocation of working hours for the job a user is engaged in, and includes break times, start times, end times, etc.

[0453] A "rest plan" is a plan that includes a rest schedule and specific rest methods designed to optimize a user's rest.

[0454] "Data collection means" refers to the sensors, cameras, microphones and associated software used to acquire the user's sleep and emotional data.

[0455] "Data Analysis Means" refers to the algorithms and analytical software used to process the collected sleep and emotional data and assess the user's condition.

[0456] "Notification vehicles" are digital interfaces or applications used to communicate recommendations, action plans, work shifts, and break plans to users.

[0457] This system collects and analyzes sleep and emotional data from delivery staff, and provides work shift and rest plans and personalized advice based on the results. This system is designed to optimize users' health and work performance.

[0458] System configuration

[0459] This system consists of the following elements:

[0460] 1. Terminal

[0461] The terminal includes hardware and software for collecting sleep and emotion data of delivery staff.

[0462] Acceleration sensor: Detects staff movement and records movement data while sleeping.

[0463] Microphone: Records sounds while you sleep (snoring and surrounding noises).

[0464] Camera: Collects staff emotional data through facial expression analysis.

[0465] Application: Collects and transmits sleep and emotional data, and displays recommendations and action plans from the server.

[0466] 2. Server

[0467] The server is a central processing unit for receiving and analyzing the collected data.

[0468] Data Analysis: Running algorithms to process sleep and emotion data.

[0469] Recommendation generation: Generate appropriate advice and action plans for staff based on the analysis results.

[0470] Work shift and break plan generation: Propose optimal work shift and break plans to staff.

[0471] 3. Applications (Software)

[0472] It provides an interface on a smartphone operated by the user, and collects, sends, receives, and displays data.

[0473] Data collection: Sleep data and emotion data are collected from staff and sent to the server.

[0474] Recommendation display: Display advice and action plans from the server to staff.

[0475] Goal setting: Staff can set goals for health, stress management, exercise, etc. and send them to the server.

[0476] Program processing

[0477] Data collection

[0478] The device uses an accelerometer and microphone to collect motion data and sounds from staff, and a camera to record emotional data, which is then sent to a server via an application.

[0479] Data analysis and recommendation generation

[0480] The server analyzes the collected data, assesses sleep patterns and emotional states, and generates personalized recommendations and specific action plans for staff, along with optimal work shift and break plans.

[0481] Specific examples

[0482] A delivery staff member launches the app at 10 p.m. and sets it to sleep mode. After a week, the app checks the collected data and finds that the average sleep time was 6.5 hours and that there were three days of high stress. The server generates advice to the staff member, such as "Get more sleep" and "Try to spend more time relaxing," and notifies them. It also suggests adjusting work shifts and adding breaks.

[0483] Prompt Sentence Examples

[0484] It is recommended that staff meditate before going to bed and use a humidifier to maintain humidity in the bedroom. We will propose appropriate shift and rest plans based on staff sleep and emotional data. Please advise us on what approach would be effective.

[0485] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0486] Step 1:

[0487] The terminal collects sleep data and emotion data from the user (delivery staff).

[0488] Input: Accelerometer, microphone, camera

[0489] Output: Sleep data (movement data, sound data such as snoring), emotion data (facial expression analysis results)

[0490] Specific operation: When the user goes to bed at night, the device is turned on and set to sleep mode. The device's acceleration sensor detects movement data, the microphone records sounds such as snoring, and the camera analyzes facial expressions to collect emotional data.

[0491] Step 2:

[0492] The terminal transmits the collected sleep data and emotion data to a server.

[0493] Input: Data collected in step 1

[0494] Output: Raw data sent to the server

[0495] Specific operation: When the user wakes up the next morning, the device automatically uploads the data to the server.

[0496] Step 3:

[0497] A server receives the collected sleep and emotion data and checks the data for completeness.

[0498] Input: Sleep data and emotion data sent from the device

[0499] Output: Complete dataset, checks for missing data and outliers

[0500] Specific operation: Data is stored in a database within the server, and an analysis program checks the integrity of the data.

[0501] Step 4:

[0502] The server performs data analysis based on the data whose integrity has been confirmed.

[0503] Input: Complete sleep and emotion data

[0504] Output: Analysis results (total sleep time, deep sleep time, light sleep time, snoring frequency, emotional state, etc.)

[0505] How it works: The analysis algorithm works to calculate multiple indicators (sleep patterns, emotional trends, etc.).

[0506] Step 5:

[0507] The server generates personalized recommendations and action plans based on the analysis results.

[0508] Input: Data analysis results

[0509] Output: Recommendations and action plans

[0510] Specific Actions: Generative AI models are used to generate appropriate suggestions, creating specific advice (e.g., "Get more sleep" or "Meditate to relax") and action plans.

[0511] Step 6:

[0512] The server transmits the generated recommendations and action plans to the terminal.

[0513] Input: Generated recommendations and action plans

[0514] Output: Suggestions sent to the device

[0515] Specific operation: The communication module operates and the generated proposal is pushed to the device.

[0516] Step 7:

[0517] The terminal presents the recommendations and action plans received from the server to the user.

[0518] Input: Recommendations and action plans sent by the server

[0519] Output: Advice and plan displayed to the user

[0520] Specific operation: The terminal application displays the received data, and the user confirms it.

[0521] Step 8:

[0522] The user acts on the presented recommendations and action plan.

[0523] Input: Recommendations and action plans displayed on the device

[0524] Output: Action taken (adjustment of break time, change of work shift, etc.)

[0525] Specific actions: The user follows the application's instructions to adjust their daily activities.

[0526] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0527] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0528] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0529] [Second embodiment]

[0530] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0531] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0532] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0533] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0534] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0535] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0536] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0537] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0538] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0539] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0540] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0541] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0542] The present invention relates to a system for helping users ensure good quality sleep and improve their overall health and well-being. The system collects sleep data from users, analyzes the data, and provides personalized advice and action plans. Specific embodiments of the system are described below.

[0543] System configuration

[0544] This system consists of a smartphone (terminal) used by the user, a server for processing data, and an application for linking these.

[0545] 1. Smartphone (device)

[0546] The device includes hardware and software for collecting the user's sleep data, including the following:

[0547] Accelerometer: Detects the user's movements and records movement data while sleeping.

[0548] Microphone: Records sounds while you sleep (snoring and surrounding noises).

[0549] Application: Collects and transmits sleep data, and displays recommendations and action plans from the server.

[0550] 2. Server

[0551] The server is the central processing unit for receiving and analyzing the collected data. This server includes the following functions:

[0552] Data Analysis: Run algorithms to analyze the collected sleep data.

[0553] Recommendation generation: Generates advice and action plans appropriate for the user based on the analysis results.

[0554] Goal setting support: Generates an action plan based on the health goals set by the user.

[0555] 3. Applications (Software)

[0556] The system application provides an interface for users to operate on their smartphones and collect, send, receive, and display data. Specific functions are as follows:

[0557] Data Collection: Sleep data is collected from the user and sent to the server.

[0558] Recommendation display: Displaying advice and action plans from the server to the user.

[0559] Goal setting: Users can set goals for health, stress management, exercise, etc. and send them to the server.

[0560] Program processing

[0561] Sleep data collection and transmission

[0562] User: At night, launch the smartphone app and set it to sleep mode, which prepares the device to record motion data and sounds while sleeping.

[0563] Device: When the user goes to sleep, the accelerometer and microphone continuously collect data and store it in a buffer.

[0564] Receiving and analyzing data

[0565] Device: The next morning, when the user starts up their smartphone, the collected data is sent to the server.

[0566] Server: Receives the collected data and first verifies the data integrity, then analyzes the data using specific algorithms to calculate metrics such as total sleep time, deep sleep time, light sleep time, and snoring frequency.

[0567] Generate recommendations and action plans

[0568] Server: Based on the analysis results, the server generates recommendations that are unique to each user. These recommendations include specific advice such as "stretch before bed" or "adjust the humidity in your bedroom."

[0569] Server: Generates a detailed action plan based on the goals set by the user. For example, for the goal of "exercising 30 minutes every day", the server suggests the type and timing of exercise.

[0570] User notification and assistance

[0571] Device: Displays the recommendations and action plans received from the server to the user. It also suggests optimal ways to spend time based on the user's daily schedule and sets reminders.

[0572] User: Review and act on the recommendations and action plans provided.

[0573] Specific examples

[0574] At 11 PM, the user launches a smartphone app and sets it to sleep mode. The device collects movement data and sounds throughout the night, and when the user reopens the app at 7 AM the next morning, the data is sent to the server. The server analyzes the total sleep time to be 7 hours, the deep sleep time to be 2 hours and 30 minutes, and the snoring frequency to be 10 times. As a result, the server generates recommendations to the user, such as "stretching before going to bed at night" and "using a humidifier to maintain humidity in the bedroom." The user follows the advice provided to improve their lifestyle habits and gradually achieve their goals.

[0575] This allows users to get better quality sleep and improve their overall health and well-being. The present invention provides a system for achieving such multifaceted health management based on the claims.

[0576] The processing flow will be explained below.

[0577] Step 1:

[0578] The user opens an app on their smartphone and sets Bedtime mode, then checks the permissions the app needs to use to function properly (such as using the microphone or accelerometer).

[0579] Step 2:

[0580] The device checks the user's Bedtime setting and activates the accelerometer and microphone, preparing to record the user's movements and sounds in real time.

[0581] Step 3:

[0582] The device continuously collects user movement data and environmental sounds while the user sleeps. The accelerometer records the user's movements and movements, and the microphone records snoring and environmental sounds. This data is stored in a buffer at regular intervals (e.g., every second).

[0583] Step 4:

[0584] When the user wakes up the next morning and opens their smartphone, the app will self-check the collected data and prepare it to be sent to the server. The user can then tap the "Send Data" button to send the collected data to the server.

[0585] Step 5:

[0586] The server receives the data sent by the user and checks the data for completeness and consistency. If there are no missing or inconsistent data, it proceeds to the next step.

[0587] Step 6:

[0588] The server runs a data analysis algorithm to analyze the collected sleep data, specifically calculating the following metrics:

[0589] Total sleep time

[0590] Deep sleep time

[0591] Light sleep duration

[0592] The frequency and frequency of snoring

[0593] Step 7:

[0594] The server then generates a report for each user based on the analysis results, which includes the aforementioned metrics and provides an overall assessment of the user's sleep performance.

[0595] Step 8:

[0596] The server uses the generated report to generate specific recommendations and action plans for the user, which, depending on the analysis results, may include advice such as:

[0597] Relaxation techniques to do before bed

[0598] Adjusting the bedroom environment (temperature, humidity, sound)

[0599] Daytime activities (exercise, caffeine intake, etc.)

[0600] Step 9:

[0601] The server receives the user's goal settings and generates a customized action plan based on them. For example, if the goal is "30 minutes of exercise every day," the server will suggest the type of exercise and timing.

[0602] Step 10:

[0603] The device displays the reports, recommendations, and action plans received from the server to the user. The app notifies the user that a new report is available.

[0604] Step 11:

[0605] The user opens the app, reviews the reports and recommendations provided, and plans specific steps to implement the proposed action plan.

[0606] Step 12:

[0607] The device manages the user's daily schedule and sets reminders to help them use their time efficiently. For example, if the user sets a goal of "going to bed at 10 p.m.", the device will display a notification such as "take some time to relax at 9:30 p.m."

[0608] Through these processing steps, users receive specific advice and action plans to improve their sleep quality and build healthy lifestyle habits.

[0609] Example 1

[0610] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0611] In modern society, many people suffer from poor sleep quality due to excessive stress and irregular lifestyles. Furthermore, it is often difficult to obtain specific advice and action plans tailored to individual health conditions, making it difficult to find effective solutions. This invention aims to improve users' sleep quality and overall health by collecting and analyzing their sleep and health data to provide more effective, individually tailored advice and action plans.

[0612] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0613] In this invention, the server includes a device for collecting biometric data from a user, a device for analyzing the collected biometric data, and a device for generating individual advice and action plans based on the analysis results, thereby making it possible to provide optimal advice and action plans to the user.

[0614] "User" refers to an individual who uses this system.

[0615] "Biometric data" is a general term for various data that indicate the user's sleep state and health condition, and specifically includes sleep time, deep sleep time, snoring frequency, and the like.

[0616] "Device" refers to hardware or software for performing a particular function.

[0617] "Analysis" refers to the process of processing collected biometric data and converting it into meaningful information (e.g., indicators of sleep quality or health status).

[0618] "Advice" refers to specific advice based on the analysis results to improve the user's sleep and health.

[0619] An "action plan" refers to a specific implementation plan created based on the user's health status and goals, and includes, for example, daily exercise routines and pre-sleep routines.

[0620] "Providing" refers to the act of informing or displaying advice or a course of action to a user.

[0621] The present invention relates to a system for helping users achieve better quality sleep and improve their overall health and well-being by collecting biometric data from the user, analyzing that data, and providing personalized advice and action plans.

[0622] System configuration

[0623] This system consists of a terminal (smartphone) used by the user, a server for processing data, and an application for linking these.

[0624] 1. Device (smartphone)

[0625] The terminal includes hardware and software for collecting biometric data of the user, specifically the following elements:

[0626] Accelerometer: Detects the user's movements and records movement data while sleeping.

[0627] Microphone: Records sounds while you sleep (snoring and surrounding noises).

[0628] Application: Collects and transmits biometric data and displays advice and action plans from the server.

[0629] 2. Server

[0630] The server is a central processing unit for receiving and analyzing the collected data. The server includes the following functions:

[0631] Data analysis: The system runs algorithms that analyze the collected biometric data, which calculates things like total sleep time, deep sleep time, and snoring frequency.

[0632] Advice generation: Based on the analysis results, appropriate advice and action plans are generated for the user, including specific advice such as "stretch before going to bed" and "adjust the humidity in your bedroom."

[0633] Goal setting support: Generates a detailed action plan based on the health goals set by the user. For example, for a goal of "exercising 30 minutes daily," the system suggests the type and timing of exercise.

[0634] 3. Applications (Software)

[0635] The system application provides an interface for users to operate on their smartphones and collect, send, receive, and display data. Specific functions are as follows:

[0636] Data collection: Biometric data is collected from the user and sent to the server.

[0637] Advice display: displays advice and action plans from the server to the user.

[0638] Goal setting: Users can set goals for health, stress management, exercise, etc. and send them to the server.

[0639] Specific examples

[0640] At 11 PM, the user launches a smartphone app and sets it to sleep mode. The device collects movement data and sounds throughout the night, and when the user reopens the app at 7 AM the next morning, the collected data is sent to the server. The server obtains the analysis results of 7 hours of total sleep, 2.5 hours of deep sleep, and 10 snoring episodes. As a result, the server generates and notifies the user of advice such as "stretching before going to bed at night" and "using a humidifier to maintain humidity in the bedroom." The user follows the advice to improve their lifestyle habits and gradually achieve their goals.

[0641] Prompt Sentence Examples

[0642] Explain the processing procedure of the following system, and write it so that the subject is either the server, the terminal, or the user. Also, add a concrete example.

[0643] (system):

[0644] This system helps users ensure good quality sleep and improve their overall health and well-being. The system collects biometric data from users, analyzes that data, and provides personalized advice and action plans. The system consists of:

[0645] 1. Device (smartphone)

[0646] Acceleration sensor

[0647] microphone

[0648] application

[0649] 2. Server

[0650] Data analysis

[0651] Advice Generation

[0652] Goal setting support

[0653] 3. Applications (Software)

[0654] Data collection

[0655] Advisory Display

[0656] goal setting

[0657] (Example):

[0658] At 11 PM, the user launches a smartphone app and sets it to sleep mode. The device collects movement data and sounds throughout the night, and when the user reopens the app at 7 AM the next morning, the data is sent to the server. The server analyzes the total sleep time to be 7 hours, the deep sleep time to be 2 hours and 30 minutes, and the snoring frequency to be 10 times. As a result, the server generates and notifies the user of advice such as "stretching before going to bed at night" and "using a humidifier to maintain humidity in the bedroom." The user follows the advice provided to improve their lifestyle habits and gradually achieve their goals.

[0659] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0660] Step 1:

[0661] User: At night, the user launches the smartphone app and sets it to sleep mode. At this point, the app activates the accelerometer and microphone, and is ready to record data.

[0662] Input: User operation (launching an app, setting sleep mode)

[0663] Output: The device's sensors and microphone enter data collection mode.

[0664] What it does: When the user taps the app's "Bedtime Mode" button, the app activates the accelerometer and microphone, preparing to start recording data.

[0665] Step 2:

[0666] Device: When the user goes to sleep, the accelerometer records the user's movements and the microphone records sounds (snoring and sounds from the surrounding environment). These data are stored in a buffer at regular intervals.

[0667] Input: User movement, sounds while sleeping

[0668] Output: Buffered motion and audio data

[0669] How it works: The accelerometer detects user movement every minute and records the time and intensity of any movement. The microphone measures the decibels of sound every second and records an event if the decibel level exceeds a certain level.

[0670] Step 3:

[0671] Device: The next morning, when the user turns on their smartphone and opens the app, all the data collected overnight is sent to the server.

[0672] Input: Sleep motion and voice data collected overnight

[0673] Output: Data sent to the server

[0674] Specific operation: When the user taps the "Send Data" button in the app, the app will upload the data to the server via an Internet connection.

[0675] Step 4:

[0676] Server: Receives data from the device and first checks the data integrity. It checks the data volume and whether there are any missing data, and issues an alert if there is a shortage.

[0677] Input: Data sent from the terminal

[0678] Output: Data integrity check results, alerts if there are any missing data

[0679] What happens: The server checks the format and size of the data to make sure there is no invalid data mixed in. If there is a problem, it generates an error log.

[0680] Step 5:

[0681] Server: Analyzes the received data using batch processing, identifies each stage of sleep (deep sleep, light sleep, REM sleep, etc.), and calculates total sleep time, deep sleep time, snoring frequency, etc.

[0682] Input: Integrity-checked biometric data

[0683] Output: Analysis results (total sleep time, deep sleep time, snoring frequency, etc.)

[0684] How it works: The analysis algorithm analyzes the data over time and calculates each indicator. The results are stored in a database.

[0685] Step 6:

[0686] Server: Based on the analysis results, it generates personalized advice. For example, if deep sleep time is short, it recommends stretching.

[0687] Input: Analysis results

[0688] Output: personalized advice

[0689] Specific operation: The advice generation algorithm selects the most appropriate advice based on the analysis results and creates an advice list.

[0690] Step 7:

[0691] Server: Generates a detailed action plan based on the health goals set by the user, suggesting daily activities such as stretching and meditation.

[0692] Input: User's health goals and analysis results

[0693] Output: Detailed action plan

[0694] Specific operation: The goal management module refers to the user's goals, and the plan generation algorithm creates an individual action plan.

[0695] Step 8:

[0696] Terminal: Receives advice and action plans from the server and notifies the user using alarms and pop-up messages.

[0697] Input: Advice and action plan sent from the server

[0698] Output: User notification

[0699] What it does: The app receives a push notification, displays it as a pop-up on the screen, and notifies you by adding a reminder to your calendar.

[0700] Step 9:

[0701] User: Review the provided advice and action plan and act as instructed. You can also record your action history in the app.

[0702] Input: Notification content from the terminal (advice and action plan)

[0703] Output: Record of user actions and action history

[0704] Specific operation: The user taps the "Action Completed" button to record the day's achievements in the app, allowing the server to monitor progress.

[0705] (Application example 1)

[0706] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0707] In modern society, many people suffer from poor sleep quality and irregular lifestyles. This leads to issues such as a deterioration in health and a lower quality of life. In addition, there is a lack of appropriate meal plans based on sleep and health status, making it difficult to consume meals tailored to individual health conditions. To solve these issues, a system that utilizes sleep data to achieve comprehensive health management is needed.

[0708] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0709] In this invention, the server includes means for collecting sleep data from a user, means for analyzing the collected sleep data, means for generating individual recommendations and action plans based on the analysis results, means for providing the generated recommendations and action plans to the user, means for proposing a meal plan suitable for the user based on the analysis results, and means for providing foods and dishes according to the proposed meal plan, thereby enabling health management based on sleep data and provision of meal plans linked thereto.

[0710] "Sleep data" refers to general information related to the user's sleep, and specifically includes data such as total sleep time, deep sleep time, and frequency of snoring.

[0711] "Analysis results" refer to conclusions or information about the user's sleep quality or health condition obtained by analyzing the collected sleep data.

[0712] "Recommendations" are specific advice or improvement measures provided to users based on the analysis results.

[0713] An "action plan" is a specific action or plan that a user should take based on a recommendation.

[0714] A "meal plan" is a nutritionally balanced and appropriate meal plan designed based on the user's health status.

[0715] "Means for providing food or meals" refers to a method or system that physically delivers food or meals appropriate to the user according to the proposed meal plan.

[0716] "Health goal" refers to a health goal that a user wishes to achieve, such as weight management, stress reduction, or establishing an exercise habit.

[0717] The "daily schedule" refers to the user's daily plans and timetable, and by adjusting this, guidance is given on how to use time efficiently.

[0718] The present invention provides a system for helping users achieve better quality sleep and improve their overall health and well-being. The system collects sleep data from users, analyzes the data, and provides personalized advice, action plans, and even appropriate diet plans. Specific embodiments of the system are described below.

[0719] System configuration

[0720] This system consists of a terminal used by a user, a server for processing data, and an application for linking these.

[0721] Terminal

[0722] The device includes hardware and software for collecting the user's sleep data, including the following:

[0723] Accelerometer: Detects the user's movements and records movement data while sleeping.

[0724] Microphone: Records sounds while you sleep (snoring and surrounding noises).

[0725] Application: Collects and transmits sleep data, and displays recommendations, action plans, and meal plans from the server.

[0726] server

[0727] The server is the central processing unit for receiving and analyzing the collected data. This server includes the following functions:

[0728] Data Analysis: Run an algorithm to analyze the collected sleep data, for example using Python.

[0729] Recommendation generation: Generates advice and action plans appropriate for the user based on the analysis results.

[0730] Meal plan generation: Proposes a nutritionally balanced meal plan based on the user's health status.

[0731] Delivery Order: A means of delivering food and dishes according to a proposed meal plan.

[0732] Application (software)

[0733] The system application provides an interface for users to operate the terminal and collect, send, receive, and display data. The specific functions are as follows:

[0734] Data Collection: Sleep data is collected from the user and sent to the server.

[0735] Recommendation display: Advice, action plans, and meal plans from the server are displayed to the user.

[0736] Goal setting: Users can set goals for health, stress management, exercise, etc. and send them to the server.

[0737] Delivery Order Management: Use delivery services based on proposed meal plans.

[0738] Program processing

[0739] Data collection and transmission

[0740] The user launches the app on their device at night and activates sleep mode. This allows the device to collect sleep data using the accelerometer and microphone, and store it in a buffer. When the user reopens the app the next morning, the collected data is sent to the server.

[0741] Receiving and analyzing data

[0742] The server receives the collected data, first verifies its integrity, and then analyzes it using specific algorithms to calculate metrics such as total sleep time, deep sleep time, and snoring frequency.

[0743] Generate recommendations and meal plans

[0744] The server generates personalized recommendations based on the analysis results, and also suggests nutritionally balanced meal plans based on the user's health status, for example, suggesting a high-protein breakfast to a sleep-deprived user.

[0745] Fulfilling delivery orders

[0746] The application automatically places food and meal delivery orders based on the generated meal plan, allowing the user to receive the food and meals according to the proposed meal plan.

[0747] Examples of concrete examples and prompts

[0748] As a specific example, a user launches an app on their device at 11 PM and sets it to sleep mode. The device collects movement data and sounds throughout the night, and when the user reopens the app at 7 AM the next morning, the data is sent to the server. The server analyzes the total sleep time to be 7 hours, the deep sleep time to be 2 hours and 30 minutes, and the snoring frequency to be 10 times. As a result, it generates recommendations to the user, such as "stretching before going to bed at night" and "using a humidifier to maintain humidity in the bedroom." In addition, based on the user's health condition, it recommends a "high-protein breakfast," and appropriate food is delivered.

[0749] Example prompt sentence:

[0750] The user's sleep data for the past 7 days is as follows: Total sleep time: 6 hours on average, Deep sleep time: 2 hours on average, Snoring frequency: 15 times on average. Based on this, provide appropriate diet and lifestyle recommendations to maintain good health.

[0751] This not only allows users to get quality sleep, but also allows them to eat a nutritionally balanced diet that is appropriate for their health condition.

[0752] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0753] Step 1:

[0754] Bedtime mode settings

[0755] At night, the user launches the app on their device and sets it to sleep mode, which prepares the device to start collecting motion data and sounds.

[0756] Input: User launches app and sets Bedtime mode

[0757] Output: The device switches to data collection mode

[0758] Step 2:

[0759] Data collection

[0760] The device's accelerometer and microphone continuously collect motion data and sounds (such as snoring and ambient noise) and store them in a buffer.

[0761] Input: User motion data and sound

[0762] Output: Buffered sleep data (motion data and sound)

[0763] Step 3:

[0764] Sending data

[0765] The next morning, when the user reopens the app, the saved sleep data is sent to the server.

[0766] Input: Buffered sleep data

[0767] Output: Sleep data sent to the server

[0768] Step 4:

[0769] Data Receipt and Validation

[0770] The server checks the integrity of the received data, verifying that there are no missing data or outliers.

[0771] Input: Sleep data sent to the server

[0772] Output: Verified sleep data

[0773] Step 5:

[0774] Data analysis

[0775] The server analyzes the verified data using a specific algorithm, calculating indicators such as total sleep time, deep sleep time, and snoring frequency to evaluate the user's sleep quality.

[0776] Input: Verified sleep data

[0777] Output: Analysis results (total sleep time, deep sleep time, snoring frequency, etc.)

[0778] Step 6:

[0779] Generating recommendations

[0780] Based on the analysis results, the server generates personalized recommendations and action plans, such as "avoid caffeine late at night" or "adjust the humidity in your bedroom."

[0781] Input: Analysis results

[0782] Output: personalized recommendations and action plans

[0783] Step 7:

[0784] Generate a meal plan

[0785] Based on the analysis results, the server proposes a suitable meal plan for the user, for example, recommending a high-protein breakfast for a sleep-deprived user.

[0786] Input: Analysis results

[0787] Output: Suitable meal plan

[0788] Step 8:

[0789] View recommendations and meal plans

[0790] The terminal displays the recommendations and meal plans received from the server to the user.

[0791] Input: Recommendations and Meal Plans

[0792] Output: Recommendations and meal plans displayed on the device screen

[0793] Step 9:

[0794] Fulfilling delivery orders

[0795] Once the user agrees to the meal plan, the device automatically sends a delivery order to the server, and the food or meal is delivered to the user.

[0796] Input: User consent

[0797] Output: Sending delivery orders and delivering food and dishes

[0798] Through these steps, users can ensure they get good quality sleep and eat a diet that is optimal for their health.

[0799] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0800] The present invention provides a system for ensuring a user's quality sleep and improving their overall health and well-being. The system collects and analyzes sleep and emotional data from the user, and provides personalized advice and action plans based on the collected data. Specific embodiments are described in detail below.

[0801] System configuration

[0802] This system consists of the following elements:

[0803] 1. Smartphone (device)

[0804] The terminal includes hardware and software for collecting the user's sleep and emotion data.

[0805] Acceleration sensor: Detects the user's movements and records movement data while sleeping.

[0806] Microphone: Records sounds while you sleep (snoring and surrounding noises).

[0807] Camera: Collects user emotion data through facial expression analysis.

[0808] Application: Collects and transmits sleep and emotional data, and displays recommendations and action plans from the server.

[0809] 2. Server

[0810] The server is a central processing unit for receiving and analyzing the collected data.

[0811] Data Analysis: Running algorithms to process sleep and emotion data.

[0812] Recommendation generation: Generates advice and action plans appropriate for the user based on the analysis results.

[0813] Emotion Engine: Recognizes user emotions and performs emotion-based analysis and suggestions.

[0814] 3. Applications (Software)

[0815] It provides an interface on a smartphone operated by the user, and collects, sends, receives, and displays data.

[0816] Data collection: Sleep data and emotion data are collected from the user and sent to the server.

[0817] Recommendation display: Displaying advice and action plans from the server to the user.

[0818] Goal setting: Users can set goals for health, stress management, exercise, etc. and send them to the server.

[0819] Program processing

[0820] Collection and transmission of sleep and emotional data

[0821] User: The smartphone app is started at night, sleep mode is set, facial expression analysis function is enabled, and the emotion engine is running.

[0822] Device: The acceleration sensor and microphone are activated to continuously collect data while the user is sleeping. The camera analyzes the user's facial expressions and collects emotional data.

[0823] Receiving and analyzing data

[0824] On the device: The next morning, when the user wakes up and reopens the app, the collected data is sent to the server.

[0825] Server: Receives the collected data and first verifies the data integrity. Then, it analyzes the data using algorithms to calculate metrics such as total sleep time, deep sleep time, light sleep time, and snoring frequency. In addition, it analyzes the user's emotional data using an emotion engine.

[0826] Generate recommendations and action plans

[0827] Server: Based on the analysis results, it generates different recommendations for each user. For example, if a user snores a lot, it recommends using a humidifier to maintain appropriate humidity in the bedroom. Based on emotional data, if the user is under a lot of stress, it suggests meditating to relax before going to bed at night.

[0828] Server: Generates a detailed action plan based on the goals set by the user. For example, if the goal is to exercise 30 minutes a day, the server will suggest specific exercise content and timing.

[0829] User notification and assistance

[0830] Device: Displays the recommendations and action plans received from the server to the user. It also suggests optimal time management based on the user's daily schedule and sets reminders.

[0831] User: Opens the app, reviews the recommendations and action plans provided, and acts on them.

[0832] Specific examples

[0833] The user enables the emotion engine and sets the bedtime mode at 10 p.m. The device collects motion, sound, and emotion data throughout the night, and when the user reopens the app at 7 a.m. the next morning, the data is sent to the server. The server analyzes the data and determines that the total sleep time is 7 hours, the deep sleep time is 2 hours and 30 minutes, the snoring frequency is 10 times, and the stress level is high. As a result, the server generates and notifies the user of the recommendation to "meditate before bed and use a humidifier to maintain humidity in the bedroom." The user follows the suggested advice, improving their sleep quality and promoting their overall health.

[0834] This allows users to combine emotional data to receive more accurate advice and action plans to build healthy lifestyle habits.The present invention provides a comprehensive system for supporting users' sleep and emotional management based on the claims.

[0835] The processing flow will be explained below.

[0836] Step 1:

[0837] A user opens a smartphone app, sets the sleep mode, enables the facial expression analysis function, and runs the emotion engine. At this time, the app checks for necessary permissions (such as access to the microphone, accelerometer, and camera).

[0838] Step 2:

[0839] The device checks the user's Bedtime setting and activates the accelerometer, microphone, and camera, ready to record the user's movements, sounds, and facial expressions in real time.

[0840] Step 3:

[0841] The device continuously collects motion and sound data from the user while they sleep. The acceleration sensor records the user's movements and movements, and the microphone records snoring and environmental sounds. The camera also records the user's facial expressions, which the emotion engine analyzes to generate emotion data.

[0842] Step 4:

[0843] The data collected by the terminal is stored in a buffer at regular intervals (for example, every second), which ensures data consistency and integrity.

[0844] Step 5:

[0845] When the user wakes up the next morning and opens their smartphone, the app prepares to send the data collected during the night to the server. The user taps the "Send Data" button to send the collected data to the server.

[0846] Step 6:

[0847] The server receives the data sent by the user and checks the data for completeness and consistency. If there are any missing or inconsistent data, it notifies the user and asks them to resubmit.

[0848] Step 7:

[0849] The server runs a data analysis algorithm and emotion engine to analyze the collected sleep and emotion data, and calculates the following metrics:

[0850] Total sleep time

[0851] Deep sleep time

[0852] Light sleep duration

[0853] The frequency and frequency of snoring

[0854] Emotional fluctuation patterns

[0855] Step 8:

[0856] The server then generates a report for each user based on the analysis results, which includes the aforementioned metrics and provides an overall assessment of the user's sleep performance.

[0857] Step 9:

[0858] Based on the generated report, the server generates specific recommendations and action plans suited to the user. For example, if the user snores a lot, it may suggest using a humidifier to maintain appropriate humidity, or if the user is under high stress based on emotional data, it may advise them to practice meditation to relax before going to bed at night.

[0859] Step 10:

[0860] The server receives the user's goal setting and generates a customized action plan accordingly. For example, if the goal is set to "exercise 30 minutes every day," the server will suggest the type and timing of exercise.

[0861] Step 11:

[0862] The device displays the reports, recommendations, and action plans received from the server to the user, and also sets reminders based on daily schedules to help users use their time efficiently.

[0863] Step 12:

[0864] The user opens the app, reviews the reports and recommendations provided, and plans specific steps to implement the proposed action plan and incorporate it into their daily activities.

[0865] Step 13:

[0866] The device periodically records the user's daily progress and emotional data and sends it to the server, which can then use the latest data to provide further recommendations and refine the action plan.

[0867] In this way, the system of the present invention collects and analyzes the user's sleep and emotional data, and provides personalized advice and action plans to improve the user's sleep quality and overall health. By implementing specific processing steps, the user can effectively manage their health.

[0868] Example 2

[0869] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0870] Conventional sleep management systems only collect and analyze users' sleep data and are unable to provide comprehensive recommendations that take into account the user's emotional state. As a result, users are unable to receive support in both appropriate sleep advice and emotional management, resulting in insufficient benefits for improving their overall health and well-being.

[0871] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0872] In this invention, the server includes means for collecting sleep data and emotional data from a user, means for verifying the completeness of the collected data, means for analyzing the data for total sleep time, deep sleep time, light sleep time, snoring frequency, and emotional state, means for generating individual recommendations and action plans based on the analysis results, and means for providing the generated recommendations and action plans to the user and setting reminders. This makes it possible to comprehensively analyze the user's sleep data and emotional data and provide optimal recommendations tailored to individual needs.

[0873] "User" refers to an individual who utilizes the system to provide sleep and emotional data.

[0874] "Sleep data" refers to information such as movement data, total sleep time, deep sleep time, light sleep time, and snoring frequency collected while the user is sleeping.

[0875] "Emotion data" refers to data on the user's emotional state obtained by analyzing their facial expressions or using an emotion engine.

[0876] "Means of collection" refers to the acceleration sensor, microphone, camera, and software that controls these devices installed on the user's device.

[0877] "Data Integrity Verification Measures" refers to the processes and algorithms used to verify that data received by the server is complete, free of errors and omissions.

[0878] "Means for analyzing" refers to algorithms or software for analyzing the collected sleep data and emotional data and assessing total sleep time, deep sleep time, light sleep time, snoring frequency, and emotional state.

[0879] "Recommendations" refers to specific lifestyle and behavioral advice provided to users based on the analysis results.

[0880] An "action plan" refers to a plan that includes specific actions and schedules that a user will take.

[0881] "Reminder" refers to a function that notifies users at the appropriate time to execute action plans or recommendations set by the user.

[0882] The present invention is a system for ensuring users' quality sleep and improving their overall health and well-being. The system collects and analyzes sleep and emotional data from users, and provides personalized advice and action plans based on the collected data.

[0883] System configuration

[0884] This system consists of the following elements:

[0885] 1. Smartphone (device)

[0886] The terminal includes hardware and software for collecting the user's sleep and emotion data.

[0887] Acceleration sensor: Detects the user's movements and records movement data while sleeping.

[0888] Microphone: Records sounds while you sleep (snoring and surrounding noises).

[0889] Camera: Collects user emotion data through facial expression analysis.

[0890] Application: Collects and transmits sleep and emotional data, and displays recommendations and action plans from the server.

[0891] 2. Server

[0892] The server is the central processing unit that receives and analyzes the collected data and provides specific recommendations to the user.

[0893] Data Analysis: Running algorithms to process sleep and emotion data.

[0894] Recommendation generation: Generates advice and action plans appropriate for the user based on the analysis results.

[0895] Emotion Engine: Recognizes user emotions and performs emotion-based analysis and suggestions.

[0896] 3. Applications (Software)

[0897] It provides an interface on the smartphone operated by the user, and collects, sends, receives, and displays data.

[0898] Data collection: Sleep data and emotion data are collected from the user and sent to the server.

[0899] Recommendation display: Displaying advice and action plans from the server to the user.

[0900] Goal setting: Users can set goals for health, stress management, exercise, etc. and send them to the server.

[0901] Specific examples

[0902] The user enables the emotion engine and sets the sleep mode at 10 p.m. The device collects motion, sound, and emotion data throughout the night, and when the user reopens the app at 7 a.m. the next morning, the data is sent to the server. The server verifies the completeness of the data and analyzes it. For example, the server determines that the total sleep time is 7 hours, the deep sleep time is 2 hours and 30 minutes, the snoring frequency is 10 times, and the stress level is high. As a result, the server generates and notifies the user of the recommendation to "meditate before bed and use a humidifier to maintain humidity in the bedroom." The user follows the suggested advice, improving their sleep quality and promoting their overall health.

[0903] Prompt Sentence Examples

[0904] "The user enables the emotion engine and sets bedtime mode at 10 p.m. The device collects motion data, sound data, and emotion data throughout the night. When the user reopens the app at 7 a.m. the next morning, the data is sent to the server. The server analyzes the data and determines that the total sleep time is 7 hours, the deep sleep time is 2 hours and 30 minutes, the snoring frequency is 10 times, and the stress level is high. As a result, the server generates a recommendation to 'meditate before going to bed at night and use a humidifier to maintain humidity in the bedroom' and notifies the user."

[0905] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0906] Step 1:

[0907] User sets Bedtime mode

[0908] Input: User input operations

[0909] How it works: A user sets up bedtime mode on a smartphone app and enables the emotion engine and facial expression analysis functions. Specifically, they tap "Bedtime mode" on the app's settings screen and turn on the emotion engine and facial expression analysis.

[0910] Output: The device is ready to collect data.

[0911] Step 2:

[0912] The device collects data

[0913] Input: User-defined bedtime mode

[0914] Operation: The device activates the accelerometer, microphone, and camera. The accelerometer continuously records the user's movements, and the microphone collects sounds in the bedroom. The camera analyzes the user's facial expressions and collects emotional data. This data is stored in temporary storage on the device.

[0915] Output: Collected sleep and emotion data

[0916] Step 3:

[0917] The device sends the data to the server

[0918] Input: Collected sleep and emotion data

[0919] How it works: The next morning, when the user reopens the smartphone app, data transmission begins. The device sends the data stored in its internal storage to the server. This transmission is encrypted for security reasons.

[0920] Output: Data sent to the server

[0921] Step 4:

[0922] The server verifies the integrity of the data

[0923] Input: Data sent from the terminal

[0924] Behavior: The server verifies the integrity of the data it receives, checking for missing or corrupted data and requesting retransmissions if necessary.

[0925] Output: Data with integrity checked

[0926] Step 5:

[0927] The server analyzes the data

[0928] Input: Integrity checked data

[0929] How it works: The server applies algorithms to analyze the data. First, it calculates sleep metrics such as total sleep time, deep sleep time, light sleep time, and snoring frequency. Second, it uses an emotion engine to analyze the user's emotional state.

[0930] Output: Analysis results of sleep data and emotion data

[0931] Step 6:

[0932] The server generates recommendations and action plans

[0933] Input: Analysis results

[0934] How it works: The server generates personalized recommendations for each user based on the analysis results. For example, if the deep sleep time is short, it may suggest "We recommend meditating every night before going to bed." If the user snores a lot, it may recommend "Using a humidifier to adjust the humidity in the bedroom." It also generates a detailed action plan based on the goals set by the user.

[0935] Output: Generated recommendations and action plans

[0936] Step 7:

[0937] The device notifies the user

[0938] Input: Generated recommendations and action plans

[0939] How it works: The device will notify you of the recommendations and action plans it receives from the server. The notification will include specific advice and instructions on how to implement it. Detailed instructions will also be provided within the app.

[0940] Output: User notification

[0941] Step 8:

[0942] The user executes the advice

[0943] Input: Notifications from your device

[0944] Action: The user sees the notification, opens the app to see the provided recommendation and action plan, and follows through on the specific advice offered (e.g., meditating or using a humidifier).

[0945] Output: Improved user health and sleep quality

[0946] (Application example 2)

[0947] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0948] In traditional food delivery services, the sleep and emotional state of delivery staff often have a significant impact on the quality of service. However, currently, there is no system that effectively utilizes this data to optimize staff health and performance. Furthermore, the lack of work shift and rest plans based on sleep and emotional data makes it difficult for delivery staff to manage stress and perform their work efficiently. Therefore, there is a need for a system that collects and analyzes sleep and emotional data and provides appropriate recommendations and action plans based on this data to improve staff health and service quality.

[0949] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting sleep data and emotion data from the user, means for analyzing the collected sleep data and emotion data, and means for generating individual recommendations and action plans based on the analysis results. This makes it possible to generate work shift and rest plans that optimize staff performance based on the collected data. The server also includes means for providing the generated recommendations and action plans to the user, means for generating work shift and rest plans to optimize the user's performance, and means for notifying the user of the generated work shift and rest plans. This solves the problem of improving the health and service quality of delivery staff.

[0950] "User" refers to a person who uses the system to provide data and receive recommendations and action plans.

[0951] "Sleep data" refers to data that includes information about a user's sleep patterns, sleep duration, deep sleep duration, light sleep duration, and other sleep-related information.

[0952] "Emotion data" is data that represents the user's emotional state, and includes information such as stress level and happiness level collected by facial expression analysis.

[0953] "Recommendations" refer to advice or specific instructions for action provided to users based on analysis results.

[0954] An "action plan" is a plan outlining specific actions or steps a user should take to optimize their health or performance.

[0955] A "work shift" refers to the allocation of working hours for the job a user is engaged in, and includes break times, start times, end times, etc.

[0956] A "rest plan" is a plan that includes a rest schedule and specific rest methods designed to optimize a user's rest.

[0957] "Data collection means" refers to the sensors, cameras, microphones and associated software used to acquire the user's sleep and emotional data.

[0958] "Data Analysis Means" refers to the algorithms and analytical software used to process the collected sleep and emotional data and assess the user's condition.

[0959] "Notification vehicles" are digital interfaces or applications used to communicate recommendations, action plans, work shifts, and break plans to users.

[0960] This system collects and analyzes sleep and emotional data from delivery staff, and provides work shift and rest plans and personalized advice based on the results. This system is designed to optimize users' health and work performance.

[0961] System configuration

[0962] This system consists of the following elements:

[0963] 1. Terminal

[0964] The terminal includes hardware and software for collecting sleep and emotion data of delivery staff.

[0965] Acceleration sensor: Detects staff movement and records movement data while sleeping.

[0966] Microphone: Records sounds while you sleep (snoring and surrounding noises).

[0967] Camera: Collects staff emotional data through facial expression analysis.

[0968] Application: Collects and transmits sleep and emotional data, and displays recommendations and action plans from the server.

[0969] 2. Server

[0970] The server is a central processing unit for receiving and analyzing the collected data.

[0971] Data Analysis: Running algorithms to process sleep and emotion data.

[0972] Recommendation generation: Generate appropriate advice and action plans for staff based on the analysis results.

[0973] Work shift and break plan generation: Propose optimal work shift and break plans to staff.

[0974] 3. Applications (Software)

[0975] It provides an interface on a smartphone operated by the user, and collects, sends, receives, and displays data.

[0976] Data collection: Sleep data and emotion data are collected from staff and sent to the server.

[0977] Recommendation display: Display advice and action plans from the server to staff.

[0978] Goal setting: Staff can set goals for health, stress management, exercise, etc. and send them to the server.

[0979] Program processing

[0980] Data collection

[0981] The device uses an accelerometer and microphone to collect motion data and sounds from staff, and a camera to record emotional data, which is then sent to a server via an application.

[0982] Data analysis and recommendation generation

[0983] The server analyzes the collected data, assesses sleep patterns and emotional states, and generates personalized recommendations and specific action plans for staff, along with optimal work shift and break plans.

[0984] Specific examples

[0985] A delivery staff member launches the app at 10 p.m. and sets it to sleep mode. After a week, the app checks the collected data and finds that the average sleep time was 6.5 hours and that there were three days of high stress. The server generates advice to the staff member, such as "Get more sleep" and "Try to spend more time relaxing," and notifies them. It also suggests adjusting work shifts and adding breaks.

[0986] Prompt Sentence Examples

[0987] It is recommended that staff meditate before going to bed and use a humidifier to maintain humidity in the bedroom. We will propose appropriate shift and rest plans based on staff sleep and emotional data. Please advise us on what approach would be effective.

[0988] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0989] Step 1:

[0990] The terminal collects sleep data and emotion data from the user (delivery staff).

[0991] Input: Accelerometer, microphone, camera

[0992] Output: Sleep data (movement data, sound data such as snoring), emotion data (facial expression analysis results)

[0993] Specific operation: When the user goes to bed at night, the device is turned on and set to sleep mode. The device's acceleration sensor detects movement data, the microphone records sounds such as snoring, and the camera analyzes facial expressions to collect emotional data.

[0994] Step 2:

[0995] The terminal transmits the collected sleep data and emotion data to a server.

[0996] Input: Data collected in step 1

[0997] Output: Raw data sent to the server

[0998] Specific operation: When the user wakes up the next morning, the device automatically uploads the data to the server.

[0999] Step 3:

[1000] A server receives the collected sleep and emotion data and checks the data for completeness.

[1001] Input: Sleep data and emotion data sent from the device

[1002] Output: Complete dataset, checks for missing data and outliers

[1003] Specific operation: Data is stored in a database within the server, and an analysis program checks the integrity of the data.

[1004] Step 4:

[1005] The server performs data analysis based on the data whose integrity has been confirmed.

[1006] Input: Complete sleep and emotion data

[1007] Output: Analysis results (total sleep time, deep sleep time, light sleep time, snoring frequency, emotional state, etc.)

[1008] How it works: The analysis algorithm works to calculate multiple indicators (sleep patterns, emotional trends, etc.).

[1009] Step 5:

[1010] The server generates personalized recommendations and action plans based on the analysis results.

[1011] Input: Data analysis results

[1012] Output: Recommendations and action plans

[1013] Specific Actions: Generative AI models are used to generate appropriate suggestions, creating specific advice (e.g., "Get more sleep" or "Meditate to relax") and action plans.

[1014] Step 6:

[1015] The server transmits the generated recommendations and action plans to the terminal.

[1016] Input: Generated recommendations and action plans

[1017] Output: Suggestions sent to the device

[1018] Specific operation: The communication module operates and the generated proposal is pushed to the device.

[1019] Step 7:

[1020] The terminal presents the recommendations and action plans received from the server to the user.

[1021] Input: Recommendations and action plans sent by the server

[1022] Output: Advice and plan displayed to the user

[1023] Specific operation: The terminal application displays the received data, and the user confirms it.

[1024] Step 8:

[1025] The user acts on the presented recommendations and action plan.

[1026] Input: Recommendations and action plans displayed on the device

[1027] Output: Action taken (adjustment of break time, change of work shift, etc.)

[1028] Specific actions: The user follows the application's instructions to adjust their daily activities.

[1029] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1030] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1031] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1032] [Third embodiment]

[1033] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1034] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1035] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1036] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1037] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1038] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1039] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1040] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1041] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1042] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1043] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1044] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1045] The present invention relates to a system for helping users ensure good quality sleep and improve their overall health and well-being. The system collects sleep data from users, analyzes the data, and provides personalized advice and action plans. Specific embodiments of the system are described below.

[1046] System configuration

[1047] This system consists of a smartphone (terminal) used by the user, a server for processing data, and an application for linking these.

[1048] 1. Smartphone (device)

[1049] The device includes hardware and software for collecting the user's sleep data, including the following:

[1050] Accelerometer: Detects the user's movements and records movement data while sleeping.

[1051] Microphone: Records sounds while you sleep (snoring and surrounding noises).

[1052] Application: Collects and transmits sleep data, and displays recommendations and action plans from the server.

[1053] 2. Server

[1054] The server is the central processing unit for receiving and analyzing the collected data. This server includes the following functions:

[1055] Data Analysis: Run algorithms to analyze the collected sleep data.

[1056] Recommendation generation: Generates advice and action plans appropriate for the user based on the analysis results.

[1057] Goal setting support: Generates an action plan based on the health goals set by the user.

[1058] 3. Applications (Software)

[1059] The system application provides an interface for users to operate on their smartphones and collect, send, receive, and display data. Specific functions are as follows:

[1060] Data Collection: Sleep data is collected from the user and sent to the server.

[1061] Recommendation display: Displaying advice and action plans from the server to the user.

[1062] Goal setting: Users can set goals for health, stress management, exercise, etc. and send them to the server.

[1063] Program processing

[1064] Sleep data collection and transmission

[1065] User: At night, launch the smartphone app and set it to sleep mode, which prepares the device to record motion data and sounds while sleeping.

[1066] Device: When the user goes to sleep, the accelerometer and microphone continuously collect data and store it in a buffer.

[1067] Receiving and analyzing data

[1068] Device: The next morning, when the user starts up their smartphone, the collected data is sent to the server.

[1069] Server: Receives the collected data and first verifies the data integrity, then analyzes the data using specific algorithms to calculate metrics such as total sleep time, deep sleep time, light sleep time, and snoring frequency.

[1070] Generate recommendations and action plans

[1071] Server: Based on the analysis results, the server generates recommendations that are unique to each user. These recommendations include specific advice such as "stretch before bed" or "adjust the humidity in your bedroom."

[1072] Server: Generates a detailed action plan based on the goals set by the user. For example, for the goal of "exercising 30 minutes every day", the server suggests the type and timing of exercise.

[1073] User notification and assistance

[1074] Device: Displays the recommendations and action plans received from the server to the user. It also suggests optimal ways to spend time based on the user's daily schedule and sets reminders.

[1075] User: Review and act on the recommendations and action plans provided.

[1076] Specific examples

[1077] At 11 PM, the user launches a smartphone app and sets it to sleep mode. The device collects movement data and sounds throughout the night, and when the user reopens the app at 7 AM the next morning, the data is sent to the server. The server analyzes the total sleep time to be 7 hours, the deep sleep time to be 2 hours and 30 minutes, and the snoring frequency to be 10 times. As a result, the server generates recommendations to the user, such as "stretching before going to bed at night" and "using a humidifier to maintain humidity in the bedroom." The user follows the advice provided to improve their lifestyle habits and gradually achieve their goals.

[1078] This allows users to get better quality sleep and improve their overall health and well-being. The present invention provides a system for achieving such multifaceted health management based on the claims.

[1079] The processing flow will be explained below.

[1080] Step 1:

[1081] The user opens an app on their smartphone and sets Bedtime mode, then checks the permissions the app needs to use to function properly (such as using the microphone or accelerometer).

[1082] Step 2:

[1083] The device checks the user's Bedtime setting and activates the accelerometer and microphone, preparing to record the user's movements and sounds in real time.

[1084] Step 3:

[1085] The device continuously collects user movement data and environmental sounds while the user sleeps. The accelerometer records the user's movements and movements, and the microphone records snoring and environmental sounds. This data is stored in a buffer at regular intervals (e.g., every second).

[1086] Step 4:

[1087] When the user wakes up the next morning and opens their smartphone, the app will self-check the collected data and prepare it to be sent to the server. The user can then tap the "Send Data" button to send the collected data to the server.

[1088] Step 5:

[1089] The server receives the data sent by the user and checks the data for completeness and consistency. If there are no missing or inconsistent data, it proceeds to the next step.

[1090] Step 6:

[1091] The server runs a data analysis algorithm to analyze the collected sleep data, specifically calculating the following metrics:

[1092] Total sleep time

[1093] Deep sleep time

[1094] Light sleep duration

[1095] The frequency and frequency of snoring

[1096] Step 7:

[1097] The server then generates a report for each user based on the analysis results, which includes the aforementioned metrics and provides an overall assessment of the user's sleep performance.

[1098] Step 8:

[1099] The server uses the generated report to generate specific recommendations and action plans for the user, which, depending on the analysis results, may include advice such as:

[1100] Relaxation techniques to do before bed

[1101] Adjusting the bedroom environment (temperature, humidity, sound)

[1102] Daytime activities (exercise, caffeine intake, etc.)

[1103] Step 9:

[1104] The server receives the user's goal settings and generates a customized action plan based on them. For example, if the goal is "30 minutes of exercise every day," the server will suggest the type of exercise and timing.

[1105] Step 10:

[1106] The device displays the reports, recommendations, and action plans received from the server to the user. The app notifies the user that a new report is available.

[1107] Step 11:

[1108] The user opens the app, reviews the reports and recommendations provided, and plans specific steps to implement the proposed action plan.

[1109] Step 12:

[1110] The device manages the user's daily schedule and sets reminders to help them use their time efficiently. For example, if the user sets a goal of "going to bed at 10 p.m.", the device will display a notification such as "take some time to relax at 9:30 p.m."

[1111] Through these processing steps, users receive specific advice and action plans to improve their sleep quality and build healthy lifestyle habits.

[1112] Example 1

[1113] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1114] In modern society, many people suffer from poor sleep quality due to excessive stress and irregular lifestyles. Furthermore, it is often difficult to obtain specific advice and action plans tailored to individual health conditions, making it difficult to find effective solutions. This invention aims to improve users' sleep quality and overall health by collecting and analyzing their sleep and health data to provide more effective, individually tailored advice and action plans.

[1115] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1116] In this invention, the server includes a device for collecting biometric data from a user, a device for analyzing the collected biometric data, and a device for generating individual advice and action plans based on the analysis results, thereby making it possible to provide optimal advice and action plans to the user.

[1117] "User" refers to an individual who uses this system.

[1118] "Biometric data" is a general term for various data that indicate the user's sleep state and health condition, and specifically includes sleep time, deep sleep time, snoring frequency, and the like.

[1119] "Device" refers to hardware or software for performing a particular function.

[1120] "Analysis" refers to the process of processing collected biometric data and converting it into meaningful information (e.g., indicators of sleep quality or health status).

[1121] "Advice" refers to specific advice based on the analysis results to improve the user's sleep and health.

[1122] An "action plan" refers to a specific implementation plan created based on the user's health status and goals, and includes, for example, daily exercise routines and pre-sleep routines.

[1123] "Providing" refers to the act of informing or displaying advice or a course of action to a user.

[1124] The present invention relates to a system for helping users achieve better quality sleep and improve their overall health and well-being by collecting biometric data from the user, analyzing that data, and providing personalized advice and action plans.

[1125] System configuration

[1126] This system consists of a terminal (smartphone) used by the user, a server for processing data, and an application for linking these.

[1127] 1. Device (smartphone)

[1128] The terminal includes hardware and software for collecting biometric data of the user, specifically the following elements:

[1129] Accelerometer: Detects the user's movements and records movement data while sleeping.

[1130] Microphone: Records sounds while you sleep (snoring and surrounding noises).

[1131] Application: Collects and transmits biometric data and displays advice and action plans from the server.

[1132] 2. Server

[1133] The server is a central processing unit for receiving and analyzing the collected data. The server includes the following functions:

[1134] Data analysis: The system runs algorithms that analyze the collected biometric data, which calculates things like total sleep time, deep sleep time, and snoring frequency.

[1135] Advice generation: Based on the analysis results, appropriate advice and action plans are generated for the user, including specific advice such as "stretch before going to bed" and "adjust the humidity in your bedroom."

[1136] Goal setting support: Generates a detailed action plan based on the health goals set by the user. For example, for a goal of "exercising 30 minutes daily," the system suggests the type and timing of exercise.

[1137] 3. Applications (Software)

[1138] The system application provides an interface for users to operate on their smartphones and collect, send, receive, and display data. Specific functions are as follows:

[1139] Data collection: Biometric data is collected from the user and sent to the server.

[1140] Advice display: displays advice and action plans from the server to the user.

[1141] Goal setting: Users can set goals for health, stress management, exercise, etc. and send them to the server.

[1142] Specific examples

[1143] At 11 PM, the user launches a smartphone app and sets it to sleep mode. The device collects movement data and sounds throughout the night, and when the user reopens the app at 7 AM the next morning, the collected data is sent to the server. The server obtains the analysis results of 7 hours of total sleep, 2.5 hours of deep sleep, and 10 snoring episodes. As a result, the server generates and notifies the user of advice such as "stretching before going to bed at night" and "using a humidifier to maintain humidity in the bedroom." The user follows the advice to improve their lifestyle habits and gradually achieve their goals.

[1144] Prompt Sentence Examples

[1145] Explain the processing procedure of the following system, and write it so that the subject is either the server, the terminal, or the user. Also, add a concrete example.

[1146] (system):

[1147] This system helps users ensure good quality sleep and improve their overall health and well-being. The system collects biometric data from users, analyzes that data, and provides personalized advice and action plans. The system consists of:

[1148] 1. Device (smartphone)

[1149] Acceleration sensor

[1150] microphone

[1151] application

[1152] 2. Server

[1153] Data analysis

[1154] Advice Generation

[1155] Goal setting support

[1156] 3. Applications (Software)

[1157] Data collection

[1158] Advisory Display

[1159] goal setting

[1160] (Example):

[1161] At 11 PM, the user launches a smartphone app and sets it to sleep mode. The device collects movement data and sounds throughout the night, and when the user reopens the app at 7 AM the next morning, the data is sent to the server. The server analyzes the total sleep time to be 7 hours, the deep sleep time to be 2 hours and 30 minutes, and the snoring frequency to be 10 times. As a result, the server generates and notifies the user of advice such as "stretching before going to bed at night" and "using a humidifier to maintain humidity in the bedroom." The user follows the advice provided to improve their lifestyle habits and gradually achieve their goals.

[1162] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1163] Step 1:

[1164] User: At night, the user launches the smartphone app and sets it to sleep mode. At this point, the app activates the accelerometer and microphone, and is ready to record data.

[1165] Input: User operation (launching an app, setting sleep mode)

[1166] Output: The device's sensors and microphone enter data collection mode.

[1167] What it does: When the user taps the app's "Bedtime Mode" button, the app activates the accelerometer and microphone, preparing to start recording data.

[1168] Step 2:

[1169] Device: When the user goes to sleep, the accelerometer records the user's movements and the microphone records sounds (snoring and sounds from the surrounding environment). These data are stored in a buffer at regular intervals.

[1170] Input: User movement, sounds while sleeping

[1171] Output: Buffered motion and audio data

[1172] How it works: The accelerometer detects user movement every minute and records the time and intensity of any movement. The microphone measures the decibels of sound every second and records an event if the decibel level exceeds a certain level.

[1173] Step 3:

[1174] Device: The next morning, when the user turns on their smartphone and opens the app, all the data collected overnight is sent to the server.

[1175] Input: Sleep motion and voice data collected overnight

[1176] Output: Data sent to the server

[1177] Specific operation: When the user taps the "Send Data" button in the app, the app will upload the data to the server via an Internet connection.

[1178] Step 4:

[1179] Server: Receives data from the device and first checks the data integrity. It checks the data volume and whether there are any missing data, and issues an alert if there is a shortage.

[1180] Input: Data sent from the terminal

[1181] Output: Data integrity check results, alerts if there are any missing data

[1182] What happens: The server checks the format and size of the data to make sure there is no invalid data mixed in. If there is a problem, it generates an error log.

[1183] Step 5:

[1184] Server: Analyzes the received data using batch processing, identifies each stage of sleep (deep sleep, light sleep, REM sleep, etc.), and calculates total sleep time, deep sleep time, snoring frequency, etc.

[1185] Input: Integrity-checked biometric data

[1186] Output: Analysis results (total sleep time, deep sleep time, snoring frequency, etc.)

[1187] How it works: The analysis algorithm analyzes the data over time and calculates each indicator. The results are stored in a database.

[1188] Step 6:

[1189] Server: Based on the analysis results, it generates personalized advice. For example, if deep sleep time is short, it recommends stretching.

[1190] Input: Analysis results

[1191] Output: personalized advice

[1192] Specific operation: The advice generation algorithm selects the most appropriate advice based on the analysis results and creates an advice list.

[1193] Step 7:

[1194] Server: Generates a detailed action plan based on the health goals set by the user, suggesting daily activities such as stretching and meditation.

[1195] Input: User's health goals and analysis results

[1196] Output: Detailed action plan

[1197] Specific operation: The goal management module refers to the user's goals, and the plan generation algorithm creates an individual action plan.

[1198] Step 8:

[1199] Terminal: Receives advice and action plans from the server and notifies the user using alarms and pop-up messages.

[1200] Input: Advice and action plan sent from the server

[1201] Output: User notification

[1202] What it does: The app receives a push notification, displays it as a pop-up on the screen, and notifies you by adding a reminder to your calendar.

[1203] Step 9:

[1204] User: Review the provided advice and action plan and act as instructed. You can also record your action history in the app.

[1205] Input: Notification content from the terminal (advice and action plan)

[1206] Output: Record of user actions and action history

[1207] Specific operation: The user taps the "Action Completed" button to record the day's achievements in the app, allowing the server to monitor progress.

[1208] (Application example 1)

[1209] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1210] In modern society, many people suffer from poor sleep quality and irregular lifestyles. This leads to issues such as a deterioration in health and a lower quality of life. In addition, there is a lack of appropriate meal plans based on sleep and health status, making it difficult to consume meals tailored to individual health conditions. To solve these issues, a system that utilizes sleep data to achieve comprehensive health management is needed.

[1211] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1212] In this invention, the server includes means for collecting sleep data from a user, means for analyzing the collected sleep data, means for generating individual recommendations and action plans based on the analysis results, means for providing the generated recommendations and action plans to the user, means for proposing a meal plan suitable for the user based on the analysis results, and means for providing foods and dishes according to the proposed meal plan, thereby enabling health management based on sleep data and provision of meal plans linked thereto.

[1213] "Sleep data" refers to general information related to the user's sleep, and specifically includes data such as total sleep time, deep sleep time, and frequency of snoring.

[1214] "Analysis results" refer to conclusions or information about the user's sleep quality or health condition obtained by analyzing the collected sleep data.

[1215] "Recommendations" are specific advice or improvement measures provided to users based on the analysis results.

[1216] An "action plan" is a specific action or plan that a user should take based on a recommendation.

[1217] A "meal plan" is a nutritionally balanced and appropriate meal plan designed based on the user's health status.

[1218] "Means for providing food or meals" refers to a method or system that physically delivers food or meals appropriate to the user according to the proposed meal plan.

[1219] "Health goal" refers to a health goal that a user wishes to achieve, such as weight management, stress reduction, or establishing an exercise habit.

[1220] The "daily schedule" refers to the user's daily plans and timetable, and by adjusting this, guidance is given on how to use time efficiently.

[1221] The present invention provides a system for helping users achieve better quality sleep and improve their overall health and well-being. The system collects sleep data from users, analyzes the data, and provides personalized advice, action plans, and even appropriate diet plans. Specific embodiments of the system are described below.

[1222] System configuration

[1223] This system consists of a terminal used by a user, a server for processing data, and an application for linking these.

[1224] Terminal

[1225] The device includes hardware and software for collecting the user's sleep data, including the following:

[1226] Accelerometer: Detects the user's movements and records movement data while sleeping.

[1227] Microphone: Records sounds while you sleep (snoring and surrounding noises).

[1228] Application: Collects and transmits sleep data, and displays recommendations, action plans, and meal plans from the server.

[1229] server

[1230] The server is the central processing unit for receiving and analyzing the collected data. This server includes the following functions:

[1231] Data Analysis: Run an algorithm to analyze the collected sleep data, for example using Python.

[1232] Recommendation generation: Generates advice and action plans appropriate for the user based on the analysis results.

[1233] Meal plan generation: Proposes a nutritionally balanced meal plan based on the user's health status.

[1234] Delivery Order: A means of delivering food and dishes according to a proposed meal plan.

[1235] Application (software)

[1236] The system application provides an interface for users to operate the terminal and collect, send, receive, and display data. The specific functions are as follows:

[1237] Data Collection: Sleep data is collected from the user and sent to the server.

[1238] Recommendation display: Advice, action plans, and meal plans from the server are displayed to the user.

[1239] Goal setting: Users can set goals for health, stress management, exercise, etc. and send them to the server.

[1240] Delivery Order Management: Use delivery services based on proposed meal plans.

[1241] Program processing

[1242] Data collection and transmission

[1243] The user launches the app on their device at night and activates sleep mode. This allows the device to collect sleep data using the accelerometer and microphone, and store it in a buffer. When the user reopens the app the next morning, the collected data is sent to the server.

[1244] Receiving and analyzing data

[1245] The server receives the collected data, first verifies its integrity, and then analyzes it using specific algorithms to calculate metrics such as total sleep time, deep sleep time, and snoring frequency.

[1246] Generate recommendations and meal plans

[1247] The server generates personalized recommendations based on the analysis results, and also suggests nutritionally balanced meal plans based on the user's health status, for example, suggesting a high-protein breakfast to a sleep-deprived user.

[1248] Fulfilling delivery orders

[1249] The application automatically places food and meal delivery orders based on the generated meal plan, allowing the user to receive the food and meals according to the proposed meal plan.

[1250] Examples of concrete examples and prompts

[1251] As a specific example, a user launches an app on their device at 11 PM and sets it to sleep mode. The device collects movement data and sounds throughout the night, and when the user reopens the app at 7 AM the next morning, the data is sent to the server. The server analyzes the total sleep time to be 7 hours, the deep sleep time to be 2 hours and 30 minutes, and the snoring frequency to be 10 times. As a result, it generates recommendations to the user, such as "stretching before going to bed at night" and "using a humidifier to maintain humidity in the bedroom." In addition, based on the user's health condition, it recommends a "high-protein breakfast," and appropriate food is delivered.

[1252] Example prompt sentence:

[1253] The user's sleep data for the past 7 days is as follows: Total sleep time: 6 hours on average, Deep sleep time: 2 hours on average, Snoring frequency: 15 times on average. Based on this, provide appropriate diet and lifestyle recommendations to maintain good health.

[1254] This not only allows users to get quality sleep, but also allows them to eat a nutritionally balanced diet that is appropriate for their health condition.

[1255] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1256] Step 1:

[1257] Bedtime mode settings

[1258] At night, the user launches the app on their device and sets it to sleep mode, which prepares the device to start collecting motion data and sounds.

[1259] Input: User launches app and sets Bedtime mode

[1260] Output: The device switches to data collection mode

[1261] Step 2:

[1262] Data collection

[1263] The device's accelerometer and microphone continuously collect motion data and sounds (such as snoring and ambient noise) and store them in a buffer.

[1264] Input: User motion data and sound

[1265] Output: Buffered sleep data (motion data and sound)

[1266] Step 3:

[1267] Sending data

[1268] The next morning, when the user reopens the app, the saved sleep data is sent to the server.

[1269] Input: Buffered sleep data

[1270] Output: Sleep data sent to the server

[1271] Step 4:

[1272] Data Receipt and Validation

[1273] The server checks the integrity of the received data, verifying that there are no missing data or outliers.

[1274] Input: Sleep data sent to the server

[1275] Output: Verified sleep data

[1276] Step 5:

[1277] Data analysis

[1278] The server analyzes the verified data using a specific algorithm, calculating indicators such as total sleep time, deep sleep time, and snoring frequency to evaluate the user's sleep quality.

[1279] Input: Verified sleep data

[1280] Output: Analysis results (total sleep time, deep sleep time, snoring frequency, etc.)

[1281] Step 6:

[1282] Generating recommendations

[1283] Based on the analysis results, the server generates personalized recommendations and action plans, such as "avoid caffeine late at night" or "adjust the humidity in your bedroom."

[1284] Input: Analysis results

[1285] Output: personalized recommendations and action plans

[1286] Step 7:

[1287] Generate a meal plan

[1288] Based on the analysis results, the server proposes a suitable meal plan for the user, for example, recommending a high-protein breakfast for a sleep-deprived user.

[1289] Input: Analysis results

[1290] Output: Suitable meal plan

[1291] Step 8:

[1292] View recommendations and meal plans

[1293] The terminal displays the recommendations and meal plans received from the server to the user.

[1294] Input: Recommendations and Meal Plans

[1295] Output: Recommendations and meal plans displayed on the device screen

[1296] Step 9:

[1297] Fulfilling delivery orders

[1298] Once the user agrees to the meal plan, the device automatically sends a delivery order to the server, and the food or meal is delivered to the user.

[1299] Input: User consent

[1300] Output: Sending delivery orders and delivering food and dishes

[1301] Through these steps, users can ensure they get good quality sleep and eat a diet that is optimal for their health.

[1302] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1303] The present invention provides a system for ensuring a user's quality sleep and improving their overall health and well-being. The system collects and analyzes sleep and emotional data from the user, and provides personalized advice and action plans based on the collected data. Specific embodiments are described in detail below.

[1304] System configuration

[1305] This system consists of the following elements:

[1306] 1. Smartphone (device)

[1307] The terminal includes hardware and software for collecting the user's sleep and emotion data.

[1308] Acceleration sensor: Detects the user's movements and records movement data while sleeping.

[1309] Microphone: Records sounds while you sleep (snoring and surrounding noises).

[1310] Camera: Collects user emotion data through facial expression analysis.

[1311] Application: Collects and transmits sleep and emotional data, and displays recommendations and action plans from the server.

[1312] 2. Server

[1313] The server is a central processing unit for receiving and analyzing the collected data.

[1314] Data Analysis: Running algorithms to process sleep and emotion data.

[1315] Recommendation generation: Generates advice and action plans appropriate for the user based on the analysis results.

[1316] Emotion Engine: Recognizes user emotions and performs emotion-based analysis and suggestions.

[1317] 3. Applications (Software)

[1318] It provides an interface on a smartphone operated by the user, and collects, sends, receives, and displays data.

[1319] Data collection: Sleep data and emotion data are collected from the user and sent to the server.

[1320] Recommendation display: Displaying advice and action plans from the server to the user.

[1321] Goal setting: Users can set goals for health, stress management, exercise, etc. and send them to the server.

[1322] Program processing

[1323] Collection and transmission of sleep and emotional data

[1324] User: The smartphone app is started at night, sleep mode is set, facial expression analysis function is enabled, and the emotion engine is running.

[1325] Device: The acceleration sensor and microphone are activated to continuously collect data while the user is sleeping. The camera analyzes the user's facial expressions and collects emotional data.

[1326] Receiving and analyzing data

[1327] On the device: The next morning, when the user wakes up and reopens the app, the collected data is sent to the server.

[1328] Server: Receives the collected data and first verifies the data integrity. Then, it analyzes the data using algorithms to calculate metrics such as total sleep time, deep sleep time, light sleep time, and snoring frequency. In addition, it analyzes the user's emotional data using an emotion engine.

[1329] Generate recommendations and action plans

[1330] Server: Based on the analysis results, it generates different recommendations for each user. For example, if a user snores a lot, it recommends using a humidifier to maintain appropriate humidity in the bedroom. Based on emotional data, if the user is under a lot of stress, it suggests meditating to relax before going to bed at night.

[1331] Server: Generates a detailed action plan based on the goals set by the user. For example, if the goal is to exercise 30 minutes a day, the server will suggest specific exercise content and timing.

[1332] User notification and assistance

[1333] Device: Displays the recommendations and action plans received from the server to the user. It also suggests optimal time management based on the user's daily schedule and sets reminders.

[1334] User: Opens the app, reviews the recommendations and action plans provided, and acts on them.

[1335] Specific examples

[1336] The user enables the emotion engine and sets the bedtime mode at 10 p.m. The device collects motion, sound, and emotion data throughout the night, and when the user reopens the app at 7 a.m. the next morning, the data is sent to the server. The server analyzes the data and determines that the total sleep time is 7 hours, the deep sleep time is 2 hours and 30 minutes, the snoring frequency is 10 times, and the stress level is high. As a result, the server generates and notifies the user of the recommendation to "meditate before bed and use a humidifier to maintain humidity in the bedroom." The user follows the suggested advice, improving their sleep quality and promoting their overall health.

[1337] This allows users to combine emotional data to receive more accurate advice and action plans to build healthy lifestyle habits.The present invention provides a comprehensive system for supporting users' sleep and emotional management based on the claims.

[1338] The processing flow will be explained below.

[1339] Step 1:

[1340] A user opens a smartphone app, sets the sleep mode, enables the facial expression analysis function, and runs the emotion engine. At this time, the app checks for necessary permissions (such as access to the microphone, accelerometer, and camera).

[1341] Step 2:

[1342] The device checks the user's Bedtime setting and activates the accelerometer, microphone, and camera, ready to record the user's movements, sounds, and facial expressions in real time.

[1343] Step 3:

[1344] The device continuously collects motion and sound data from the user while they sleep. The acceleration sensor records the user's movements and movements, and the microphone records snoring and environmental sounds. The camera also records the user's facial expressions, which the emotion engine analyzes to generate emotion data.

[1345] Step 4:

[1346] The data collected by the terminal is stored in a buffer at regular intervals (for example, every second), which ensures data consistency and integrity.

[1347] Step 5:

[1348] When the user wakes up the next morning and opens their smartphone, the app prepares to send the data collected during the night to the server. The user taps the "Send Data" button to send the collected data to the server.

[1349] Step 6:

[1350] The server receives the data sent by the user and checks the data for completeness and consistency. If there are any missing or inconsistent data, it notifies the user and asks them to resubmit.

[1351] Step 7:

[1352] The server runs a data analysis algorithm and emotion engine to analyze the collected sleep and emotion data, and calculates the following metrics:

[1353] Total sleep time

[1354] Deep sleep time

[1355] Light sleep duration

[1356] The frequency and frequency of snoring

[1357] Emotional fluctuation patterns

[1358] Step 8:

[1359] The server then generates a report for each user based on the analysis results, which includes the aforementioned metrics and provides an overall assessment of the user's sleep performance.

[1360] Step 9:

[1361] Based on the generated report, the server generates specific recommendations and action plans suited to the user. For example, if the user snores a lot, it may suggest using a humidifier to maintain appropriate humidity, or if the user is under high stress based on emotional data, it may advise them to practice meditation to relax before going to bed at night.

[1362] Step 10:

[1363] The server receives the user's goal setting and generates a customized action plan accordingly. For example, if the goal is set to "exercise 30 minutes every day," the server will suggest the type and timing of exercise.

[1364] Step 11:

[1365] The device displays the reports, recommendations, and action plans received from the server to the user, and also sets reminders based on daily schedules to help users use their time efficiently.

[1366] Step 12:

[1367] The user opens the app, reviews the reports and recommendations provided, and plans specific steps to implement the proposed action plan and incorporate it into their daily activities.

[1368] Step 13:

[1369] The device periodically records the user's daily progress and emotional data and sends it to the server, which can then use the latest data to provide further recommendations and refine the action plan.

[1370] In this way, the system of the present invention collects and analyzes the user's sleep and emotional data, and provides personalized advice and action plans to improve the user's sleep quality and overall health. By implementing specific processing steps, the user can effectively manage their health.

[1371] Example 2

[1372] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1373] Conventional sleep management systems only collect and analyze users' sleep data and are unable to provide comprehensive recommendations that take into account the user's emotional state. As a result, users are unable to receive support in both appropriate sleep advice and emotional management, resulting in insufficient benefits for improving their overall health and well-being.

[1374] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1375] In this invention, the server includes means for collecting sleep data and emotional data from a user, means for verifying the completeness of the collected data, means for analyzing the data for total sleep time, deep sleep time, light sleep time, snoring frequency, and emotional state, means for generating individual recommendations and action plans based on the analysis results, and means for providing the generated recommendations and action plans to the user and setting reminders. This makes it possible to comprehensively analyze the user's sleep data and emotional data and provide optimal recommendations tailored to individual needs.

[1376] "User" refers to an individual who utilizes the system to provide sleep and emotional data.

[1377] "Sleep data" refers to information such as movement data, total sleep time, deep sleep time, light sleep time, and snoring frequency collected while the user is sleeping.

[1378] "Emotion data" refers to data on the user's emotional state obtained by analyzing their facial expressions or using an emotion engine.

[1379] "Means of collection" refers to the acceleration sensor, microphone, camera, and software that controls these devices installed on the user's device.

[1380] "Data Integrity Verification Measures" refers to the processes and algorithms used to verify that data received by the server is complete, free of errors and omissions.

[1381] "Means for analyzing" refers to algorithms or software for analyzing the collected sleep data and emotional data and assessing total sleep time, deep sleep time, light sleep time, snoring frequency, and emotional state.

[1382] "Recommendations" refers to specific lifestyle and behavioral advice provided to users based on the analysis results.

[1383] An "action plan" refers to a plan that includes specific actions and schedules that a user will take.

[1384] "Reminder" refers to a function that notifies users at the appropriate time to execute action plans or recommendations set by the user.

[1385] The present invention is a system for ensuring users' quality sleep and improving their overall health and well-being. The system collects and analyzes sleep and emotional data from users, and provides personalized advice and action plans based on the collected data.

[1386] System configuration

[1387] This system consists of the following elements:

[1388] 1. Smartphone (device)

[1389] The terminal includes hardware and software for collecting the user's sleep and emotion data.

[1390] Acceleration sensor: Detects the user's movements and records movement data while sleeping.

[1391] Microphone: Records sounds while you sleep (snoring and surrounding noises).

[1392] Camera: Collects user emotion data through facial expression analysis.

[1393] Application: Collects and transmits sleep and emotional data, and displays recommendations and action plans from the server.

[1394] 2. Server

[1395] The server is the central processing unit that receives and analyzes the collected data and provides specific recommendations to the user.

[1396] Data Analysis: Running algorithms to process sleep and emotion data.

[1397] Recommendation generation: Generates advice and action plans appropriate for the user based on the analysis results.

[1398] Emotion Engine: Recognizes user emotions and performs emotion-based analysis and suggestions.

[1399] 3. Applications (Software)

[1400] It provides an interface on the smartphone operated by the user, and collects, sends, receives, and displays data.

[1401] Data collection: Sleep data and emotion data are collected from the user and sent to the server.

[1402] Recommendation display: Displaying advice and action plans from the server to the user.

[1403] Goal setting: Users can set goals for health, stress management, exercise, etc. and send them to the server.

[1404] Specific examples

[1405] The user enables the emotion engine and sets the sleep mode at 10 p.m. The device collects motion, sound, and emotion data throughout the night, and when the user reopens the app at 7 a.m. the next morning, the data is sent to the server. The server verifies the completeness of the data and analyzes it. For example, the server determines that the total sleep time is 7 hours, the deep sleep time is 2 hours and 30 minutes, the snoring frequency is 10 times, and the stress level is high. As a result, the server generates and notifies the user of the recommendation to "meditate before bed and use a humidifier to maintain humidity in the bedroom." The user follows the suggested advice, improving their sleep quality and promoting their overall health.

[1406] Prompt Sentence Examples

[1407] "The user enables the emotion engine and sets bedtime mode at 10 p.m. The device collects motion data, sound data, and emotion data throughout the night. When the user reopens the app at 7 a.m. the next morning, the data is sent to the server. The server analyzes the data and determines that the total sleep time is 7 hours, the deep sleep time is 2 hours and 30 minutes, the snoring frequency is 10 times, and the stress level is high. As a result, the server generates a recommendation to 'meditate before going to bed at night and use a humidifier to maintain humidity in the bedroom' and notifies the user."

[1408] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1409] Step 1:

[1410] User sets Bedtime mode

[1411] Input: User input operations

[1412] How it works: A user sets up bedtime mode on a smartphone app and enables the emotion engine and facial expression analysis functions. Specifically, they tap "Bedtime mode" on the app's settings screen and turn on the emotion engine and facial expression analysis.

[1413] Output: The device is ready to collect data.

[1414] Step 2:

[1415] The device collects data

[1416] Input: User-defined bedtime mode

[1417] Operation: The device activates the accelerometer, microphone, and camera. The accelerometer continuously records the user's movements, and the microphone collects sounds in the bedroom. The camera analyzes the user's facial expressions and collects emotional data. This data is stored in temporary storage on the device.

[1418] Output: Collected sleep and emotion data

[1419] Step 3:

[1420] The device sends the data to the server

[1421] Input: Collected sleep and emotion data

[1422] How it works: The next morning, when the user reopens the smartphone app, data transmission begins. The device sends the data stored in its internal storage to the server. This transmission is encrypted for security reasons.

[1423] Output: Data sent to the server

[1424] Step 4:

[1425] The server verifies the integrity of the data

[1426] Input: Data sent from the terminal

[1427] Behavior: The server verifies the integrity of the data it receives, checking for missing or corrupted data and requesting retransmissions if necessary.

[1428] Output: Data with integrity checked

[1429] Step 5:

[1430] The server analyzes the data

[1431] Input: Integrity checked data

[1432] How it works: The server applies algorithms to analyze the data. First, it calculates sleep metrics such as total sleep time, deep sleep time, light sleep time, and snoring frequency. Second, it uses an emotion engine to analyze the user's emotional state.

[1433] Output: Analysis results of sleep data and emotion data

[1434] Step 6:

[1435] The server generates recommendations and action plans

[1436] Input: Analysis results

[1437] How it works: The server generates personalized recommendations for each user based on the analysis results. For example, if the deep sleep time is short, it may suggest "We recommend meditating every night before going to bed." If the user snores a lot, it may recommend "Using a humidifier to adjust the humidity in the bedroom." It also generates a detailed action plan based on the goals set by the user.

[1438] Output: Generated recommendations and action plans

[1439] Step 7:

[1440] The device notifies the user

[1441] Input: Generated recommendations and action plans

[1442] How it works: The device will notify you of the recommendations and action plans it receives from the server. The notification will include specific advice and instructions on how to implement it. Detailed instructions will also be provided within the app.

[1443] Output: User notification

[1444] Step 8:

[1445] The user executes the advice

[1446] Input: Notifications from your device

[1447] Action: The user sees the notification, opens the app to see the provided recommendation and action plan, and follows through on the specific advice offered (e.g., meditating or using a humidifier).

[1448] Output: Improved user health and sleep quality

[1449] (Application example 2)

[1450] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1451] In traditional food delivery services, the sleep and emotional state of delivery staff often have a significant impact on the quality of service. However, currently, there is no system that effectively utilizes this data to optimize staff health and performance. Furthermore, the lack of work shift and rest plans based on sleep and emotional data makes it difficult for delivery staff to manage stress and perform their work efficiently. Therefore, there is a need for a system that collects and analyzes sleep and emotional data and provides appropriate recommendations and action plans based on this data to improve staff health and service quality.

[1452] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting sleep data and emotion data from the user, means for analyzing the collected sleep data and emotion data, and means for generating individual recommendations and action plans based on the analysis results. This makes it possible to generate work shift and rest plans that optimize staff performance based on the collected data. The server also includes means for providing the generated recommendations and action plans to the user, means for generating work shift and rest plans to optimize the user's performance, and means for notifying the user of the generated work shift and rest plans. This solves the problem of improving the health and service quality of delivery staff.

[1453] "User" refers to a person who uses the system to provide data and receive recommendations and action plans.

[1454] "Sleep data" refers to data that includes information about a user's sleep patterns, sleep duration, deep sleep duration, light sleep duration, and other sleep-related information.

[1455] "Emotion data" is data that represents the user's emotional state, and includes information such as stress level and happiness level collected by facial expression analysis.

[1456] "Recommendations" refer to advice or specific instructions for action provided to users based on analysis results.

[1457] An "action plan" is a plan outlining specific actions or steps a user should take to optimize their health or performance.

[1458] A "work shift" refers to the allocation of working hours for the job a user is engaged in, and includes break times, start times, end times, etc.

[1459] A "rest plan" is a plan that includes a rest schedule and specific rest methods designed to optimize a user's rest.

[1460] "Data collection means" refers to the sensors, cameras, microphones and associated software used to acquire the user's sleep and emotional data.

[1461] "Data Analysis Means" refers to the algorithms and analytical software used to process the collected sleep and emotional data and assess the user's condition.

[1462] "Notification vehicles" are digital interfaces or applications used to communicate recommendations, action plans, work shifts, and break plans to users.

[1463] This system collects and analyzes sleep and emotional data from delivery staff, and provides work shift and rest plans and personalized advice based on the results. This system is designed to optimize users' health and work performance.

[1464] System configuration

[1465] This system consists of the following elements:

[1466] 1. Terminal

[1467] The terminal includes hardware and software for collecting sleep and emotion data of delivery staff.

[1468] Acceleration sensor: Detects staff movement and records movement data while sleeping.

[1469] Microphone: Records sounds while you sleep (snoring and surrounding noises).

[1470] Camera: Collects staff emotional data through facial expression analysis.

[1471] Application: Collects and transmits sleep and emotional data, and displays recommendations and action plans from the server.

[1472] 2. Server

[1473] The server is a central processing unit for receiving and analyzing the collected data.

[1474] Data Analysis: Running algorithms to process sleep and emotion data.

[1475] Recommendation generation: Generate appropriate advice and action plans for staff based on the analysis results.

[1476] Work shift and break plan generation: Propose optimal work shift and break plans to staff.

[1477] 3. Applications (Software)

[1478] It provides an interface on a smartphone operated by the user, and collects, sends, receives, and displays data.

[1479] Data collection: Sleep data and emotion data are collected from staff and sent to the server.

[1480] Recommendation display: Display advice and action plans from the server to staff.

[1481] Goal setting: Staff can set goals for health, stress management, exercise, etc. and send them to the server.

[1482] Program processing

[1483] Data collection

[1484] The device uses an accelerometer and microphone to collect motion data and sounds from staff, and a camera to record emotional data, which is then sent to a server via an application.

[1485] Data analysis and recommendation generation

[1486] The server analyzes the collected data, assesses sleep patterns and emotional states, and generates personalized recommendations and specific action plans for staff, along with optimal work shift and break plans.

[1487] Specific examples

[1488] A delivery staff member launches the app at 10 p.m. and sets it to sleep mode. After a week, the app checks the collected data and finds that the average sleep time was 6.5 hours and that there were three days of high stress. The server generates advice to the staff member, such as "Get more sleep" and "Try to spend more time relaxing," and notifies them. It also suggests adjusting work shifts and adding breaks.

[1489] Prompt Sentence Examples

[1490] It is recommended that staff meditate before going to bed and use a humidifier to maintain humidity in the bedroom. We will propose appropriate shift and rest plans based on staff sleep and emotional data. Please advise us on what approach would be effective.

[1491] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1492] Step 1:

[1493] The terminal collects sleep data and emotion data from the user (delivery staff).

[1494] Input: Accelerometer, microphone, camera

[1495] Output: Sleep data (movement data, sound data such as snoring), emotion data (facial expression analysis results)

[1496] Specific operation: When the user goes to bed at night, the device is turned on and set to sleep mode. The device's acceleration sensor detects movement data, the microphone records sounds such as snoring, and the camera analyzes facial expressions to collect emotional data.

[1497] Step 2:

[1498] The terminal transmits the collected sleep data and emotion data to a server.

[1499] Input: Data collected in step 1

[1500] Output: Raw data sent to the server

[1501] Specific operation: When the user wakes up the next morning, the device automatically uploads the data to the server.

[1502] Step 3:

[1503] A server receives the collected sleep and emotion data and checks the data for completeness.

[1504] Input: Sleep data and emotion data sent from the device

[1505] Output: Complete dataset, checks for missing data and outliers

[1506] Specific operation: Data is stored in a database within the server, and an analysis program checks the integrity of the data.

[1507] Step 4:

[1508] The server performs data analysis based on the data whose integrity has been confirmed.

[1509] Input: Complete sleep and emotion data

[1510] Output: Analysis results (total sleep time, deep sleep time, light sleep time, snoring frequency, emotional state, etc.)

[1511] How it works: The analysis algorithm works to calculate multiple indicators (sleep patterns, emotional trends, etc.).

[1512] Step 5:

[1513] The server generates personalized recommendations and action plans based on the analysis results.

[1514] Input: Data analysis results

[1515] Output: Recommendations and action plans

[1516] Specific Actions: Generative AI models are used to generate appropriate suggestions, creating specific advice (e.g., "Get more sleep" or "Meditate to relax") and action plans.

[1517] Step 6:

[1518] The server transmits the generated recommendations and action plans to the terminal.

[1519] Input: Generated recommendations and action plans

[1520] Output: Suggestions sent to the device

[1521] Specific operation: The communication module operates and the generated proposal is pushed to the device.

[1522] Step 7:

[1523] The terminal presents the recommendations and action plans received from the server to the user.

[1524] Input: Recommendations and action plans sent by the server

[1525] Output: Advice and plan displayed to the user

[1526] Specific operation: The terminal application displays the received data, and the user confirms it.

[1527] Step 8:

[1528] The user acts on the presented recommendations and action plan.

[1529] Input: Recommendations and action plans displayed on the device

[1530] Output: Action taken (adjustment of break time, change of work shift, etc.)

[1531] Specific actions: The user follows the application's instructions to adjust their daily activities.

[1532] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1533] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1534] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1535] [Fourth embodiment]

[1536] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1537] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1538] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1539] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1540] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1541] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1542] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1543] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1544] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1545] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1546] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1547] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1548] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1549] The present invention relates to a system for helping users ensure good quality sleep and improve their overall health and well-being. The system collects sleep data from users, analyzes the data, and provides personalized advice and action plans. Specific embodiments of the system are described below.

[1550] System configuration

[1551] This system consists of a smartphone (terminal) used by the user, a server for processing data, and an application for linking these.

[1552] 1. Smartphone (device)

[1553] The device includes hardware and software for collecting the user's sleep data, including the following:

[1554] Accelerometer: Detects the user's movements and records movement data while sleeping.

[1555] Microphone: Records sounds while you sleep (snoring and surrounding noises).

[1556] Application: Collects and transmits sleep data, and displays recommendations and action plans from the server.

[1557] 2. Server

[1558] The server is the central processing unit for receiving and analyzing the collected data. This server includes the following functions:

[1559] Data Analysis: Run algorithms to analyze the collected sleep data.

[1560] Recommendation generation: Generates advice and action plans appropriate for the user based on the analysis results.

[1561] Goal setting support: Generates an action plan based on the health goals set by the user.

[1562] 3. Applications (Software)

[1563] The system application provides an interface for users to operate on their smartphones and collect, send, receive, and display data. Specific functions are as follows:

[1564] Data Collection: Sleep data is collected from the user and sent to the server.

[1565] Recommendation display: Displaying advice and action plans from the server to the user.

[1566] Goal setting: Users can set goals for health, stress management, exercise, etc. and send them to the server.

[1567] Program processing

[1568] Sleep data collection and transmission

[1569] User: At night, launch the smartphone app and set it to sleep mode, which prepares the device to record motion data and sounds while sleeping.

[1570] Device: When the user goes to sleep, the accelerometer and microphone continuously collect data and store it in a buffer.

[1571] Receiving and analyzing data

[1572] Device: The next morning, when the user starts up their smartphone, the collected data is sent to the server.

[1573] Server: Receives the collected data and first verifies the data integrity, then analyzes the data using specific algorithms to calculate metrics such as total sleep time, deep sleep time, light sleep time, and snoring frequency.

[1574] Generate recommendations and action plans

[1575] Server: Based on the analysis results, the server generates recommendations that are unique to each user. These recommendations include specific advice such as "stretch before bed" or "adjust the humidity in your bedroom."

[1576] Server: Generates a detailed action plan based on the goals set by the user. For example, for the goal of "exercising 30 minutes every day", the server suggests the type and timing of exercise.

[1577] User notification and assistance

[1578] Device: Displays the recommendations and action plans received from the server to the user. It also suggests optimal ways to spend time based on the user's daily schedule and sets reminders.

[1579] User: Review and act on the recommendations and action plans provided.

[1580] Specific examples

[1581] At 11 PM, the user launches a smartphone app and sets it to sleep mode. The device collects movement data and sounds throughout the night, and when the user reopens the app at 7 AM the next morning, the data is sent to the server. The server analyzes the total sleep time to be 7 hours, the deep sleep time to be 2 hours and 30 minutes, and the snoring frequency to be 10 times. As a result, the server generates recommendations to the user, such as "stretching before going to bed at night" and "using a humidifier to maintain humidity in the bedroom." The user follows the advice provided to improve their lifestyle habits and gradually achieve their goals.

[1582] This allows users to get better quality sleep and improve their overall health and well-being. The present invention provides a system for achieving such multifaceted health management based on the claims.

[1583] The processing flow will be explained below.

[1584] Step 1:

[1585] The user opens an app on their smartphone and sets Bedtime mode, then checks the permissions the app needs to use to function properly (such as using the microphone or accelerometer).

[1586] Step 2:

[1587] The device checks the user's Bedtime setting and activates the accelerometer and microphone, preparing to record the user's movements and sounds in real time.

[1588] Step 3:

[1589] The device continuously collects user movement data and environmental sounds while the user sleeps. The accelerometer records the user's movements and movements, and the microphone records snoring and environmental sounds. This data is stored in a buffer at regular intervals (e.g., every second).

[1590] Step 4:

[1591] When the user wakes up the next morning and opens their smartphone, the app will self-check the collected data and prepare it to be sent to the server. The user can then tap the "Send Data" button to send the collected data to the server.

[1592] Step 5:

[1593] The server receives the data sent by the user and checks the data for completeness and consistency. If there are no missing or inconsistent data, it proceeds to the next step.

[1594] Step 6:

[1595] The server runs a data analysis algorithm to analyze the collected sleep data, specifically calculating the following metrics:

[1596] Total sleep time

[1597] Deep sleep time

[1598] Light sleep duration

[1599] The frequency and frequency of snoring

[1600] Step 7:

[1601] The server then generates a report for each user based on the analysis results, which includes the aforementioned metrics and provides an overall assessment of the user's sleep performance.

[1602] Step 8:

[1603] The server uses the generated report to generate specific recommendations and action plans for the user, which, depending on the analysis results, may include advice such as:

[1604] Relaxation techniques to do before bed

[1605] Adjusting the bedroom environment (temperature, humidity, sound)

[1606] Daytime activities (exercise, caffeine intake, etc.)

[1607] Step 9:

[1608] The server receives the user's goal settings and generates a customized action plan based on them. For example, if the goal is "30 minutes of exercise every day," the server will suggest the type of exercise and timing.

[1609] Step 10:

[1610] The device displays the reports, recommendations, and action plans received from the server to the user. The app notifies the user that a new report is available.

[1611] Step 11:

[1612] The user opens the app, reviews the reports and recommendations provided, and plans specific steps to implement the proposed action plan.

[1613] Step 12:

[1614] The device manages the user's daily schedule and sets reminders to help them use their time efficiently. For example, if the user sets a goal of "going to bed at 10 p.m.", the device will display a notification such as "take some time to relax at 9:30 p.m."

[1615] Through these processing steps, users receive specific advice and action plans to improve their sleep quality and build healthy lifestyle habits.

[1616] Example 1

[1617] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1618] In modern society, many people suffer from poor sleep quality due to excessive stress and irregular lifestyles. Furthermore, it is often difficult to obtain specific advice and action plans tailored to individual health conditions, making it difficult to find effective solutions. This invention aims to improve users' sleep quality and overall health by collecting and analyzing their sleep and health data to provide more effective, individually tailored advice and action plans.

[1619] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1620] In this invention, the server includes a device for collecting biometric data from a user, a device for analyzing the collected biometric data, and a device for generating individual advice and action plans based on the analysis results, thereby making it possible to provide optimal advice and action plans to the user.

[1621] "User" refers to an individual who uses this system.

[1622] "Biometric data" is a general term for various data that indicate the user's sleep state and health condition, and specifically includes sleep time, deep sleep time, snoring frequency, and the like.

[1623] "Device" refers to hardware or software for performing a particular function.

[1624] "Analysis" refers to the process of processing collected biometric data and converting it into meaningful information (e.g., indicators of sleep quality or health status).

[1625] "Advice" refers to specific advice based on the analysis results to improve the user's sleep and health.

[1626] An "action plan" refers to a specific implementation plan created based on the user's health status and goals, and includes, for example, daily exercise routines and pre-sleep routines.

[1627] "Providing" refers to the act of informing or displaying advice or a course of action to a user.

[1628] The present invention relates to a system for helping users achieve better quality sleep and improve their overall health and well-being by collecting biometric data from the user, analyzing that data, and providing personalized advice and action plans.

[1629] System configuration

[1630] This system consists of a terminal (smartphone) used by the user, a server for processing data, and an application for linking these.

[1631] 1. Device (smartphone)

[1632] The terminal includes hardware and software for collecting biometric data of the user, specifically the following elements:

[1633] Accelerometer: Detects the user's movements and records movement data while sleeping.

[1634] Microphone: Records sounds while you sleep (snoring and surrounding noises).

[1635] Application: Collects and transmits biometric data and displays advice and action plans from the server.

[1636] 2. Server

[1637] The server is a central processing unit for receiving and analyzing the collected data. The server includes the following functions:

[1638] Data analysis: The system runs algorithms that analyze the collected biometric data, which calculates things like total sleep time, deep sleep time, and snoring frequency.

[1639] Advice generation: Based on the analysis results, appropriate advice and action plans are generated for the user, including specific advice such as "stretch before going to bed" and "adjust the humidity in your bedroom."

[1640] Goal setting support: Generates a detailed action plan based on the health goals set by the user. For example, for a goal of "exercising 30 minutes daily," the system suggests the type and timing of exercise.

[1641] 3. Applications (Software)

[1642] The system application provides an interface for users to operate on their smartphones and collect, send, receive, and display data. Specific functions are as follows:

[1643] Data collection: Biometric data is collected from the user and sent to the server.

[1644] Advice display: displays advice and action plans from the server to the user.

[1645] Goal setting: Users can set goals for health, stress management, exercise, etc. and send them to the server.

[1646] Specific examples

[1647] At 11 PM, the user launches a smartphone app and sets it to sleep mode. The device collects movement data and sounds throughout the night, and when the user reopens the app at 7 AM the next morning, the collected data is sent to the server. The server obtains the analysis results of 7 hours of total sleep, 2.5 hours of deep sleep, and 10 snoring episodes. As a result, the server generates and notifies the user of advice such as "stretching before going to bed at night" and "using a humidifier to maintain humidity in the bedroom." The user follows the advice to improve their lifestyle habits and gradually achieve their goals.

[1648] Prompt Sentence Examples

[1649] Explain the processing procedure of the following system, and write it so that the subject is either the server, the terminal, or the user. Also, add a concrete example.

[1650] (system):

[1651] This system helps users ensure good quality sleep and improve their overall health and well-being. The system collects biometric data from users, analyzes that data, and provides personalized advice and action plans. The system consists of:

[1652] 1. Device (smartphone)

[1653] Acceleration sensor

[1654] microphone

[1655] application

[1656] 2. Server

[1657] Data analysis

[1658] Advice Generation

[1659] Goal setting support

[1660] 3. Applications (Software)

[1661] Data collection

[1662] Advisory Display

[1663] goal setting

[1664] (Example):

[1665] At 11 PM, the user launches a smartphone app and sets it to sleep mode. The device collects movement data and sounds throughout the night, and when the user reopens the app at 7 AM the next morning, the data is sent to the server. The server analyzes the total sleep time to be 7 hours, the deep sleep time to be 2 hours and 30 minutes, and the snoring frequency to be 10 times. As a result, the server generates and notifies the user of advice such as "stretching before going to bed at night" and "using a humidifier to maintain humidity in the bedroom." The user follows the advice provided to improve their lifestyle habits and gradually achieve their goals.

[1666] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1667] Step 1:

[1668] User: At night, the user launches the smartphone app and sets it to sleep mode. At this point, the app activates the accelerometer and microphone, and is ready to record data.

[1669] Input: User operation (launching an app, setting sleep mode)

[1670] Output: The device's sensors and microphone enter data collection mode.

[1671] What it does: When the user taps the app's "Bedtime Mode" button, the app activates the accelerometer and microphone, preparing to start recording data.

[1672] Step 2:

[1673] Device: When the user goes to sleep, the accelerometer records the user's movements and the microphone records sounds (snoring and sounds from the surrounding environment). These data are stored in a buffer at regular intervals.

[1674] Input: User movement, sounds while sleeping

[1675] Output: Buffered motion and audio data

[1676] How it works: The accelerometer detects user movement every minute and records the time and intensity of any movement. The microphone measures the decibels of sound every second and records an event if the decibel level exceeds a certain level.

[1677] Step 3:

[1678] Device: The next morning, when the user turns on their smartphone and opens the app, all the data collected overnight is sent to the server.

[1679] Input: Sleep motion and voice data collected overnight

[1680] Output: Data sent to the server

[1681] Specific operation: When the user taps the "Send Data" button in the app, the app will upload the data to the server via an Internet connection.

[1682] Step 4:

[1683] Server: Receives data from the device and first checks the data integrity. It checks the data volume and whether there are any missing data, and issues an alert if there is a shortage.

[1684] Input: Data sent from the terminal

[1685] Output: Data integrity check results, alerts if there are any missing data

[1686] What happens: The server checks the format and size of the data to make sure there is no invalid data mixed in. If there is a problem, it generates an error log.

[1687] Step 5:

[1688] Server: Analyzes the received data using batch processing, identifies each stage of sleep (deep sleep, light sleep, REM sleep, etc.), and calculates total sleep time, deep sleep time, snoring frequency, etc.

[1689] Input: Integrity-checked biometric data

[1690] Output: Analysis results (total sleep time, deep sleep time, snoring frequency, etc.)

[1691] How it works: The analysis algorithm analyzes the data over time and calculates each indicator. The results are stored in a database.

[1692] Step 6:

[1693] Server: Based on the analysis results, it generates personalized advice. For example, if deep sleep time is short, it recommends stretching.

[1694] Input: Analysis results

[1695] Output: personalized advice

[1696] Specific operation: The advice generation algorithm selects the most appropriate advice based on the analysis results and creates an advice list.

[1697] Step 7:

[1698] Server: Generates a detailed action plan based on the health goals set by the user, suggesting daily activities such as stretching and meditation.

[1699] Input: User's health goals and analysis results

[1700] Output: Detailed action plan

[1701] Specific operation: The goal management module refers to the user's goals, and the plan generation algorithm creates an individual action plan.

[1702] Step 8:

[1703] Terminal: Receives advice and action plans from the server and notifies the user using alarms and pop-up messages.

[1704] Input: Advice and action plan sent from the server

[1705] Output: User notification

[1706] What it does: The app receives a push notification, displays it as a pop-up on the screen, and notifies you by adding a reminder to your calendar.

[1707] Step 9:

[1708] User: Review the provided advice and action plan and act as instructed. You can also record your action history in the app.

[1709] Input: Notification content from the terminal (advice and action plan)

[1710] Output: Record of user actions and action history

[1711] Specific operation: The user taps the "Action Completed" button to record the day's achievements in the app, allowing the server to monitor progress.

[1712] (Application example 1)

[1713] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1714] In modern society, many people suffer from poor sleep quality and irregular lifestyles. This leads to issues such as a deterioration in health and a lower quality of life. In addition, there is a lack of appropriate meal plans based on sleep and health status, making it difficult to consume meals tailored to individual health conditions. To solve these issues, a system that utilizes sleep data to achieve comprehensive health management is needed.

[1715] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1716] In this invention, the server includes means for collecting sleep data from a user, means for analyzing the collected sleep data, means for generating individual recommendations and action plans based on the analysis results, means for providing the generated recommendations and action plans to the user, means for proposing a meal plan suitable for the user based on the analysis results, and means for providing foods and dishes according to the proposed meal plan, thereby enabling health management based on sleep data and provision of meal plans linked thereto.

[1717] "Sleep data" refers to general information related to the user's sleep, and specifically includes data such as total sleep time, deep sleep time, and frequency of snoring.

[1718] "Analysis results" refer to conclusions or information about the user's sleep quality or health condition obtained by analyzing the collected sleep data.

[1719] "Recommendations" are specific advice or improvement measures provided to users based on the analysis results.

[1720] An "action plan" is a specific action or plan that a user should take based on a recommendation.

[1721] A "meal plan" is a nutritionally balanced and appropriate meal plan designed based on the user's health status.

[1722] "Means for providing food or meals" refers to a method or system that physically delivers food or meals appropriate to the user according to the proposed meal plan.

[1723] "Health goal" refers to a health goal that a user wishes to achieve, such as weight management, stress reduction, or establishing an exercise habit.

[1724] The "daily schedule" refers to the user's daily plans and timetable, and by adjusting this, guidance is given on how to use time efficiently.

[1725] The present invention provides a system for helping users achieve better quality sleep and improve their overall health and well-being. The system collects sleep data from users, analyzes the data, and provides personalized advice, action plans, and even appropriate diet plans. Specific embodiments of the system are described below.

[1726] System configuration

[1727] This system consists of a terminal used by a user, a server for processing data, and an application for linking these.

[1728] Terminal

[1729] The device includes hardware and software for collecting the user's sleep data, including the following:

[1730] Accelerometer: Detects the user's movements and records movement data while sleeping.

[1731] Microphone: Records sounds while you sleep (snoring and surrounding noises).

[1732] Application: Collects and transmits sleep data, and displays recommendations, action plans, and meal plans from the server.

[1733] server

[1734] The server is the central processing unit for receiving and analyzing the collected data. This server includes the following functions:

[1735] Data Analysis: Run an algorithm to analyze the collected sleep data, for example using Python.

[1736] Recommendation generation: Generates advice and action plans appropriate for the user based on the analysis results.

[1737] Meal plan generation: Proposes a nutritionally balanced meal plan based on the user's health status.

[1738] Delivery Order: A means of delivering food and dishes according to a proposed meal plan.

[1739] Application (software)

[1740] The system application provides an interface for users to operate the terminal and collect, send, receive, and display data. The specific functions are as follows:

[1741] Data Collection: Sleep data is collected from the user and sent to the server.

[1742] Recommendation display: Advice, action plans, and meal plans from the server are displayed to the user.

[1743] Goal setting: Users can set goals for health, stress management, exercise, etc. and send them to the server.

[1744] Delivery Order Management: Use delivery services based on proposed meal plans.

[1745] Program processing

[1746] Data collection and transmission

[1747] The user launches the app on their device at night and activates sleep mode. This allows the device to collect sleep data using the accelerometer and microphone, and store it in a buffer. When the user reopens the app the next morning, the collected data is sent to the server.

[1748] Receiving and analyzing data

[1749] The server receives the collected data, first verifies its integrity, and then analyzes it using specific algorithms to calculate metrics such as total sleep time, deep sleep time, and snoring frequency.

[1750] Generate recommendations and meal plans

[1751] The server generates personalized recommendations based on the analysis results, and also suggests nutritionally balanced meal plans based on the user's health status, for example, suggesting a high-protein breakfast to a sleep-deprived user.

[1752] Fulfilling delivery orders

[1753] The application automatically places food and meal delivery orders based on the generated meal plan, allowing the user to receive the food and meals according to the proposed meal plan.

[1754] Examples of concrete examples and prompts

[1755] As a specific example, a user launches an app on their device at 11 PM and sets it to sleep mode. The device collects movement data and sounds throughout the night, and when the user reopens the app at 7 AM the next morning, the data is sent to the server. The server analyzes the total sleep time to be 7 hours, the deep sleep time to be 2 hours and 30 minutes, and the snoring frequency to be 10 times. As a result, it generates recommendations to the user, such as "stretching before going to bed at night" and "using a humidifier to maintain humidity in the bedroom." In addition, based on the user's health condition, it recommends a "high-protein breakfast," and appropriate food is delivered.

[1756] Example prompt sentence:

[1757] The user's sleep data for the past 7 days is as follows: Total sleep time: 6 hours on average, Deep sleep time: 2 hours on average, Snoring frequency: 15 times on average. Based on this, provide appropriate diet and lifestyle recommendations to maintain good health.

[1758] This not only allows users to get quality sleep, but also allows them to eat a nutritionally balanced diet that is appropriate for their health condition.

[1759] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1760] Step 1:

[1761] Bedtime mode settings

[1762] At night, the user launches the app on their device and sets it to sleep mode, which prepares the device to start collecting motion data and sounds.

[1763] Input: User launches app and sets Bedtime mode

[1764] Output: The device switches to data collection mode

[1765] Step 2:

[1766] Data collection

[1767] The device's accelerometer and microphone continuously collect motion data and sounds (such as snoring and ambient noise) and store them in a buffer.

[1768] Input: User motion data and sound

[1769] Output: Buffered sleep data (motion data and sound)

[1770] Step 3:

[1771] Sending data

[1772] The next morning, when the user reopens the app, the saved sleep data is sent to the server.

[1773] Input: Buffered sleep data

[1774] Output: Sleep data sent to the server

[1775] Step 4:

[1776] Data Receipt and Validation

[1777] The server checks the integrity of the received data, verifying that there are no missing data or outliers.

[1778] Input: Sleep data sent to the server

[1779] Output: Verified sleep data

[1780] Step 5:

[1781] Data analysis

[1782] The server analyzes the verified data using a specific algorithm, calculating indicators such as total sleep time, deep sleep time, and snoring frequency to evaluate the user's sleep quality.

[1783] Input: Verified sleep data

[1784] Output: Analysis results (total sleep time, deep sleep time, snoring frequency, etc.)

[1785] Step 6:

[1786] Generating recommendations

[1787] Based on the analysis results, the server generates personalized recommendations and action plans, such as "avoid caffeine late at night" or "adjust the humidity in your bedroom."

[1788] Input: Analysis results

[1789] Output: personalized recommendations and action plans

[1790] Step 7:

[1791] Generate a meal plan

[1792] Based on the analysis results, the server proposes a suitable meal plan for the user, for example, recommending a high-protein breakfast for a sleep-deprived user.

[1793] Input: Analysis results

[1794] Output: Suitable meal plan

[1795] Step 8:

[1796] View recommendations and meal plans

[1797] The terminal displays the recommendations and meal plans received from the server to the user.

[1798] Input: Recommendations and Meal Plans

[1799] Output: Recommendations and meal plans displayed on the device screen

[1800] Step 9:

[1801] Fulfilling delivery orders

[1802] Once the user agrees to the meal plan, the device automatically sends a delivery order to the server, and the food or meal is delivered to the user.

[1803] Input: User consent

[1804] Output: Sending delivery orders and delivering food and dishes

[1805] Through these steps, users can ensure they get good quality sleep and eat a diet that is optimal for their health.

[1806] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1807] The present invention provides a system for ensuring a user's quality sleep and improving their overall health and well-being. The system collects and analyzes sleep and emotional data from the user, and provides personalized advice and action plans based on the collected data. Specific embodiments are described in detail below.

[1808] System configuration

[1809] This system consists of the following elements:

[1810] 1. Smartphone (device)

[1811] The terminal includes hardware and software for collecting the user's sleep and emotion data.

[1812] Acceleration sensor: Detects the user's movements and records movement data while sleeping.

[1813] Microphone: Records sounds while you sleep (snoring and surrounding noises).

[1814] Camera: Collects user emotion data through facial expression analysis.

[1815] Application: Collects and transmits sleep and emotional data, and displays recommendations and action plans from the server.

[1816] 2. Server

[1817] The server is a central processing unit for receiving and analyzing the collected data.

[1818] Data Analysis: Running algorithms to process sleep and emotion data.

[1819] Recommendation generation: Generates advice and action plans appropriate for the user based on the analysis results.

[1820] Emotion Engine: Recognizes user emotions and performs emotion-based analysis and suggestions.

[1821] 3. Applications (Software)

[1822] It provides an interface on a smartphone operated by the user, and collects, sends, receives, and displays data.

[1823] Data collection: Sleep data and emotion data are collected from the user and sent to the server.

[1824] Recommendation display: Displaying advice and action plans from the server to the user.

[1825] Goal setting: Users can set goals for health, stress management, exercise, etc. and send them to the server.

[1826] Program processing

[1827] Collection and transmission of sleep and emotional data

[1828] User: The smartphone app is started at night, sleep mode is set, facial expression analysis function is enabled, and the emotion engine is running.

[1829] Device: The acceleration sensor and microphone are activated to continuously collect data while the user is sleeping. The camera analyzes the user's facial expressions and collects emotional data.

[1830] Receiving and analyzing data

[1831] On the device: The next morning, when the user wakes up and reopens the app, the collected data is sent to the server.

[1832] Server: Receives the collected data and first verifies the data integrity. Then, it analyzes the data using algorithms to calculate metrics such as total sleep time, deep sleep time, light sleep time, and snoring frequency. In addition, it analyzes the user's emotional data using an emotion engine.

[1833] Generate recommendations and action plans

[1834] Server: Based on the analysis results, it generates different recommendations for each user. For example, if a user snores a lot, it recommends using a humidifier to maintain appropriate humidity in the bedroom. Based on emotional data, if the user is under a lot of stress, it suggests meditating to relax before going to bed at night.

[1835] Server: Generates a detailed action plan based on the goals set by the user. For example, if the goal is to exercise 30 minutes a day, the server will suggest specific exercise content and timing.

[1836] User notification and assistance

[1837] Device: Displays the recommendations and action plans received from the server to the user. It also suggests optimal time management based on the user's daily schedule and sets reminders.

[1838] User: Opens the app, reviews the recommendations and action plans provided, and acts on them.

[1839] Specific examples

[1840] The user enables the emotion engine and sets the bedtime mode at 10 p.m. The device collects motion, sound, and emotion data throughout the night, and when the user reopens the app at 7 a.m. the next morning, the data is sent to the server. The server analyzes the data and determines that the total sleep time is 7 hours, the deep sleep time is 2 hours and 30 minutes, the snoring frequency is 10 times, and the stress level is high. As a result, the server generates and notifies the user of the recommendation to "meditate before bed and use a humidifier to maintain humidity in the bedroom." The user follows the suggested advice, improving their sleep quality and promoting their overall health.

[1841] This allows users to combine emotional data to receive more accurate advice and action plans to build healthy lifestyle habits.The present invention provides a comprehensive system for supporting users' sleep and emotional management based on the claims.

[1842] The processing flow will be explained below.

[1843] Step 1:

[1844] A user opens a smartphone app, sets the sleep mode, enables the facial expression analysis function, and runs the emotion engine. At this time, the app checks for necessary permissions (such as access to the microphone, accelerometer, and camera).

[1845] Step 2:

[1846] The device checks the user's Bedtime setting and activates the accelerometer, microphone, and camera, ready to record the user's movements, sounds, and facial expressions in real time.

[1847] Step 3:

[1848] The device continuously collects motion and sound data from the user while they sleep. The acceleration sensor records the user's movements and movements, and the microphone records snoring and environmental sounds. The camera also records the user's facial expressions, which the emotion engine analyzes to generate emotion data.

[1849] Step 4:

[1850] The data collected by the terminal is stored in a buffer at regular intervals (for example, every second), which ensures data consistency and integrity.

[1851] Step 5:

[1852] When the user wakes up the next morning and opens their smartphone, the app prepares to send the data collected during the night to the server. The user taps the "Send Data" button to send the collected data to the server.

[1853] Step 6:

[1854] The server receives the data sent by the user and checks the data for completeness and consistency. If there are any missing or inconsistent data, it notifies the user and asks them to resubmit.

[1855] Step 7:

[1856] The server runs a data analysis algorithm and emotion engine to analyze the collected sleep and emotion data, and calculates the following metrics:

[1857] Total sleep time

[1858] Deep sleep time

[1859] Light sleep duration

[1860] The frequency and frequency of snoring

[1861] Emotional fluctuation patterns

[1862] Step 8:

[1863] The server then generates a report for each user based on the analysis results, which includes the aforementioned metrics and provides an overall assessment of the user's sleep performance.

[1864] Step 9:

[1865] Based on the generated report, the server generates specific recommendations and action plans suited to the user. For example, if the user snores a lot, it may suggest using a humidifier to maintain appropriate humidity, or if the user is under high stress based on emotional data, it may advise them to practice meditation to relax before going to bed at night.

[1866] Step 10:

[1867] The server receives the user's goal setting and generates a customized action plan accordingly. For example, if the goal is set to "exercise 30 minutes every day," the server will suggest the type and timing of exercise.

[1868] Step 11:

[1869] The device displays the reports, recommendations, and action plans received from the server to the user, and also sets reminders based on daily schedules to help users use their time efficiently.

[1870] Step 12:

[1871] The user opens the app, reviews the reports and recommendations provided, and plans specific steps to implement the proposed action plan and incorporate it into their daily activities.

[1872] Step 13:

[1873] The device periodically records the user's daily progress and emotional data and sends it to the server, which can then use the latest data to provide further recommendations and refine the action plan.

[1874] In this way, the system of the present invention collects and analyzes the user's sleep and emotional data, and provides personalized advice and action plans to improve the user's sleep quality and overall health. By implementing specific processing steps, the user can effectively manage their health.

[1875] Example 2

[1876] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1877] Conventional sleep management systems only collect and analyze users' sleep data and are unable to provide comprehensive recommendations that take into account the user's emotional state. As a result, users are unable to receive support in both appropriate sleep advice and emotional management, resulting in insufficient benefits for improving their overall health and well-being.

[1878] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1879] In this invention, the server includes means for collecting sleep data and emotional data from a user, means for verifying the completeness of the collected data, means for analyzing the data for total sleep time, deep sleep time, light sleep time, snoring frequency, and emotional state, means for generating individual recommendations and action plans based on the analysis results, and means for providing the generated recommendations and action plans to the user and setting reminders. This makes it possible to comprehensively analyze the user's sleep data and emotional data and provide optimal recommendations tailored to individual needs.

[1880] "User" refers to an individual who utilizes the system to provide sleep and emotional data.

[1881] "Sleep data" refers to information such as movement data, total sleep time, deep sleep time, light sleep time, and snoring frequency collected while the user is sleeping.

[1882] "Emotion data" refers to data on the user's emotional state obtained by analyzing their facial expressions or using an emotion engine.

[1883] "Means of collection" refers to the acceleration sensor, microphone, camera, and software that controls these devices installed on the user's device.

[1884] "Data Integrity Verification Measures" refers to the processes and algorithms used to verify that data received by the server is complete, free of errors and omissions.

[1885] "Means for analyzing" refers to algorithms or software for analyzing the collected sleep data and emotional data and assessing total sleep time, deep sleep time, light sleep time, snoring frequency, and emotional state.

[1886] "Recommendations" refers to specific lifestyle and behavioral advice provided to users based on the analysis results.

[1887] An "action plan" refers to a plan that includes specific actions and schedules that a user will take.

[1888] "Reminder" refers to a function that notifies users at the appropriate time to execute action plans or recommendations set by the user.

[1889] The present invention is a system for ensuring users' quality sleep and improving their overall health and well-being. The system collects and analyzes sleep and emotional data from users, and provides personalized advice and action plans based on the collected data.

[1890] System configuration

[1891] This system consists of the following elements:

[1892] 1. Smartphone (device)

[1893] The terminal includes hardware and software for collecting the user's sleep and emotion data.

[1894] Acceleration sensor: Detects the user's movements and records movement data while sleeping.

[1895] Microphone: Records sounds while you sleep (snoring and surrounding noises).

[1896] Camera: Collects user emotion data through facial expression analysis.

[1897] Application: Collects and transmits sleep and emotional data, and displays recommendations and action plans from the server.

[1898] 2. Server

[1899] The server is the central processing unit that receives and analyzes the collected data and provides specific recommendations to the user.

[1900] Data Analysis: Running algorithms to process sleep and emotion data.

[1901] Recommendation generation: Generates advice and action plans appropriate for the user based on the analysis results.

[1902] Emotion Engine: Recognizes user emotions and performs emotion-based analysis and suggestions.

[1903] 3. Applications (Software)

[1904] It provides an interface on the smartphone operated by the user, and collects, sends, receives, and displays data.

[1905] Data collection: Sleep data and emotion data are collected from the user and sent to the server.

[1906] Recommendation display: Displaying advice and action plans from the server to the user.

[1907] Goal setting: Users can set goals for health, stress management, exercise, etc. and send them to the server.

[1908] Specific examples

[1909] The user enables the emotion engine and sets the sleep mode at 10 p.m. The device collects motion, sound, and emotion data throughout the night, and when the user reopens the app at 7 a.m. the next morning, the data is sent to the server. The server verifies the completeness of the data and analyzes it. For example, the server determines that the total sleep time is 7 hours, the deep sleep time is 2 hours and 30 minutes, the snoring frequency is 10 times, and the stress level is high. As a result, the server generates and notifies the user of the recommendation to "meditate before bed and use a humidifier to maintain humidity in the bedroom." The user follows the suggested advice, improving their sleep quality and promoting their overall health.

[1910] Prompt Sentence Examples

[1911] "The user enables the emotion engine and sets bedtime mode at 10 p.m. The device collects motion data, sound data, and emotion data throughout the night. When the user reopens the app at 7 a.m. the next morning, the data is sent to the server. The server analyzes the data and determines that the total sleep time is 7 hours, the deep sleep time is 2 hours and 30 minutes, the snoring frequency is 10 times, and the stress level is high. As a result, the server generates a recommendation to 'meditate before going to bed at night and use a humidifier to maintain humidity in the bedroom' and notifies the user."

[1912] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1913] Step 1:

[1914] User sets Bedtime mode

[1915] Input: User input operations

[1916] How it works: A user sets up bedtime mode on a smartphone app and enables the emotion engine and facial expression analysis functions. Specifically, they tap "Bedtime mode" on the app's settings screen and turn on the emotion engine and facial expression analysis.

[1917] Output: The device is ready to collect data.

[1918] Step 2:

[1919] The device collects data

[1920] Input: User-defined bedtime mode

[1921] Operation: The device activates the accelerometer, microphone, and camera. The accelerometer continuously records the user's movements, and the microphone collects sounds in the bedroom. The camera analyzes the user's facial expressions and collects emotional data. This data is stored in temporary storage on the device.

[1922] Output: Collected sleep and emotion data

[1923] Step 3:

[1924] The device sends the data to the server

[1925] Input: Collected sleep and emotion data

[1926] How it works: The next morning, when the user reopens the smartphone app, data transmission begins. The device sends the data stored in its internal storage to the server. This transmission is encrypted for security reasons.

[1927] Output: Data sent to the server

[1928] Step 4:

[1929] The server verifies the integrity of the data

[1930] Input: Data sent from the terminal

[1931] Behavior: The server verifies the integrity of the data it receives, checking for missing or corrupted data and requesting retransmissions if necessary.

[1932] Output: Data with integrity checked

[1933] Step 5:

[1934] The server analyzes the data

[1935] Input: Integrity checked data

[1936] How it works: The server applies algorithms to analyze the data. First, it calculates sleep metrics such as total sleep time, deep sleep time, light sleep time, and snoring frequency. Second, it uses an emotion engine to analyze the user's emotional state.

[1937] Output: Analysis results of sleep data and emotion data

[1938] Step 6:

[1939] The server generates recommendations and action plans

[1940] Input: Analysis results

[1941] How it works: The server generates personalized recommendations for each user based on the analysis results. For example, if the deep sleep time is short, it may suggest "We recommend meditating every night before going to bed." If the user snores a lot, it may recommend "Using a humidifier to adjust the humidity in the bedroom." It also generates a detailed action plan based on the goals set by the user.

[1942] Output: Generated recommendations and action plans

[1943] Step 7:

[1944] The device notifies the user

[1945] Input: Generated recommendations and action plans

[1946] How it works: The device will notify you of the recommendations and action plans it receives from the server. The notification will include specific advice and instructions on how to implement it. Detailed instructions will also be provided within the app.

[1947] Output: User notification

[1948] Step 8:

[1949] The user executes the advice

[1950] Input: Notifications from your device

[1951] Action: The user sees the notification, opens the app to see the provided recommendation and action plan, and follows through on the specific advice offered (e.g., meditating or using a humidifier).

[1952] Output: Improved user health and sleep quality

[1953] (Application example 2)

[1954] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1955] In traditional food delivery services, the sleep and emotional state of delivery staff often have a significant impact on the quality of service. However, currently, there is no system that effectively utilizes this data to optimize staff health and performance. Furthermore, the lack of work shift and rest plans based on sleep and emotional data makes it difficult for delivery staff to manage stress and perform their work efficiently. Therefore, there is a need for a system that collects and analyzes sleep and emotional data and provides appropriate recommendations and action plans based on this data to improve staff health and service quality.

[1956] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting sleep data and emotion data from the user, means for analyzing the collected sleep data and emotion data, and means for generating individual recommendations and action plans based on the analysis results. This makes it possible to generate work shift and rest plans that optimize staff performance based on the collected data. The server also includes means for providing the generated recommendations and action plans to the user, means for generating work shift and rest plans to optimize the user's performance, and means for notifying the user of the generated work shift and rest plans. This solves the problem of improving the health and service quality of delivery staff.

[1957] "User" refers to a person who uses the system to provide data and receive recommendations and action plans.

[1958] "Sleep data" refers to data that includes information about a user's sleep patterns, sleep duration, deep sleep duration, light sleep duration, and other sleep-related information.

[1959] "Emotion data" is data that represents the user's emotional state, and includes information such as stress level and happiness level collected by facial expression analysis.

[1960] "Recommendations" refer to advice or specific instructions for action provided to users based on analysis results.

[1961] An "action plan" is a plan outlining specific actions or steps a user should take to optimize their health or performance.

[1962] A "work shift" refers to the allocation of working hours for the job a user is engaged in, and includes break times, start times, end times, etc.

[1963] A "rest plan" is a plan that includes a rest schedule and specific rest methods designed to optimize a user's rest.

[1964] "Data collection means" refers to the sensors, cameras, microphones and associated software used to acquire the user's sleep and emotional data.

[1965] "Data Analysis Means" refers to the algorithms and analytical software used to process the collected sleep and emotional data and assess the user's condition.

[1966] "Notification vehicles" are digital interfaces or applications used to communicate recommendations, action plans, work shifts, and break plans to users.

[1967] This system collects and analyzes sleep and emotional data from delivery staff, and provides work shift and rest plans and personalized advice based on the results. This system is designed to optimize users' health and work performance.

[1968] System configuration

[1969] This system consists of the following elements:

[1970] 1. Terminal

[1971] The terminal includes hardware and software for collecting sleep and emotion data of delivery staff.

[1972] Acceleration sensor: Detects staff movement and records movement data while sleeping.

[1973] Microphone: Records sounds while you sleep (snoring and surrounding noises).

[1974] Camera: Collects staff emotional data through facial expression analysis.

[1975] Application: Collects and transmits sleep and emotional data, and displays recommendations and action plans from the server.

[1976] 2. Server

[1977] The server is a central processing unit for receiving and analyzing the collected data.

[1978] Data Analysis: Running algorithms to process sleep and emotion data.

[1979] Recommendation generation: Generate appropriate advice and action plans for staff based on the analysis results.

[1980] Work shift and break plan generation: Propose optimal work shift and break plans to staff.

[1981] 3. Applications (Software)

[1982] It provides an interface on a smartphone operated by the user, and collects, sends, receives, and displays data.

[1983] Data collection: Sleep data and emotion data are collected from staff and sent to the server.

[1984] Recommendation display: Display advice and action plans from the server to staff.

[1985] Goal setting: Staff can set goals for health, stress management, exercise, etc. and send them to the server.

[1986] Program processing

[1987] Data collection

[1988] The device uses an accelerometer and microphone to collect motion data and sounds from staff, and a camera to record emotional data, which is then sent to a server via an application.

[1989] Data analysis and recommendation generation

[1990] The server analyzes the collected data, assesses sleep patterns and emotional states, and generates personalized recommendations and specific action plans for staff, along with optimal work shift and break plans.

[1991] Specific examples

[1992] A delivery staff member launches the app at 10 p.m. and sets it to sleep mode. After a week, the app checks the collected data and finds that the average sleep time was 6.5 hours and that there were three days of high stress. The server generates advice to the staff member, such as "Get more sleep" and "Try to spend more time relaxing," and notifies them. It also suggests adjusting work shifts and adding breaks.

[1993] Prompt Sentence Examples

[1994] It is recommended that staff meditate before going to bed and use a humidifier to maintain humidity in the bedroom. We will propose appropriate shift and rest plans based on staff sleep and emotional data. Please advise us on what approach would be effective.

[1995] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1996] Step 1:

[1997] The terminal collects sleep data and emotion data from the user (delivery staff).

[1998] Input: Accelerometer, microphone, camera

[1999] Output: Sleep data (movement data, sound data such as snoring), emotion data (facial expression analysis results)

[2000] Specific operation: When the user goes to bed at night, the device is turned on and set to sleep mode. The device's acceleration sensor detects movement data, the microphone records sounds such as snoring, and the camera analyzes facial expressions to collect emotional data.

[2001] Step 2:

[2002] The terminal transmits the collected sleep data and emotion data to a server.

[2003] Input: Data collected in step 1

[2004] Output: Raw data sent to the server

[2005] Specific operation: When the user wakes up the next morning, the device automatically uploads the data to the server.

[2006] Step 3:

[2007] A server receives the collected sleep and emotion data and checks the data for completeness.

[2008] Input: Sleep data and emotion data sent from the device

[2009] Output: Complete dataset, checks for missing data and outliers

[2010] Specific operation: Data is stored in a database within the server, and an analysis program checks the integrity of the data.

[2011] Step 4:

[2012] The server performs data analysis based on the data whose integrity has been confirmed.

[2013] Input: Complete sleep and emotion data

[2014] Output: Analysis results (total sleep time, deep sleep time, light sleep time, snoring frequency, emotional state, etc.)

[2015] How it works: The analysis algorithm works to calculate multiple indicators (sleep patterns, emotional trends, etc.).

[2016] Step 5:

[2017] The server generates personalized recommendations and action plans based on the analysis results.

[2018] Input: Data analysis results

[2019] Output: Recommendations and action plans

[2020] Specific Actions: Generative AI models are used to generate appropriate suggestions, creating specific advice (e.g., "Get more sleep" or "Meditate to relax") and action plans.

[2021] Step 6:

[2022] The server transmits the generated recommendations and action plans to the terminal.

[2023] Input: Generated recommendations and action plans

[2024] Output: Suggestions sent to the device

[2025] Specific operation: The communication module operates and the generated proposal is pushed to the device.

[2026] Step 7:

[2027] The terminal presents the recommendations and action plans received from the server to the user.

[2028] Input: Recommendations and action plans sent by the server

[2029] Output: Advice and plan displayed to the user

[2030] Specific operation: The terminal application displays the received data, and the user confirms it.

[2031] Step 8:

[2032] The user acts on the presented recommendations and action plan.

[2033] Input: Recommendations and action plans displayed on the device

[2034] Output: Action taken (adjustment of break time, change of work shift, etc.)

[2035] Specific actions: The user follows the application's instructions to adjust their daily activities.

[2036] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2037] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2038] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2039] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2040] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2041] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2042] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2043] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2044] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2045] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2046] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2047] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2048] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2049] 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.

[2050] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2051] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2052] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2053] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2054] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2055] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2056] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2057] The following is further disclosed regarding the above embodiment.

[2058] (Claim 1)

[2059] means for collecting sleep data from a user;

[2060] a means for analyzing the collected sleep data;

[2061] a means for generating personalized recommendations and action plans based on the analysis results;

[2062] means for providing the generated recommendations and action plans to a user;

[2063] A system including:

[2064] (Claim 2)

[2065] A means for generating detailed action plans based on user-defined health, stress management, exercise, and other goals;

[2066] means for providing the generated action plan to a user;

[2067] 10. The system of claim 1, further comprising:

[2068] (Claim 3)

[2069] A means to help users adjust their daily schedules and use their time efficiently,

[2070] means for notifying a user of the adjusted schedule;

[2071] 10. The system of claim 1, further comprising:

[2072] "Example 1"

[2073] (Claim 1)

[2074] a device for collecting biometric data from a user;

[2075] a device for analyzing the collected biometric data;

[2076] a device for generating personalized advice and action plans based on the analysis results;

[2077] a device for providing the generated advice and action plan to a user;

[2078] A system including:

[2079] (Claim 2)

[2080] a device for generating a detailed action plan based on goals set by a user, such as health management, mental stress management, and physical activity;

[2081] a device for providing the generated action plan to a user;

[2082] 10. The system of claim 1, further comprising:

[2083] (Claim 3)

[2084] A device that adjusts a user's daily activity plan and supports efficient time usage;

[2085] a device for notifying a user of the adjusted action plan;

[2086] 10. The system of claim 1, further comprising:

[2087] "Application Example 1"

[2088] (Claim 1)

[2089] means for collecting sleep data from a user;

[2090] a means for analyzing the collected sleep data;

[2091] a means for generating personalized recommendations and action plans based on the analysis results;

[2092] means for providing the generated recommendations and action plans to a user;

[2093] A means for proposing a meal plan suitable for the user based on the analysis results;

[2094] means for providing food or dishes according to said proposed meal plan;

[2095] A system including:

[2096] (Claim 2)

[2097] A means for generating detailed action plans based on user-defined health, stress management, exercise, and other goals;

[2098] means for providing the generated action plan to a user;

[2099] 10. The system of claim 1, further comprising:

[2100] (Claim 3)

[2101] A means to help users adjust their daily schedules and use their time efficiently,

[2102] means for notifying a user of the adjusted schedule;

[2103] 10. The system of claim 1, further comprising:

[2104] "Example 2: Combining Emotion Engines"

[2105] (Claim 1)

[2106] means for collecting sleep data and emotion data from a user;

[2107] a means of verifying the integrity of the collected data;

[2108] The data will be used to analyze total sleep time, deep sleep time, light sleep time, snoring frequency, and emotional state.

[2109] a means for generating personalized recommendations and action plans based on the analysis results;

[2110] means for providing the generated recommendations and action plans to a user and setting reminders;

[2111] A system including:

[2112] (Claim 2)

[2113] A means for generating detailed action plans based on user-defined health, stress management, exercise, and other goals;

[2114] means for providing the generated action plan to a user;

[2115] 10. The system of claim 1, further comprising:

[2116] (Claim 3)

[2117] A means to help users adjust their daily schedules and use their time efficiently,

[2118] means for notifying a user of the adjusted schedule;

[2119] 10. The system of claim 1, further comprising:

[2120] "Application example 2 when combining emotion engines"

[2121] (Claim 1)

[2122] means for collecting sleep data and emotion data from a user;

[2123] means for analyzing the collected sleep data and emotion data;

[2124] a means for generating personalized recommendations and action plans based on the analysis results;

[2125] means for providing the generated recommendations and action plans to a user;

[2126] means for generating work shift and break plans to optimize a user's job performance;

[2127] means for notifying a user of the generated work shift and rest plan;

[2128] A system including:

[2129] (Claim 2)

[2130] means for generating a detailed action plan based on user-defined health, stress management, exercise, and other goals;

[2131] means for providing the generated action plan to a user;

[2132] 10. The system of claim 1, further comprising:

[2133] (Claim 3)

[2134] A means to help users adjust their daily schedules and use their time efficiently,

[2135] means for notifying a user of the adjusted schedule;

[2136] a means of providing specific advice to improve job performance;

[2137] A means for analyzing data collected from users according to their occupational characteristics;

[2138] A means for generating individualized advice using performance data and emotion data according to job characteristics;

[2139] 10. The system of claim 1, further comprising: [Explanation of symbols]

[2140] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for collecting sleep data from a user; a means for analyzing the collected sleep data; a means for generating personalized recommendations and action plans based on the analysis results; means for providing the generated recommendations and action plans to a user; A system including:

2. A means for generating detailed action plans based on user-defined health, stress management, exercise, and other goals; means for providing the generated action plan to a user; The system of claim 1 further comprising:

3. A means to help users adjust their daily schedules and use their time efficiently, means for notifying a user of the adjusted schedule; The system of claim 1 further comprising:

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