system

Abstract

Provide a system. The system includes a means for parents to input data regarding their child's daily activities; A means for collecting child health data from a wearable device; means for transmitting the collected data to a server; A method to analyze data on the server and calculate the optimal time to put the child to sleep. means for generating advice based on the calculated sleep time; a means for notifying the parent terminal of the generated advice; A system including:

Description

[Technical Field]

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

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

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

[0004] In modern society, children's sleep is a major issue for parents. While adequate sleep is essential for a child's health and development, determining the appropriate time to put them to bed can be difficult. It is particularly difficult for parents to determine the optimal time for putting their child to bed each day, as daily activity levels and physical condition can have an impact. To solve this issue, a system is needed that can monitor each child's individual condition in real time and provide accurate advice on the optimal time for putting their child to bed each day. [Means for solving the problem]

[0005] The present invention provides a means for parents to input data about their child's daily activities and collect data on the child's physical condition from a wearable device. The collected data is sent to a server, which analyzes the data and calculates the optimal time for putting the child to bed. Furthermore, advice based on the calculated time is generated and sent to the parent's device, allowing the parent to determine the optimal time for putting the child to bed that day. The system also includes a means for analyzing past data using machine learning algorithms to learn the child's sleep patterns, and a means for collecting data in real time and dynamically generating suggestions, enabling more accurate advice to be provided.

[0006] "Means for parents to input data on their children's daily activities" refers to a function that allows parents to input their children's daily activities, such as mealtimes, playtime, study time, and screen time, via devices such as smartphones or tablets.

[0007] "Means for collecting children's health data from wearable devices" refers to the function of collecting health data such as heart rate, body temperature, and number of steps in real time from wearable devices worn by children.

[0008] "Means for transmitting collected data to a server" refers to the function of transmitting collected daily activity data and physical condition data to a server via the Internet via a device such as a smartphone or tablet.

[0009] "Means for analyzing data on the server and calculating the optimal time for putting a child to bed" refers to the function of analyzing the data collected on the server and executing an algorithm to calculate the optimal time for putting a child to bed on that day.

[0010] "Means for generating advice based on the calculated time for putting a child to sleep" refers to a function that creates a specific action plan and advice based on the optimal time for putting a child to sleep calculated by the server.

[0011] "Means for notifying the parent's device of the generated advice" refers to a function that sends the generated advice as a notification message to the parent's device, such as a smartphone or tablet, to notify the parent.

[0012] "Means of using machine learning algorithms to analyze past data and learn a child's sleep patterns" refers to the function in which the server runs machine learning algorithms using past data to analyze and learn a child's sleep patterns and tendencies.

[0013] "Means for collecting data in real time and dynamically generating suggestions based on that data" refers to a function that continuously collects daily activity data and physical condition data in real time and generates suggestions in real time based on that data. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] MODE FOR CARRYING OUT THE INVENTION

[0036] This invention relates to a system that suggests the optimal time for putting a child to bed based on data on the child's daily activities and physical condition. The system is realized by having a server analyze information entered by the parent and data automatically collected from a wearable device, and then notifying the parent of the optimal time for putting the child to bed.

[0037] System Overview

[0038] 1. Data Collection

[0039] Parental input: Using a dedicated application, the "user" inputs data about their child's daily activities, such as mealtimes, playtime, naptime, study time, and screen time.

[0040] Wearable devices: Children wear wearable devices that collect real-time health data such as heart rate, body temperature, and number of steps taken. This data is automatically sent to a "terminal."

[0041] 2. Data transmission

[0042] All collected data is transmitted from the "terminal" to the "server" via the Internet.

[0043] 3. Data analysis and proposal generation

[0044] The server analyzes the collected data and calculates the optimal time for each child to go to bed. The server uses machine learning algorithms to learn a child's sleep patterns based on past data and predict the optimal time for that day.

[0045] The server generates specific sleep-related advice based on the analysis results, such as "To get your child to bed by 8:30 PM, it's best to take a bath before 8 PM."

[0046] 4. Sending notifications

[0047] The "server" sends the generated advice to the "terminal" and delivers it to the "user" as a push notification.

[0048] Specific examples

[0049] For example, consider a family with a three-year-old child.

[0050] 1. Parent Input and Data Collection

[0051] "Users" use the app to input their children's breakfast time (7:30), playtime (9:00-10:00), naptime (13:00-14:00), dinnertime (18:00), etc.

[0052] As the child wears the wearable device, the number of steps taken, heart rate, and body temperature for the day are automatically recorded on the "terminal."

[0053] 2. Data Transmission

[0054] These data are sent from the "terminal" to the "server."

[0055] 3. Data analysis and proposal generation

[0056] The server analyzes the data and takes into account the child's activity level and physical condition that day, calculating, for example, "Since the child was very active today, the optimal time to put the child to bed is 8:30 p.m."

[0057] The server also generates specific advice, such as "We recommend taking a bath from 7:30 p.m."

[0058] 4. Sending notifications

[0059] The "server" sends this advice to the "terminal", which then notifies the "user".

[0060] The "user" checks this notification, gives the child a bath at 7:30 p.m., and puts the child to bed at 8:30 p.m.

[0061] In this way, the system can calculate the optimal time for putting a child to bed based on the child's daily activities and physical condition data, and provide effective advice to parents in a timely manner, thereby helping parents ensure healthy sleep for their children and supporting their daily childcare.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] The "user" launches the smartphone app and inputs their child's daily activity data for the day, including mealtimes, playtime, naptime, screen time, and study time.

[0065] Step 2:

[0066] The "user" puts the wearable device on the child.

[0067] Step 3:

[0068] The "terminal" collects physical data such as heart rate, body temperature, and number of steps from the wearable device in real time.

[0069] Step 4:

[0070] The "terminal" compiles the collected daily activity data and physical condition data and sends it to the "server."

[0071] Step 5:

[0072] The "server" organizes the data it receives by date and time.

[0073] Step 6:

[0074] The "server" references historical data sets and uses machine learning algorithms to analyze the child's sleep patterns.

[0075] Step 7:

[0076] The server uses the latest data to calculate the optimal time for putting a child to bed on that day. For example, if the child was very active on a day like that, it will suggest putting the child to bed earlier.

[0077] Step 8:

[0078] The server generates specific advice based on the calculated sleep time, such as "Take a bath from 7:30 PM and read a picture book in bed by 8:30 PM."

[0079] Step 9:

[0080] The "server" transmits the generated advice to the "terminal."

[0081] Step 10:

[0082] The "terminal" displays the advice as a notification message to the "user."

[0083] Step 11:

[0084] The "user" checks the notification and adjusts the child's bedtime based on the advice, for example, giving the child a bath at 7:30 p.m. and putting them to bed at 8:30 p.m.

[0085] These steps will help parents find the optimal bedtime for their child's individual needs and provide support in their daily parenting.

[0086] Example 1

[0087] 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."

[0088] For today's parents, putting their children to sleep effectively while maintaining their health is a major challenge. It is particularly difficult to centrally manage data on a child's daily activities and physical condition and determine the optimal time for putting them to sleep based on that data. It is also challenging to obtain specific advice that parents can immediately put into practice in their busy daily lives.

[0089] 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.

[0090] In this invention, the server includes means for having parents input data on their child's daily activities, means for collecting data on the child's physical condition from the wearable device, means for transmitting the collected data to an information processing device, means for analyzing the data in the information processing device and calculating an optimal time for putting the child to bed, means for generating advice based on the calculated time for putting the child to bed, means for notifying the parent's terminal of the generated advice, and means for ensuring security of data transmission. This makes it possible to calculate an optimal time for putting the child to bed based on the child's daily activities and physical condition data, and to provide effective advice to the parent.

[0091] "Means for parents to input data regarding their child's daily activities" refers to a function that allows parents to record their child's daily activities through a dedicated application or interface.

[0092] "Means for collecting children's health data from wearable devices" refers to a function that uses sensor-equipped devices (such as wristbands or smartwatches) worn by children to obtain health information such as heart rate, body temperature, and number of steps.

[0093] The "means for transmitting collected data to an information processing device" is a communication function for transmitting the daily activity data and physical condition data collected by the terminal to a server via the Internet.

[0094] "Means for analyzing data using an information processing device and calculating the optimal time for putting a child to bed" refers to a function that uses statistical analysis and machine learning models to calculate the optimal time for putting a child to bed based on data collected by the server.

[0095] The "means for generating advice based on the calculated time to put a child to sleep" is a function in which the server generates specific methods for putting a child to sleep and advice on daily life in text or image format based on the analysis results.

[0096] "Means for notifying the parent's device of the generated advice" is a function that sends the advice generated by the server to the parent's smartphone or tablet as a push notification.

[0097] "Measures to ensure the security of data transmission" refers to the use of encryption technology and secure communication protocols to prevent data from being leaked to third parties during transmission.

[0098] "Means for using machine learning algorithms to analyze past data and learn a child's sleep patterns" refers to a function in which the server uses machine learning algorithms (e.g., random forests and support vector machines) to analyze previously collected data and understand a child's sleep patterns and activity trends.

[0099] "Means for collecting data in real time and dynamically generating suggestions based on that data" refers to a function that processes data obtained from wearable devices and parental input in real time, and based on that, the server generates advice that is appropriate to the current situation.

[0100] MODE FOR CARRYING OUT THE INVENTION

[0101] This invention relates to a system that suggests the optimal time for putting a child to bed based on data on the child's daily activities and physical condition. This system is realized by an information processing device that analyzes information entered by the parent and data automatically collected from a wearable device, and notifies the parent of the optimal time for putting the child to bed.

[0102] System Overview

[0103] The system includes the following main components:

[0104] 1. A dedicated application for parents to enter data about their child's daily activities.

[0105] 2. Wearable devices worn by children (e.g., wristband sensors).

[0106] 3. A network for transmitting collected data to a central information processing device (server).

[0107] 4. A server with machine learning algorithms to analyze the data and calculate and suggest optimal bedtimes.

[0108] 5. A means of communication to notify the parent's device (smartphone or tablet) of calculated results and advice.

[0109] Data collection method

[0110] Parent Input

[0111] Users (parents) use a dedicated application to input data about their child's daily activities. This data includes meal times, playtime, nap time, study time, screen time, etc. For example, a parent might input information into the app such as "Feed breakfast at 7:30," "Played between 9:00 and 10:00," "Took a nap between 1:00 and 2:00," and "Eaten dinner at 6:00." This information is temporarily stored on the smartphone or tablet, and then prepared for the next data transmission.

[0112] Wearable device operation

[0113] The user has their child wear a wearable device (a device with sensors such as a smartwatch). This device collects real-time physical data such as the child's heart rate, body temperature, and number of steps taken, and automatically sends it to a terminal. For example, the device records that the child's number of steps for that day was 3,000, their average heart rate was 120 bpm, and their body temperature was 37.0 degrees.

[0114] Sending data

[0115] All collected data (parent input data and wearable device data) is sent to a server over the internet. The device encrypts the data using the secure HTTP protocol (HTTPS) and sends it to the server.

[0116] Analyzing data and generating recommendations

[0117] The server stores all received data in a database and uses machine learning algorithms to analyze past data and learn the child's sleep patterns. Typical database systems used are MySQL (registered trademark) and PostgreSQL. The server analyzes the day's activity level and physical condition data to calculate the optimal time for putting the child to bed. For example, it may calculate that "since you exercised a lot today, the optimal time for putting the child to bed is 8:30 p.m." Based on the analysis results, the server then generates specific advice such as "We recommend that you take a bath from 7:30 p.m."

[0118] Sending notifications

[0119] The server sends the generated advice to the device and delivers it to the user as a push notification. The user checks the notification and gives their child a bath at 7:30 PM and puts them to bed by 8:30 PM. In this way, the system can ensure healthy sleep for their child and support parents in their daily childcare.

[0120] Example prompt

[0121] "Enter data such as breakfast time (7:30), playtime (9:00-10:00), naptime (13:00-14:00), and dinnertime (18:00) for a 3-year-old child. Also, if the data from your wearable device shows that the number of steps taken that day was 3,000, the average heart rate was 120 bpm, and the body temperature was 37.0 degrees, please use this data to calculate the optimal time for putting the child to bed."

[0122] When this prompt is fed into a generative AI model, the results suggest optimal bedtimes and other advice.

[0123] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0124] Program processing flow

[0125] Step 1: Entering the Parent

[0126] 1. User (parent) opens the dedicated application:

[0127] The user launches an app called "A Child's Day" on their smartphone.

[0128] Input: Data about the child's daily activities (e.g., mealtimes, playtimes, naptimes, etc.).

[0129] Specific operation: The user enters information such as "breakfast at 7:30," "play from 9:00 to 10:00," "nap from 13:00 to 14:00," and "dinner at 18:00" into the app form.

[0130] Output: The input activity data is temporarily saved in the device.

[0131] Step 2: Collecting data from wearable devices

[0132] 2. Wearable device data collection:

[0133] The user has their child wear a wearable device (e.g., a smart watch).

[0134] Input: Biometric data collected by the worn device (e.g., heart rate, body temperature, steps, etc.).

[0135] Specific operation: The device periodically measures the child's heart rate, body temperature, steps, etc. and transmits the data to the terminal in real time.

[0136] Output: Physical condition data obtained in real time is recorded on the device.

[0137] Step 3: Send data to the server

[0138] 3. Sending data from the device to the server:

[0139] The device sends all collected data to the server.

[0140] Input: Daily activity data entered by parents, health data from wearable devices.

[0141] Specific operation: The device encrypts the data using the HTTPS protocol and sends it to the server over the Internet.

[0142] Output: The data is securely sent to the server and stored in a database.

[0143] Step 4: Data analysis

[0144] 4. Data analysis by the server:

[0145] The server analyzes the received data.

[0146] Input: Daily activity data and physical condition data stored on the server.

[0147] How it works: The server uses machine learning algorithms (e.g., random forests, support vector machines) to calculate sleep patterns and optimal bedtimes based on past and current data.

[0148] Output: The analysis results will generate the optimal time for putting the child to sleep and advice on daily life.

[0149] Step 5: Generate proposals

[0150] 5. Server Proposal Generation:

[0151] The server generates specific advice based on the analysis results.

[0152] Input: The results of the analysis performed by the server (e.g., optimal time to put the child to bed).

[0153] Specific behavior: The server uses the text generation model to generate advice such as, "Since you exercised a lot today, the best time to put your child to bed is 8:30 p.m." or "I recommend taking a bath from 7:30 p.m."

[0154] Output: The generated advice is saved in text format on the server.

[0155] Step 6: Sending notifications

[0156] 6. Sending notifications from the server to the device:

[0157] The server transmits the generated advice to the terminal.

[0158] Input: The generated and saved advice.

[0159] What it does: The server sends data to your smartphone or tablet in the form of a push notification.

[0160] Output: The advice is displayed on the user's terminal.

[0161] Through this process, the system can calculate the optimal time for putting a child to bed based on the child's daily activities and physical condition data, and provide effective advice to parents in a timely manner, helping them ensure healthy sleep for their children and providing effective support in their daily childcare.

[0162] (Application example 1)

[0163] 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."

[0164] In modern families, it is becoming increasingly important to suggest appropriate bedtimes based on daily activity and physical condition data in order to maintain healthy lifestyles for children. However, it is difficult for parents to manage this manually, and there are also few ways to optimize meal content and delivery times. Furthermore, there is no system that comprehensively analyzes this data and makes appropriate suggestions, so children's health is not being adequately supported. There is a need for a system that solves these issues.

[0165] 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.

[0166] In this invention, the server includes means for having a parent input data regarding the child's daily activities, means for collecting data on the child's physical condition from the wearable device, means for transmitting the collected data to the server, means for analyzing the data in the server and calculating an optimal bedtime, means for generating advice based on the calculated bedtime, means for notifying the parent's terminal of the generated advice, means for having a parent input information on dietary preferences and allergies, means for calculating meal contents and delivery times based on the data analyzed by the server, means for generating suggestions based on the calculated meal contents and delivery times, and means for notifying the user's terminal of the generated suggestions. This makes it possible to suggest an optimal bedtime based on the child's daily activities and physical condition data, and to optimize meal contents and delivery times.

[0167] "Means for parents to input data on their children's daily activities" refers to devices or software that allow parents to input data on their children's daily activities, such as mealtimes and playtimes.

[0168] "Means for collecting children's health data from wearable devices" refers to devices or systems for collecting health information such as heart rate, body temperature, and number of steps through wearable devices worn by children.

[0169] The "means for transmitting collected data to a server" refers to a device or protocol for transmitting collected daily activity data and physical condition data to a server via the Internet.

[0170] "Means for analyzing data on a server and calculating the optimal time for putting a child to bed" refers to a method or device in which a server analyzes collected data and calculates the optimal time for putting a child to bed for each individual child.

[0171] "Means for generating advice based on calculated time for putting a child to sleep" refers to a system that generates specific instructions and advice for parents based on the time for putting a child to sleep calculated by the server.

[0172] "Means for notifying the parent's device of the generated advice" refers to means for notifying the parent's device, such as a smartphone or PC, of ​​the generated advice.

[0173] "Means for inputting dietary preferences and allergy information" refers to devices or software that allow parents to input information about their child's dietary preferences and allergies.

[0174] "Means for calculating meal contents and delivery time based on data analyzed by the server" refers to a method or device in which the server analyzes collected data and calculates the optimal meal contents and delivery time.

[0175] "Means for generating suggestions based on calculated meal contents and delivery times" refers to a system that generates specific suggestions to a user based on meal contents and delivery times calculated by the server.

[0176] "Means for notifying the user's device of the generated proposal" refers to means for notifying the user's device, such as a smartphone or PC, of ​​the generated proposal.

[0177] This invention is a system that suggests the optimal time for putting a child to bed based on data on the child's daily activities and physical condition, and also optimizes meal contents and delivery times. The components and operation procedures of the system are described in detail below.

[0178] Data collection

[0179] Parents use a dedicated application to enter data about their child's daily activities, food preferences, and allergies. This data includes, for example, mealtimes, playtimes, naptimes, and dinnertimes. Additionally, the system collects real-time health data (heart rate, body temperature, steps taken, etc.) from a wearable device worn by the child. This data is automatically sent to a smartphone or other device.

[0180] Sending data

[0181] All collected data is transmitted from the device via the internet to a server, where it is processed and analyzed using cloud services, particularly high-performance cloud computing platforms.

[0182] Data analysis and proposal generation

[0183] The server analyzes the collected data using a machine learning algorithm to calculate the optimal bedtime for each child. This machine learning model predicts the child's sleep patterns by studying past data. It also analyzes meal content and delivery times to generate optimal suggestions. Based on user input and real-time data from the wearable device, the server makes specific suggestions, such as "You're feeling well today, so a high-calorie meal would be appropriate for dinner."

[0184] Sending notifications

[0185] The analysis results and recommendations are sent from the server to the device and sent as push notifications to the parent's smartphone, etc. Parents can check these notifications and take specific actions to help their children maintain a healthy lifestyle.

[0186] Hardware and Software

[0187] The main hardware used to realize this system includes a wearable device worn by the child, a smartphone or PC used by the parent, and a cloud server for analyzing the data. The software uses a mobile application for collecting and transmitting data, machine learning algorithms for data analysis, and cloud services. Specifically, it uses "Python," "TENSORFLOW (registered trademark)," and "AWS (registered trademark) (Amazon Web Services)."

[0188] Specific examples

[0189] For example, imagine a family with a three-year-old child. Parents use the app to input their child's breakfast time (8:00), playtime (10:00-11:00), naptime (13:00-14:00), dinnertime (18:00), etc. The wearable device collects data in real time, such as the child's calories burned, heart rate, and body temperature for the day. Based on this data, the server makes suggestions such as, "Today, you exercised a lot, so you need a nutritious meal for dinner to make up for the calories lost, and the optimal time to put your child to bed is 20:30." The parent also receives a link to order the suggested meal via a food delivery service.

[0190] Prompt Sentence Examples

[0191] You can get specific suggestions for your generative AI model by using prompts like the following:

[0192] "Please suggest optimal meals based on the generative AI model and user data.

[0193] User Data:

[0194] Children's meal times: Breakfast 8:00, Lunch 12:00, Dinner 18:00

[0195] Kids' Activities: School, Soccer Practice

[0196] Children's Allergies: Peanuts

[0197] Child's food preference: Vegetarian

[0198] Wearable device data:

[0199] Calories burned: 500

[0200] Body temperature: 36.5°C

[0201] Activity level: High

[0202] As described above, this system allows parents to receive optimal advice based on their child's daily activity and physical condition data, enabling them to effectively manage their health.

[0203] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0204] Step 1:

[0205] Through the application, parents input data about their child's daily activities, including mealtimes, playtimes, naptimes, dinnertimes, and their child's food preferences and allergies, which are then stored in a local database on the device.

[0206] Step 2:

[0207] The wearable device that the child wears collects real-time health data, including heart rate, body temperature, steps taken, calories burned, etc. The collected data is automatically sent to the device.

[0208] Step 3:

[0209] The device transmits all collected data to a server via the Internet, converting the collected daily activity data and physical condition data into an appropriate format and sending it to the server as a POST request using an API.

[0210] Step 4:

[0211] The server stores the received data in a database and analyzes it using a machine learning algorithm. Specifically, it learns a child's sleep and eating patterns based on past data and predicts the optimal time for putting them to bed and what to eat. The server's analysis process uses Python and TensorFlow, from data preprocessing to model training and prediction.

[0212] Step 5:

[0213] Based on the analysis results, the server generates the optimal time to put the child to bed and advice based on that, as well as suggestions based on the optimal meal content and delivery time. For example, specific advice and suggestions may be generated, such as "Since you exercised a lot today, a high-calorie meal would be appropriate for dinner" or "8:30 p.m. is the optimal time to put the child to bed today."

[0214] Step 6:

[0215] The server sends the generated advice and suggestions to the device and sends push notifications to the parent's smartphone, PC, or other device. The notifications include detailed instructions and suggestions for specific actions to take regarding the child's health. The device displays these notifications so that the parent can review the suggestions. Specifically, the device's push notification API is used to display the notifications.

[0216] Step 7:

[0217] The parent can then review the notification and take the action suggested by the server, such as ordering the suggested meal from a food delivery service and getting the child to bed based on the suggested bedtime. During this process, the parent can easily order the meal using the food delivery link within the app.

[0218] Through the above-mentioned series of processing steps, parents can receive optimal advice based on their child's daily activities and physical condition data, and can effectively manage their child's health.

[0219] 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.

[0220] MODE FOR CARRYING OUT THE INVENTION

[0221] This invention relates to a system that suggests the optimal time for putting a child to bed based on the child's daily activities and physical condition data, and also taking into consideration the emotional states of the user (parent) and the child. This system is realized by a server that analyzes input information from the parent, data collected from a wearable device, and emotional data generated by an emotion engine, and notifies the parent of the optimal time for putting the child to bed.

[0222] System Overview

[0223] 1. Data Collection

[0224] Parental input: Through a dedicated application, the "user" inputs their child's daily activity data, including mealtimes, playtime, naptime, study time, and screen time.

[0225] Wearable devices: Children wear wearable devices that collect real-time health data such as heart rate, body temperature, and number of steps taken. This data is automatically sent to a "terminal."

[0226] 2. Collecting Emotional Data

[0227] Parental Emotions: The device uses an emotion engine to recognize parental emotions through the application. Parental emotions are collected using text input and facial expression recognition technology.

[0228] Child's emotions: The Device will use facial and voice recognition technology through wearable devices or other appropriate sensors to recognize the child's emotions.

[0229] 3. Data transmission

[0230] All data is transmitted from the "terminal" to the "server" via the internet. The transmitted data includes parent input data, physical condition data from the wearable device, and emotional data of the parent and child.

[0231] 4. Data analysis and proposal generation

[0232] The "server" analyzes all the data it receives and runs machine learning algorithms to calculate the optimal bedtime for each child, taking into account past data and the current day's situation.

[0233] The server then uses the emotional data collected by the emotion engine to generate advice tailored to the parent's mental state and the child's mood. For example, if the parent is tired or the child is in a bad mood, it will adjust the timing and method of putting the child to bed.

[0234] 5. Sending notifications

[0235] The server generates specific advice based on the calculated optimal sleep time, which is then sent to the device and displayed to the user as a push notification.

[0236] Specific examples

[0237] For example, here is an example of a home with a 3-year-old child.

[0238] 1. Parent Input and Data Collection

[0239] "Users" use the app to input activity data such as their child's breakfast time (7:30), playtime (9:00-10:00), naptime (13:00-14:00), and dinnertime (18:00).

[0240] Children wear wearable devices that record their physical condition data, such as heart rate and body temperature, in real time.

[0241] "Users" input their emotional data into the app through facial recognition and text input.

[0242] Similarly, children's emotional data will be collected through wearable devices and other sensors.

[0243] 2. Data Transmission

[0244] All collected data is sent from the "terminal" to the "server."

[0245] 3. Data analysis and proposal generation

[0246] The server analyzes the data and calculates, for example, "Today, the child is very active and the parents are tired, so the optimal time to put the child to bed is 8:30 p.m." based on the child's activity level and physical condition that day, as well as the emotional state of the parent and child.

[0247] The "server" takes into account the parent's emotional state and generates specific advice such as "Play relaxing sounds at 7:30 p.m. and read picture books in bed at 8:30 p.m."

[0248] 4. Sending notifications

[0249] The "server" sends the generated advice to the "terminal," which then notifies the "user." The "user" checks this notification and adjusts the time for putting their child to bed based on the advice.

[0250] This system provides the optimal bedtime based on a child's daily activities and emotional state, reducing the burden on parents and supporting healthy sleep for children.

[0251] The processing flow will be explained below.

[0252] Step 1:

[0253] The "user" launches the smartphone app and enters the child's daily activity data for that day, including mealtimes, playtime, naptime, study time, and screen time.

[0254] Step 2:

[0255] The "user" puts the wearable device on the child.

[0256] Step 3:

[0257] The "terminal" collects real-time health data such as heart rate, body temperature, and number of steps from the wearable device.

[0258] Step 4:

[0259] To recognize the parent's emotions, the device uses an emotion engine to collect emotional data from the parent through the smartphone camera and text input, such as facial expression analysis and input of keywords that indicate emotions.

[0260] Step 5:

[0261] To recognize the child's emotions, the device uses an emotion engine to collect the child's emotional data through sensors in the wearable device and voice input.

[0262] Step 6:

[0263] The "terminal" compiles the collected daily activity data, physical condition data, and emotional data and sends it to the "server."

[0264] Step 7:

[0265] The "server" organizes the received data by date and time and prepares it for analysis.

[0266] Step 8:

[0267] The "server" references historical data sets and uses machine learning algorithms to analyze a child's sleep patterns, including daily activity, physical condition, and emotional data.

[0268] Step 9:

[0269] The "server" calculates the optimal bedtime for that day based on the latest data, for example suggesting an earlier bedtime if the child was more active, and takes into account the child's emotional state.

[0270] Step 10:

[0271] The server generates specific advice tailored to the emotions of the parent and child based on the emotional data collected by the emotion engine. For example, if the parent is tired, the advice may include "creating a relaxing environment."

[0272] Step 11:

[0273] The "server" transmits the generated advice to the "terminal."

[0274] Step 12:

[0275] The device displays advice to the user in the form of a notification message, including the time to put the child to sleep and specific suggested actions.

[0276] Step 13:

[0277] The "user" checks the notification and adjusts the child's bedtime based on the advice, for example, giving the child a bath at 7:30 p.m. and putting them to bed at 8:30 p.m.

[0278] Through these steps, the system will comprehensively analyze the child's daily activities, physical condition, and emotional state, and suggest the optimal bedtime, making it easier for parents to ensure their children get healthy sleep and reducing the burden of childcare.

[0279] Example 2

[0280] 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."

[0281] In modern society, it is important for parents to maintain healthy sleep patterns for their children. However, it is difficult to comprehensively understand a child's daily activities, physical condition, and emotional state to determine the optimal time for putting the child to bed. Furthermore, because a parent's own mental state also affects a child's sleep, there is a need for a method of putting the child to bed that takes this into consideration. Given this background, there is a need for a system that comprehensively analyzes a child's daily activity data, physical condition data, and emotional data to provide the optimal time for putting the child to bed and specific advice.

[0282] 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.

[0283] In this invention, the server includes means for having a parent input data on the child's daily activities, means for collecting the child's physical condition data from the wearable device, means for collecting emotional data of the parent and child, means for transmitting the collected data to a computer device, means for analyzing the data in the computer device and calculating an optimal time for putting the child to sleep, means for generating advice based on the calculated time for putting the child to sleep, and means for notifying the parent's information processing device of the generated advice. This allows the parent to know the optimal time for putting the child to sleep based on the child's daily activities and emotional state, and to receive specific advice.

[0284] "Means for parental input of data regarding the child's daily activities" refers to an input device or software function that allows a parent to input information about the child's daily activities, such as mealtimes, playtime, naptime, study time, and screen time.

[0285] "Means for collecting physical condition data of a child from a wearable device" refers to equipment and its functions for obtaining physical condition data such as heart rate, body temperature, and number of steps using a wearable device worn by a child.

[0286] "Means for collecting parent and child emotional data" refers to technologies and software functions for capturing the emotional states of parents and children using text input, facial expression recognition, and voice recognition technologies.

[0287] "Means for transmitting collected data to a computer device" refers to communication functions and protocols for transferring data collected from the parent's terminal or wearable device to a computer device such as a server via the Internet.

[0288] "Means for analyzing data using a computer device and calculating the optimal time for putting a child to sleep" refers to analytical technology and its software functions that allow a computer device such as a server to analyze collected data and use machine learning algorithms, etc. to derive the optimal time for putting a child to sleep.

[0289] The "means for generating advice based on the calculated sleep time" is an advice generation function for creating advice regarding specific sleep method and timing based on the analyzed optimal sleep time.

[0290] The "means for notifying the parent's information processing device of the generated advice" refers to a push notification function or communication means for notifying the parent's information processing device such as a smartphone or tablet of the generated advice.

[0291] MODE FOR CARRYING OUT THE INVENTION

[0292] The present invention relates to a system that suggests the optimal time for putting a child to bed based on data on the child's daily activities and physical condition, and also taking into consideration the emotional states of the parent and the child. This system is realized by a server that analyzes input information from the parent, data collected from a wearable device, and emotional data generated by an emotion engine, and notifies the parent of the optimal time for putting the child to bed.

[0293] The embodiments for carrying out the present invention are as follows.

[0294] Data collection

[0295] Parental input: Using a dedicated application, users input their child's daily activity data, including mealtimes, playtime, naptime, study time, screen time, etc. For example, breakfast time can be entered as "7:30" and playtime as "9:00-10:00."

[0296] Wearable devices: Children wear wearable devices (e.g., smartwatches, fitness trackers) that collect real-time physical data such as heart rate, body temperature, and number of steps. This data is automatically sent to the device.

[0297] Collecting Emotional Data

[0298] Parental Emotions: The device uses an emotion engine through the application to recognize parental emotions. Parental emotions are collected using text input and facial recognition technology (e.g., facial recognition API).

[0299] Child's emotions: The device uses facial and / or voice recognition technology in wearable devices or other appropriate sensors (e.g., voice recognition APIs) to recognize the child's emotions.

[0300] Sending data

[0301] All collected data is transmitted from the device to a server via the internet, including parental input data, physical condition data from the wearable device, and emotional data of the parent and child.

[0302] Data analysis and proposal generation

[0303] Data analysis: The server analyzes the received data and uses machine learning algorithms (e.g., TensorFlow) to calculate the optimal time for putting the child to sleep, taking into account past data and the current day's situation.

[0304] Suggestion generation: The server generates advice tailored to the parent's mental state and the child's mood based on the emotional data collected by the emotion engine. For example, if the parent is tired or the child is in a bad mood, the server provides advice on adjusting the timing and method of putting the child to bed.

[0305] Sending notifications

[0306] The server sends the generated advice to the device, which displays it to the user as a push notification. The user can then check the notification and adjust the bedtime based on the advice.

[0307] Specific examples

[0308] For example, here's an example from a home with a 3-year-old child:

[0309] 1. Parental input and data collection: Through the app, users input data such as their child's breakfast time (7:30), playtime (9:00-10:00), naptime (13:00-14:00), and dinnertime (18:00). Heart rate and body temperature data are recorded in real time from the wearable device worn by the child. Parents input their own emotional data into the app through facial recognition and text input. Emotional data about the child is also collected through the wearable device and other sensors.

[0310] 2. Data transmission: All collected data is transmitted from the device to the server.

[0311] 3. Data analysis and recommendation generation: The server analyzes the data and calculates, based on the child's activity level and physical condition that day, as well as the emotional state of the parent and child, that "Today, the child is very active and the parent is tired, so the optimal time to put the child to bed is 8:30 p.m." Taking into account the parent's emotional state, it generates specific advice such as "Play relaxing music at 7:30 p.m. and read a picture book in bed at 8:30 p.m."

[0312] 4. Sending notification: The server sends the generated advice to the device, and the device notifies the user as a push notification. The user checks the notification and adjusts their child's bedtime based on the advice.

[0313] Example prompts for generative AI models

[0314] "My child is 3 years old. Today's activity data is as follows: breakfast time is 7:30, playtime is 9:00-10:00, nap time is 13:00-14:00, and dinner time is 18:00. I'm a little tired, and my child is a little cranky. What's the best time to put him to bed? What's your advice?"

[0315] This system provides the optimal bedtime based on a child's daily activities and emotional state, reducing the burden on parents and supporting healthy sleep for children.

[0316] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0317] Program processing flow

[0318] Step 1:

[0319] A means for the user to input their child's daily activity data

[0320] Specific operations: The user opens a dedicated app on their smartphone and enters their child's daily activity data, such as breakfast time, playtime, nap time, and dinner time.

[0321] Example: "7:30" for breakfast time, "9:00-10:00" for playtime, etc.

[0322] Input: Child's daily activity data (e.g., breakfast time, playtime, etc.)

[0323] Output: The entered daily activity data is saved in the system.

[0324] Step 2:

[0325] A means for terminals to collect physical condition data from wearable devices

[0326] Specific operation: The device collects real-time physical condition data such as heart rate, body temperature, and number of steps taken via wearable devices (e.g., smart watches, fitness trackers) worn by children.

[0327] Example of how it works: After a child has completed an hour of exercise, their heart rate and temperature data are sent from the wearable device to the terminal.

[0328] Input: Real-time physical condition data received from a wearable device

[0329] Output: Collected physical condition data is saved to the device.

[0330] Step 3:

[0331] How the device collects parent and child emotional data

[0332] Specific operation: The device uses the application's emotion engine to collect the parent's emotions through text input and facial expression recognition, and the child's emotions through facial expression recognition and voice recognition technology.

[0333] How it works: Parents take pictures of their own faces using their smartphone cameras and use facial recognition technology to collect emotional data. Children's emotional data is analyzed from audio recordings of their play.

[0334] Input: Parent's emotional data (text input, facial expression recognition), Child's emotional data (facial expression recognition, voice recognition)

[0335] Output: Emotion data is recorded on the device.

[0336] Step 4:

[0337] A means for the device to send all collected data to a server

[0338] Specific operation: The terminal packetizes the collected parental input data, physical state data from the wearable device, and emotion data, and sends them to a server via the Internet.

[0339] Example of operation: Various data is collected into packets at regular intervals and transferred to the server.

[0340] Input: Parent input data, physical state data, emotion data

[0341] Output: The transmitted data arrives at the server.

[0342] Step 5:

[0343] The server analyzes the data and calculates the optimal time to put the child to sleep.

[0344] How it works: The server analyzes the received data and uses machine learning algorithms (e.g., TensorFlow) to calculate the optimal time for putting the child to bed based on the child's activity level, physical condition, and emotional state that day. The analysis also takes into account past data.

[0345] Example of operation: When the server receives the data, it first filters the emotional and physical condition data and compares it with the data from the previous day to determine the optimal time to put the child to sleep.

[0346] Input: All data sent to the server

[0347] Output: The optimal bedtime (e.g. 8:30pm) is calculated.

[0348] Step 6:

[0349] A means for the server to generate advice based on the calculation results and notify the device

[0350] Specific operation: Based on the calculated optimal time for putting the child to sleep, the server considers the emotional state of the parent and child and generates specific advice, such as "Play relaxing music at 7:30 PM and read a picture book in bed at 8:30 PM." The generated advice is sent to the device.

[0351] Example of operation: The advice generated by the server is packetized in text message format and notified to the terminal.

[0352] Input: Optimal bedtime, emotional state of parent and child

[0353] Output: Specific advice generated

[0354] Step 7:

[0355] A means for the device to notify the user of advice

[0356] Specific operation: The device displays the advice received from the server to the user as a push notification. The user checks the notification and adjusts their child's bedtime based on the advice.

[0357] Example of how it works: The device displays a push notification, which the user sees on their smartphone screen.

[0358] Input: Advice received from the server

[0359] Output: Advice given to the user

[0360] (Application example 2)

[0361] 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."

[0362] In the past, it was difficult for parents to determine the appropriate bedtime based on their child's daily activity and physical condition data. It was especially difficult to take into account the child's activity data and emotional state when visiting a physical store. It was also extremely difficult to grasp the child's emotional state in real time and make flexible suggestions based on that. To solve this problem, a system that comprehensively analyzes parent-child activity and emotional data and provides optimal advice is needed.

[0363] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for having a parent input data on the child's daily activities, means for collecting the child's physical condition data from a wearable device, means for transmitting the collected data to the server, means for analyzing the data in the server and calculating optimal rest times and home times, means for generating advice based on the calculated rest times, means for notifying the parent's terminal of the generated advice, means for inputting activity data of the parent and child in a physical store visited via a smartphone, and means for collecting emotional data of the parent and child using facial expression recognition and voice recognition technology. This makes it possible to comprehensively analyze the activity data and emotional data of the parent and child in the physical store and provide optimal advice in real time.

[0364] The "means for allowing parents to input data on their children's daily activities" provides an interface for parents to input information on their children's daily activities.

[0365] "Means for collecting children's health data from wearable devices" refers to a system that collects health data such as heart rate, body temperature, and number of steps in real time from wearable devices worn by children.

[0366] "Means for transmitting collected data to a server" refers to a communication means for transmitting all data collected from the parent and the wearable device to a server via the Internet.

[0367] "Means for analyzing data on the server and calculating optimal break times and times to go home" refers to a function that uses a machine learning algorithm to analyze the data received by the server and calculates optimal break times and times to go home based on the results.

[0368] The "means for generating advice based on the calculated rest time" is a mechanism for generating specific advice based on the optimal rest time calculated by the server.

[0369] The "means for notifying the parent's terminal of the generated advice" refers to a communication means for notifying the parent's terminal, such as a smartphone or tablet, of the advice generated by the server.

[0370] "Means for inputting activity data within physical stores visited by parents and children via smartphone" refers to an interface that allows parents to input information about their children's activities within physical stores using a smartphone app.

[0371] "Means for collecting parent-child emotional data using facial expression and voice recognition technology" refers to technology that uses smartphones or wearable devices to recognize the facial expressions and voices of parents and children and collect that emotional data.

[0372] This invention relates to a system that suggests optimal break times and times to go home in a physical store visited by parents and children, taking into consideration the child's daily activities and physical condition data, as well as the emotional states of the parents and children.

[0373] System Configuration

[0374] Hardware

[0375] Smartphone: Used by parents to input data about their children's daily activities and collect emotional data.

[0376] Wearable device: Worn by children, it collects real-time health data such as heart rate, body temperature, and number of steps taken.

[0377] Server: Analyzes data collected via the Internet and calculates optimal break times and times to go home.

[0378] software

[0379] Application development frameworks (e.g., React Native, Swift, Kotlin): Used to develop smartphone apps.

[0380] Machine learning libraries (e.g. TensorFlow, PyTorch): Used to perform data analysis on the server side.

[0381] Emotion recognition engine (e.g., Microsoft® Azure® Cognitive Services, IBM Watson®): Analyzes parent and child emotion data.

[0382] Operation overview

[0383] Data collection

[0384] Parents enter their child's daily activity data on their smartphone. This data includes playtime, study time, mealtimes, etc. Additionally, the child's wearable device sends real-time health data, such as heart rate, body temperature, and number of steps, to the smartphone.

[0385] Collecting Emotional Data

[0386] The smartphone camera is used to recognize the facial expressions of parents and children, and voice recognition technology is used to collect emotional data, which is then analyzed by an emotion engine to understand the emotional state of parents and children.

[0387] Data transmission and analysis

[0388] All data is sent over the internet to a server, where machine learning algorithms are used to analyze the data and calculate optimal break times and times to go home.

[0389] Advice Generation and Notifications

[0390] Based on the results calculated on the server, specific advice is generated and sent to the parent's smartphone as a push notification. The advice also includes suggestions for making life more convenient for both parent and child.

[0391] Specific examples

[0392] For example, parents can input their child's activity information in a physical store into a smartphone app: for example, playtime (10:00-11:00), mealtime (12:00), etc. Also, if the child is wearing a wearable device, their physical condition data, such as heart rate, body temperature, and number of steps, is collected in real time on the smartphone.

[0393] It then uses the smartphone camera to recognize the facial expressions of the parent and child, and analyzes their voice to collect emotional data, and an emotion engine analyzes the emotional state of the parent and child.

[0394] All this data is sent to a server via the internet, which uses machine learning algorithms to calculate optimal break times and times to go home. For example, if the child is very active and the parent is tired, the server will generate advice such as, "Today, it would be good to take a break at 1 p.m., so that the parent can relax together."

[0395] The generated advice is sent to the parent's smartphone via push notification, and the parent can check this notification and adjust appropriate break times, etc.

[0396] Prompt Sentence Examples

[0397] "How long did your child play this morning?"

[0398] "The child's current temperature and heart rate are as follows."

[0399] "Parents, you seem tired. It would be good to take a break today."

[0400] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0401] Step 1: Data entry

[0402] The user (parent) uses a smartphone app to input their child's daily activity data, including mealtimes, playtime, study time, screen time, etc. The input data is saved in the app and sent to the next step.

[0403] Step 2: Collecting health data

[0404] The terminal (smartphone) collects the child's physical condition data in real time from the wearable device. This data includes heart rate, body temperature, number of steps, etc. The collected data is stored on the smartphone in real time and transmitted to the next step.

[0405] Step 3: Collecting emotion data

[0406] The device (smartphone) uses the smartphone's camera to recognize the facial expressions of the parent and child, and uses voice recognition technology to collect emotional data, which is then stored on the smartphone and sent to the next step.

[0407] Step 4: Send data

[0408] The device (smartphone) sends all collected data (activity data, physical condition data, emotional data) to a server via the Internet, where it is stored and prepared for analysis.

[0409] Step 5: Data analysis

[0410] The server receives all the data and begins analyzing it. Here, it uses machine learning algorithms to analyze the data and calculate the optimal break time and time for the child to return home. In particular, it processes and calculates the data based on past data and the situation on the day.

[0411] Step 6: Advice Generation

[0412] The server generates specific advice based on the results of the data analysis. Using a generative AI model, it creates advice tailored to the parent's mental state and the child's mood. For example, it might generate advice such as, "It's a good idea to take a break at 1 p.m. today. Let's relax together as parents."

[0413] Step 7: Send notification

[0414] The server sends the generated advice to the parent's device (smartphone) as a push notification. The device receives the notification and displays it so that the user can check it. The user can adjust their child's break time and activity schedule based on this notification.

[0415] 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.

[0416] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0417] 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.

[0418] [Second embodiment]

[0419] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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).

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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."

[0431] MODE FOR CARRYING OUT THE INVENTION

[0432] This invention relates to a system that suggests the optimal time for putting a child to bed based on data on the child's daily activities and physical condition. The system is realized by having a server analyze information entered by the parent and data automatically collected from a wearable device, and then notifying the parent of the optimal time for putting the child to bed.

[0433] System Overview

[0434] 1. Data Collection

[0435] Parental input: Using a dedicated application, the "user" inputs data about their child's daily activities, such as mealtimes, playtime, naptime, study time, and screen time.

[0436] Wearable devices: Children wear wearable devices that collect real-time health data such as heart rate, body temperature, and number of steps taken. This data is automatically sent to a "terminal."

[0437] 2. Data transmission

[0438] All collected data is transmitted from the "terminal" to the "server" via the Internet.

[0439] 3. Data analysis and proposal generation

[0440] The server analyzes the collected data and calculates the optimal time for each child to go to bed. The server uses machine learning algorithms to learn a child's sleep patterns based on past data and predict the optimal time for that day.

[0441] The server generates specific sleep-related advice based on the analysis results, such as "To get your child to bed by 8:30 PM, it's best to take a bath before 8 PM."

[0442] 4. Sending notifications

[0443] The "server" sends the generated advice to the "terminal" and delivers it to the "user" as a push notification.

[0444] Specific examples

[0445] For example, consider a family with a three-year-old child.

[0446] 1. Parent Input and Data Collection

[0447] "Users" use the app to input their children's breakfast time (7:30), playtime (9:00-10:00), naptime (13:00-14:00), dinnertime (18:00), etc.

[0448] As the child wears the wearable device, the number of steps taken, heart rate, and body temperature for the day are automatically recorded on the "terminal."

[0449] 2. Data Transmission

[0450] These data are sent from the "terminal" to the "server."

[0451] 3. Data analysis and proposal generation

[0452] The server analyzes the data and takes into account the child's activity level and physical condition that day, calculating, for example, "Since the child was very active today, the optimal time to put the child to bed is 8:30 p.m."

[0453] The server also generates specific advice, such as "We recommend taking a bath from 7:30 p.m."

[0454] 4. Sending notifications

[0455] The "server" sends this advice to the "terminal", which then notifies the "user".

[0456] The "user" checks this notification, gives the child a bath at 7:30 p.m., and puts the child to bed at 8:30 p.m.

[0457] In this way, the system can calculate the optimal time for putting a child to bed based on the child's daily activities and physical condition data, and provide effective advice to parents in a timely manner, thereby helping parents ensure healthy sleep for their children and supporting their daily childcare.

[0458] The processing flow will be explained below.

[0459] Step 1:

[0460] The "user" launches the smartphone app and inputs their child's daily activity data for the day, including mealtimes, playtime, naptime, screen time, and study time.

[0461] Step 2:

[0462] The "user" puts the wearable device on the child.

[0463] Step 3:

[0464] The "terminal" collects physical data such as heart rate, body temperature, and number of steps from the wearable device in real time.

[0465] Step 4:

[0466] The "terminal" compiles the collected daily activity data and physical condition data and sends it to the "server."

[0467] Step 5:

[0468] The "server" organizes the data it receives by date and time.

[0469] Step 6:

[0470] The "server" references historical data sets and uses machine learning algorithms to analyze the child's sleep patterns.

[0471] Step 7:

[0472] The server uses the latest data to calculate the optimal time for putting a child to bed on that day. For example, if the child was very active on a day like that, it will suggest putting the child to bed earlier.

[0473] Step 8:

[0474] The server generates specific advice based on the calculated sleep time, such as "Take a bath from 7:30 PM and read a picture book in bed by 8:30 PM."

[0475] Step 9:

[0476] The "server" transmits the generated advice to the "terminal."

[0477] Step 10:

[0478] The "terminal" displays the advice as a notification message to the "user."

[0479] Step 11:

[0480] The "user" checks the notification and adjusts the child's bedtime based on the advice, for example, giving the child a bath at 7:30 p.m. and putting them to bed at 8:30 p.m.

[0481] These steps will help parents find the optimal bedtime for their child's individual needs and provide support in their daily parenting.

[0482] Example 1

[0483] 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."

[0484] For today's parents, putting their children to sleep effectively while maintaining their health is a major challenge. It is particularly difficult to centrally manage data on a child's daily activities and physical condition and determine the optimal time for putting them to sleep based on that data. It is also challenging to obtain specific advice that parents can immediately put into practice in their busy daily lives.

[0485] 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.

[0486] In this invention, the server includes means for having parents input data on their child's daily activities, means for collecting data on the child's physical condition from the wearable device, means for transmitting the collected data to an information processing device, means for analyzing the data in the information processing device and calculating an optimal time for putting the child to bed, means for generating advice based on the calculated time for putting the child to bed, means for notifying the parent's terminal of the generated advice, and means for ensuring security of data transmission. This makes it possible to calculate an optimal time for putting the child to bed based on the child's daily activities and physical condition data, and to provide effective advice to the parent.

[0487] "Means for parents to input data regarding their child's daily activities" refers to a function that allows parents to record their child's daily activities through a dedicated application or interface.

[0488] "Means for collecting children's health data from wearable devices" refers to a function that uses sensor-equipped devices (such as wristbands or smartwatches) worn by children to obtain health information such as heart rate, body temperature, and number of steps.

[0489] The "means for transmitting collected data to an information processing device" is a communication function for transmitting the daily activity data and physical condition data collected by the terminal to a server via the Internet.

[0490] "Means for analyzing data using an information processing device and calculating the optimal time for putting a child to bed" refers to a function that uses statistical analysis and machine learning models to calculate the optimal time for putting a child to bed based on data collected by the server.

[0491] The "means for generating advice based on the calculated time to put a child to sleep" is a function in which the server generates specific methods for putting a child to sleep and advice on daily life in text or image format based on the analysis results.

[0492] "Means for notifying the parent's device of the generated advice" is a function that sends the advice generated by the server to the parent's smartphone or tablet as a push notification.

[0493] "Measures to ensure the security of data transmission" refers to the use of encryption technology and secure communication protocols to prevent data from being leaked to third parties during transmission.

[0494] "Means for using machine learning algorithms to analyze past data and learn a child's sleep patterns" refers to a function in which the server uses machine learning algorithms (e.g., random forests and support vector machines) to analyze previously collected data and understand a child's sleep patterns and activity trends.

[0495] "Means for collecting data in real time and dynamically generating suggestions based on that data" refers to a function that processes data obtained from wearable devices and parental input in real time, and based on that, the server generates advice that is appropriate to the current situation.

[0496] MODE FOR CARRYING OUT THE INVENTION

[0497] This invention relates to a system that suggests the optimal time for putting a child to bed based on data on the child's daily activities and physical condition. This system is realized by an information processing device that analyzes information entered by the parent and data automatically collected from a wearable device, and notifies the parent of the optimal time for putting the child to bed.

[0498] System Overview

[0499] The system includes the following main components:

[0500] 1. A dedicated application for parents to enter data about their child's daily activities.

[0501] 2. Wearable devices worn by children (e.g., wristband sensors).

[0502] 3. A network for transmitting collected data to a central information processing device (server).

[0503] 4. A server with machine learning algorithms to analyze the data and calculate and suggest optimal bedtimes.

[0504] 5. A means of communication to notify the parent's device (smartphone or tablet) of calculated results and advice.

[0505] Data collection method

[0506] Parent Input

[0507] Users (parents) use a dedicated application to input data about their child's daily activities. This data includes meal times, playtime, nap time, study time, screen time, etc. For example, a parent might input information into the app such as "Feed breakfast at 7:30," "Played between 9:00 and 10:00," "Took a nap between 1:00 and 2:00," and "Eaten dinner at 6:00." This information is temporarily stored on the smartphone or tablet, and then prepared for the next data transmission.

[0508] Wearable device operation

[0509] The user has their child wear a wearable device (a device with sensors such as a smartwatch). This device collects real-time physical data such as the child's heart rate, body temperature, and number of steps taken, and automatically sends it to a terminal. For example, the device records that the child's number of steps for that day was 3,000, their average heart rate was 120 bpm, and their body temperature was 37.0 degrees.

[0510] Sending data

[0511] All collected data (parent input data and wearable device data) is sent to a server over the internet. The device encrypts the data using the secure HTTP protocol (HTTPS) and sends it to the server.

[0512] Analyzing data and generating recommendations

[0513] The server stores all received data in a database and uses machine learning algorithms to analyze past data and learn the child's sleep patterns. Typical database systems used are MySQL and PostgreSQL. The server analyzes the day's activity level and physical condition data to calculate the optimal time to put the child to bed. For example, it may calculate that "since you exercised a lot today, the optimal time to put the child to bed is 8:30 p.m." Based on the analysis results, the server then generates specific advice, such as "We recommend that you take a bath from 7:30 p.m."

[0514] Sending notifications

[0515] The server sends the generated advice to the device and delivers it to the user as a push notification. The user checks the notification and gives their child a bath at 7:30 PM and puts them to bed by 8:30 PM. In this way, the system can ensure healthy sleep for their child and support parents in their daily childcare.

[0516] Example prompt

[0517] "Enter data such as breakfast time (7:30), playtime (9:00-10:00), naptime (13:00-14:00), and dinnertime (18:00) for a 3-year-old child. Also, if the data from your wearable device shows that the number of steps taken that day was 3,000, the average heart rate was 120 bpm, and the body temperature was 37.0 degrees, please use this data to calculate the optimal time for putting the child to bed."

[0518] When this prompt is fed into a generative AI model, the results suggest optimal bedtimes and other advice.

[0519] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0520] Program processing flow

[0521] Step 1: Entering the Parent

[0522] 1. User (parent) opens the dedicated application:

[0523] The user launches an app called "A Child's Day" on their smartphone.

[0524] Input: Data about the child's daily activities (e.g., mealtimes, playtimes, naptimes, etc.).

[0525] Specific operation: The user enters information such as "breakfast at 7:30," "play from 9:00 to 10:00," "nap from 13:00 to 14:00," and "dinner at 18:00" into the app form.

[0526] Output: The input activity data is temporarily saved in the device.

[0527] Step 2: Collecting data from wearable devices

[0528] 2. Wearable device data collection:

[0529] The user has their child wear a wearable device (e.g., a smart watch).

[0530] Input: Biometric data collected by the worn device (e.g., heart rate, body temperature, steps, etc.).

[0531] Specific operation: The device periodically measures the child's heart rate, body temperature, steps, etc. and transmits the data to the terminal in real time.

[0532] Output: Physical condition data obtained in real time is recorded on the device.

[0533] Step 3: Send data to the server

[0534] 3. Sending data from the device to the server:

[0535] The device sends all collected data to the server.

[0536] Input: Daily activity data entered by parents, health data from wearable devices.

[0537] Specific operation: The device encrypts the data using the HTTPS protocol and sends it to the server over the Internet.

[0538] Output: The data is securely sent to the server and stored in a database.

[0539] Step 4: Data analysis

[0540] 4. Data analysis by the server:

[0541] The server analyzes the received data.

[0542] Input: Daily activity data and physical condition data stored on the server.

[0543] How it works: The server uses machine learning algorithms (e.g., random forests, support vector machines) to calculate sleep patterns and optimal bedtimes based on past and current data.

[0544] Output: The analysis results will generate the optimal time for putting the child to sleep and advice on daily life.

[0545] Step 5: Generate proposals

[0546] 5. Server Proposal Generation:

[0547] The server generates specific advice based on the analysis results.

[0548] Input: The results of the analysis performed by the server (e.g., optimal time to put the child to bed).

[0549] Specific behavior: The server uses the text generation model to generate advice such as, "Since you exercised a lot today, the best time to put your child to bed is 8:30 p.m." or "I recommend taking a bath from 7:30 p.m."

[0550] Output: The generated advice is saved in text format on the server.

[0551] Step 6: Sending notifications

[0552] 6. Sending notifications from the server to the device:

[0553] The server transmits the generated advice to the terminal.

[0554] Input: The generated and saved advice.

[0555] What it does: The server sends data to your smartphone or tablet in the form of a push notification.

[0556] Output: The advice is displayed on the user's terminal.

[0557] Through this process, the system can calculate the optimal time for putting a child to bed based on the child's daily activities and physical condition data, and provide effective advice to parents in a timely manner, helping them ensure healthy sleep for their children and providing effective support in their daily childcare.

[0558] (Application example 1)

[0559] 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."

[0560] In modern families, it is becoming increasingly important to suggest appropriate bedtimes based on daily activity and physical condition data in order to maintain healthy lifestyles for children. However, it is difficult for parents to manage this manually, and there are also few ways to optimize meal content and delivery times. Furthermore, there is no system that comprehensively analyzes this data and makes appropriate suggestions, so children's health is not being adequately supported. There is a need for a system that solves these issues.

[0561] 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.

[0562] In this invention, the server includes means for having a parent input data regarding the child's daily activities, means for collecting data on the child's physical condition from the wearable device, means for transmitting the collected data to the server, means for analyzing the data in the server and calculating an optimal bedtime, means for generating advice based on the calculated bedtime, means for notifying the parent's terminal of the generated advice, means for having a parent input information on dietary preferences and allergies, means for calculating meal contents and delivery times based on the data analyzed by the server, means for generating suggestions based on the calculated meal contents and delivery times, and means for notifying the user's terminal of the generated suggestions. This makes it possible to suggest an optimal bedtime based on the child's daily activities and physical condition data, and to optimize meal contents and delivery times.

[0563] "Means for parents to input data on their children's daily activities" refers to devices or software that allow parents to input data on their children's daily activities, such as mealtimes and playtimes.

[0564] "Means for collecting children's health data from wearable devices" refers to devices or systems for collecting health information such as heart rate, body temperature, and number of steps through wearable devices worn by children.

[0565] The "means for transmitting collected data to a server" refers to a device or protocol for transmitting collected daily activity data and physical condition data to a server via the Internet.

[0566] "Means for analyzing data on a server and calculating the optimal time for putting a child to bed" refers to a method or device in which a server analyzes collected data and calculates the optimal time for putting a child to bed for each individual child.

[0567] "Means for generating advice based on calculated time for putting a child to sleep" refers to a system that generates specific instructions and advice for parents based on the time for putting a child to sleep calculated by the server.

[0568] "Means for notifying the parent's device of the generated advice" refers to means for notifying the parent's device, such as a smartphone or PC, of ​​the generated advice.

[0569] "Means for inputting dietary preferences and allergy information" refers to devices or software that allow parents to input information about their child's dietary preferences and allergies.

[0570] "Means for calculating meal contents and delivery time based on data analyzed by the server" refers to a method or device in which the server analyzes collected data and calculates the optimal meal contents and delivery time.

[0571] "Means for generating suggestions based on calculated meal contents and delivery times" refers to a system that generates specific suggestions to a user based on meal contents and delivery times calculated by the server.

[0572] "Means for notifying the user's device of the generated proposal" refers to means for notifying the user's device, such as a smartphone or PC, of ​​the generated proposal.

[0573] This invention is a system that suggests the optimal time for putting a child to bed based on data on the child's daily activities and physical condition, and also optimizes meal contents and delivery times. The components and operation procedures of the system are described in detail below.

[0574] Data collection

[0575] Parents use a dedicated application to enter data about their child's daily activities, food preferences, and allergies. This data includes, for example, mealtimes, playtimes, naptimes, and dinnertimes. Additionally, the system collects real-time health data (heart rate, body temperature, steps taken, etc.) from a wearable device worn by the child. This data is automatically sent to a smartphone or other device.

[0576] Sending data

[0577] All collected data is transmitted from the device via the internet to a server, where it is processed and analyzed using cloud services, particularly high-performance cloud computing platforms.

[0578] Data analysis and proposal generation

[0579] The server analyzes the collected data using a machine learning algorithm to calculate the optimal bedtime for each child. This machine learning model predicts the child's sleep patterns by studying past data. It also analyzes meal content and delivery times to generate optimal suggestions. Based on user input and real-time data from the wearable device, the server makes specific suggestions, such as "You're feeling well today, so a high-calorie meal would be appropriate for dinner."

[0580] Sending notifications

[0581] The analysis results and recommendations are sent from the server to the device and sent as push notifications to the parent's smartphone, etc. Parents can check these notifications and take specific actions to help their children maintain a healthy lifestyle.

[0582] Hardware and Software

[0583] The main hardware components of this system include a wearable device worn by the child, a smartphone or PC used by the parent, and a cloud server for analyzing the data. Software components include a mobile application for collecting and transmitting data, machine learning algorithms for analyzing the data, and cloud services. Specifically, Python, TensorFlow, and AWS (Amazon Web Services) are used.

[0584] Specific examples

[0585] For example, imagine a family with a three-year-old child. Parents use the app to input their child's breakfast time (8:00), playtime (10:00-11:00), naptime (13:00-14:00), dinnertime (18:00), etc. The wearable device collects data in real time, such as the child's calories burned, heart rate, and body temperature for the day. Based on this data, the server makes suggestions such as, "Today, you exercised a lot, so you need a nutritious meal for dinner to make up for the calories lost, and the optimal time to put your child to bed is 20:30." The parent also receives a link to order the suggested meal via a food delivery service.

[0586] Prompt Sentence Examples

[0587] You can get specific suggestions for your generative AI model by using prompts like the following:

[0588] "Please suggest optimal meals based on the generative AI model and user data.

[0589] User Data:

[0590] Children's meal times: Breakfast 8:00, Lunch 12:00, Dinner 18:00

[0591] Kids' Activities: School, Soccer Practice

[0592] Children's Allergies: Peanuts

[0593] Child's food preference: Vegetarian

[0594] Wearable device data:

[0595] Calories burned: 500

[0596] Body temperature: 36.5°C

[0597] Activity level: High

[0598] As described above, this system allows parents to receive optimal advice based on their child's daily activity and physical condition data, enabling them to effectively manage their health.

[0599] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0600] Step 1:

[0601] Through the application, parents input data about their child's daily activities, including mealtimes, playtimes, naptimes, dinnertimes, and their child's food preferences and allergies, which are then stored in a local database on the device.

[0602] Step 2:

[0603] The wearable device that the child wears collects real-time health data, including heart rate, body temperature, steps taken, calories burned, etc. The collected data is automatically sent to the device.

[0604] Step 3:

[0605] The device transmits all collected data to a server via the Internet, converting the collected daily activity data and physical condition data into an appropriate format and sending it to the server as a POST request using an API.

[0606] Step 4:

[0607] The server stores the received data in a database and analyzes it using a machine learning algorithm. Specifically, it learns a child's sleep and eating patterns based on past data and predicts the optimal time for putting them to bed and what to eat. The server's analysis process uses Python and TensorFlow, from data preprocessing to model training and prediction.

[0608] Step 5:

[0609] Based on the analysis results, the server generates the optimal time to put the child to bed and advice based on that, as well as suggestions based on the optimal meal content and delivery time. For example, specific advice and suggestions may be generated, such as "Since you exercised a lot today, a high-calorie meal would be appropriate for dinner" or "8:30 p.m. is the optimal time to put the child to bed today."

[0610] Step 6:

[0611] The server sends the generated advice and suggestions to the device and sends push notifications to the parent's smartphone, PC, or other device. The notifications include detailed instructions and suggestions for specific actions to take regarding the child's health. The device displays these notifications so that the parent can review the suggestions. Specifically, the device's push notification API is used to display the notifications.

[0612] Step 7:

[0613] The parent can then review the notification and take the action suggested by the server, such as ordering the suggested meal from a food delivery service and getting the child to bed based on the suggested bedtime. During this process, the parent can easily order the meal using the food delivery link within the app.

[0614] Through the above-mentioned series of processing steps, parents can receive optimal advice based on their child's daily activities and physical condition data, and can effectively manage their child's health.

[0615] 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.

[0616] MODE FOR CARRYING OUT THE INVENTION

[0617] This invention relates to a system that suggests the optimal time for putting a child to bed based on the child's daily activities and physical condition data, and also taking into consideration the emotional states of the user (parent) and the child. This system is realized by a server that analyzes input information from the parent, data collected from a wearable device, and emotional data generated by an emotion engine, and notifies the parent of the optimal time for putting the child to bed.

[0618] System Overview

[0619] 1. Data Collection

[0620] Parental input: Through a dedicated application, the "user" inputs their child's daily activity data, including mealtimes, playtime, naptime, study time, and screen time.

[0621] Wearable devices: Children wear wearable devices that collect real-time health data such as heart rate, body temperature, and number of steps taken. This data is automatically sent to a "terminal."

[0622] 2. Collecting Emotional Data

[0623] Parental Emotions: The device uses an emotion engine to recognize parental emotions through the application. Parental emotions are collected using text input and facial expression recognition technology.

[0624] Child's emotions: The Device will use facial and voice recognition technology through wearable devices or other appropriate sensors to recognize the child's emotions.

[0625] 3. Data transmission

[0626] All data is transmitted from the "terminal" to the "server" via the internet. The transmitted data includes parent input data, physical condition data from the wearable device, and emotional data of the parent and child.

[0627] 4. Data analysis and proposal generation

[0628] The "server" analyzes all the data it receives and runs machine learning algorithms to calculate the optimal bedtime for each child, taking into account past data and the current day's situation.

[0629] The server then uses the emotional data collected by the emotion engine to generate advice tailored to the parent's mental state and the child's mood. For example, if the parent is tired or the child is in a bad mood, it will adjust the timing and method of putting the child to bed.

[0630] 5. Sending notifications

[0631] The server generates specific advice based on the calculated optimal sleep time, which is then sent to the device and displayed to the user as a push notification.

[0632] Specific examples

[0633] For example, here is an example of a home with a 3-year-old child.

[0634] 1. Parent Input and Data Collection

[0635] "Users" use the app to input activity data such as their child's breakfast time (7:30), playtime (9:00-10:00), naptime (13:00-14:00), and dinnertime (18:00).

[0636] Children wear wearable devices that record their physical condition data, such as heart rate and body temperature, in real time.

[0637] "Users" input their emotional data into the app through facial recognition and text input.

[0638] Similarly, children's emotional data will be collected through wearable devices and other sensors.

[0639] 2. Data Transmission

[0640] All collected data is sent from the "terminal" to the "server."

[0641] 3. Data analysis and proposal generation

[0642] The server analyzes the data and calculates, for example, "Today, the child is very active and the parents are tired, so the optimal time to put the child to bed is 8:30 p.m." based on the child's activity level and physical condition that day, as well as the emotional state of the parent and child.

[0643] The "server" takes into account the parent's emotional state and generates specific advice such as "Play relaxing sounds at 7:30 p.m. and read picture books in bed at 8:30 p.m."

[0644] 4. Sending notifications

[0645] The "server" sends the generated advice to the "terminal," which then notifies the "user." The "user" checks this notification and adjusts the time for putting their child to bed based on the advice.

[0646] This system provides the optimal bedtime based on a child's daily activities and emotional state, reducing the burden on parents and supporting healthy sleep for children.

[0647] The processing flow will be explained below.

[0648] Step 1:

[0649] The "user" launches the smartphone app and enters the child's daily activity data for that day, including mealtimes, playtime, naptime, study time, and screen time.

[0650] Step 2:

[0651] The "user" puts the wearable device on the child.

[0652] Step 3:

[0653] The "terminal" collects real-time health data such as heart rate, body temperature, and number of steps from the wearable device.

[0654] Step 4:

[0655] To recognize the parent's emotions, the device uses an emotion engine to collect emotional data from the parent through the smartphone camera and text input, such as facial expression analysis and input of keywords that indicate emotions.

[0656] Step 5:

[0657] To recognize the child's emotions, the device uses an emotion engine to collect the child's emotional data through sensors in the wearable device and voice input.

[0658] Step 6:

[0659] The "terminal" compiles the collected daily activity data, physical condition data, and emotional data and sends it to the "server."

[0660] Step 7:

[0661] The "server" organizes the received data by date and time and prepares it for analysis.

[0662] Step 8:

[0663] The "server" references historical data sets and uses machine learning algorithms to analyze a child's sleep patterns, including daily activity, physical condition, and emotional data.

[0664] Step 9:

[0665] The "server" calculates the optimal bedtime for that day based on the latest data, for example suggesting an earlier bedtime if the child was more active, and takes into account the child's emotional state.

[0666] Step 10:

[0667] The server generates specific advice tailored to the emotions of the parent and child based on the emotional data collected by the emotion engine. For example, if the parent is tired, the advice may include "creating a relaxing environment."

[0668] Step 11:

[0669] The "server" transmits the generated advice to the "terminal."

[0670] Step 12:

[0671] The device displays advice to the user in the form of a notification message, including the time to put the child to sleep and specific suggested actions.

[0672] Step 13:

[0673] The "user" checks the notification and adjusts the child's bedtime based on the advice, for example, giving the child a bath at 7:30 p.m. and putting them to bed at 8:30 p.m.

[0674] Through these steps, the system will comprehensively analyze the child's daily activities, physical condition, and emotional state, and suggest the optimal bedtime, making it easier for parents to ensure their children get healthy sleep and reducing the burden of childcare.

[0675] Example 2

[0676] 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."

[0677] In modern society, it is important for parents to maintain healthy sleep patterns for their children. However, it is difficult to comprehensively understand a child's daily activities, physical condition, and emotional state to determine the optimal time for putting the child to bed. Furthermore, because a parent's own mental state also affects a child's sleep, there is a need for a method of putting the child to bed that takes this into consideration. Given this background, there is a need for a system that comprehensively analyzes a child's daily activity data, physical condition data, and emotional data to provide the optimal time for putting the child to bed and specific advice.

[0678] 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.

[0679] In this invention, the server includes means for having a parent input data on the child's daily activities, means for collecting the child's physical condition data from the wearable device, means for collecting emotional data of the parent and child, means for transmitting the collected data to a computer device, means for analyzing the data in the computer device and calculating an optimal time for putting the child to sleep, means for generating advice based on the calculated time for putting the child to sleep, and means for notifying the parent's information processing device of the generated advice. This allows the parent to know the optimal time for putting the child to sleep based on the child's daily activities and emotional state, and to receive specific advice.

[0680] "Means for parental input of data regarding the child's daily activities" refers to an input device or software function that allows a parent to input information about the child's daily activities, such as mealtimes, playtime, naptime, study time, and screen time.

[0681] "Means for collecting physical condition data of a child from a wearable device" refers to equipment and its functions for obtaining physical condition data such as heart rate, body temperature, and number of steps using a wearable device worn by a child.

[0682] "Means for collecting parent and child emotional data" refers to technologies and software functions for capturing the emotional states of parents and children using text input, facial expression recognition, and voice recognition technologies.

[0683] "Means for transmitting collected data to a computer device" refers to communication functions and protocols for transferring data collected from the parent's terminal or wearable device to a computer device such as a server via the Internet.

[0684] "Means for analyzing data using a computer device and calculating the optimal time for putting a child to sleep" refers to analytical technology and its software functions that allow a computer device such as a server to analyze collected data and use machine learning algorithms, etc. to derive the optimal time for putting a child to sleep.

[0685] The "means for generating advice based on the calculated sleep time" is an advice generation function for creating advice regarding specific sleep method and timing based on the analyzed optimal sleep time.

[0686] The "means for notifying the parent's information processing device of the generated advice" refers to a push notification function or communication means for notifying the parent's information processing device such as a smartphone or tablet of the generated advice.

[0687] MODE FOR CARRYING OUT THE INVENTION

[0688] The present invention relates to a system that suggests the optimal time for putting a child to bed based on data on the child's daily activities and physical condition, and also taking into consideration the emotional states of the parent and the child. This system is realized by a server that analyzes input information from the parent, data collected from a wearable device, and emotional data generated by an emotion engine, and notifies the parent of the optimal time for putting the child to bed.

[0689] The embodiments for carrying out the present invention are as follows.

[0690] Data collection

[0691] Parental input: Using a dedicated application, users input their child's daily activity data, including mealtimes, playtime, naptime, study time, screen time, etc. For example, breakfast time can be entered as "7:30" and playtime as "9:00-10:00."

[0692] Wearable devices: Children wear wearable devices (e.g., smartwatches, fitness trackers) that collect real-time physical data such as heart rate, body temperature, and number of steps. This data is automatically sent to the device.

[0693] Collecting Emotional Data

[0694] Parental Emotions: The device uses an emotion engine through the application to recognize parental emotions. Parental emotions are collected using text input and facial recognition technology (e.g., facial recognition API).

[0695] Child's emotions: The device uses facial and / or voice recognition technology in wearable devices or other appropriate sensors (e.g., voice recognition APIs) to recognize the child's emotions.

[0696] Sending data

[0697] All collected data is transmitted from the device to a server via the internet, including parental input data, physical condition data from the wearable device, and emotional data of the parent and child.

[0698] Data analysis and proposal generation

[0699] Data analysis: The server analyzes the received data and uses machine learning algorithms (e.g., TensorFlow) to calculate the optimal time for putting the child to sleep, taking into account past data and the current day's situation.

[0700] Suggestion generation: The server generates advice tailored to the parent's mental state and the child's mood based on the emotional data collected by the emotion engine. For example, if the parent is tired or the child is in a bad mood, the server provides advice on adjusting the timing and method of putting the child to bed.

[0701] Sending notifications

[0702] The server sends the generated advice to the device, which displays it to the user as a push notification. The user can then check the notification and adjust the bedtime based on the advice.

[0703] Specific examples

[0704] For example, here's an example from a home with a 3-year-old child:

[0705] 1. Parental input and data collection: Through the app, users input data such as their child's breakfast time (7:30), playtime (9:00-10:00), naptime (13:00-14:00), and dinnertime (18:00). Heart rate and body temperature data are recorded in real time from the wearable device worn by the child. Parents input their own emotional data into the app through facial recognition and text input. Emotional data about the child is also collected through the wearable device and other sensors.

[0706] 2. Data transmission: All collected data is transmitted from the device to the server.

[0707] 3. Data analysis and recommendation generation: The server analyzes the data and calculates, based on the child's activity level and physical condition that day, as well as the emotional state of the parent and child, that "Today, the child is very active and the parent is tired, so the optimal time to put the child to bed is 8:30 p.m." Taking into account the parent's emotional state, it generates specific advice such as "Play relaxing music at 7:30 p.m. and read a picture book in bed at 8:30 p.m."

[0708] 4. Sending notification: The server sends the generated advice to the device, and the device notifies the user as a push notification. The user checks the notification and adjusts their child's bedtime based on the advice.

[0709] Example prompts for generative AI models

[0710] "My child is 3 years old. Today's activity data is as follows: breakfast time is 7:30, playtime is 9:00-10:00, nap time is 13:00-14:00, and dinner time is 18:00. I'm a little tired, and my child is a little cranky. What's the best time to put him to bed? What's your advice?"

[0711] This system provides the optimal bedtime based on a child's daily activities and emotional state, reducing the burden on parents and supporting healthy sleep for children.

[0712] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0713] Program processing flow

[0714] Step 1:

[0715] A means for the user to input their child's daily activity data

[0716] Specific operations: The user opens a dedicated app on their smartphone and enters their child's daily activity data, such as breakfast time, playtime, nap time, and dinner time.

[0717] Example: "7:30" for breakfast time, "9:00-10:00" for playtime, etc.

[0718] Input: Child's daily activity data (e.g., breakfast time, playtime, etc.)

[0719] Output: The entered daily activity data is saved in the system.

[0720] Step 2:

[0721] A means for terminals to collect physical condition data from wearable devices

[0722] Specific operation: The device collects real-time physical condition data such as heart rate, body temperature, and number of steps taken via wearable devices (e.g., smart watches, fitness trackers) worn by children.

[0723] Example of how it works: After a child has completed an hour of exercise, their heart rate and temperature data are sent from the wearable device to the terminal.

[0724] Input: Real-time physical condition data received from a wearable device

[0725] Output: Collected physical condition data is saved to the device.

[0726] Step 3:

[0727] How the device collects parent and child emotional data

[0728] Specific operation: The device uses the application's emotion engine to collect the parent's emotions through text input and facial expression recognition, and the child's emotions through facial expression recognition and voice recognition technology.

[0729] How it works: Parents take pictures of their own faces using their smartphone cameras and use facial recognition technology to collect emotional data. Children's emotional data is analyzed from audio recordings of their play.

[0730] Input: Parent's emotional data (text input, facial expression recognition), Child's emotional data (facial expression recognition, voice recognition)

[0731] Output: Emotion data is recorded on the device.

[0732] Step 4:

[0733] A means for the device to send all collected data to a server

[0734] Specific operation: The terminal packetizes the collected parental input data, physical state data from the wearable device, and emotion data, and sends them to a server via the Internet.

[0735] Example of operation: Various data is collected into packets at regular intervals and transferred to the server.

[0736] Input: Parent input data, physical state data, emotion data

[0737] Output: The transmitted data arrives at the server.

[0738] Step 5:

[0739] The server analyzes the data and calculates the optimal time to put the child to sleep.

[0740] How it works: The server analyzes the received data and uses machine learning algorithms (e.g., TensorFlow) to calculate the optimal time for putting the child to bed based on the child's activity level, physical condition, and emotional state that day. The analysis also takes into account past data.

[0741] Example of operation: When the server receives the data, it first filters the emotional and physical condition data and compares it with the data from the previous day to determine the optimal time to put the child to sleep.

[0742] Input: All data sent to the server

[0743] Output: The optimal bedtime (e.g. 8:30pm) is calculated.

[0744] Step 6:

[0745] A means for the server to generate advice based on the calculation results and notify the device

[0746] Specific operation: Based on the calculated optimal time for putting the child to sleep, the server considers the emotional state of the parent and child and generates specific advice, such as "Play relaxing music at 7:30 PM and read a picture book in bed at 8:30 PM." The generated advice is sent to the device.

[0747] Example of operation: The advice generated by the server is packetized in text message format and notified to the terminal.

[0748] Input: Optimal bedtime, emotional state of parent and child

[0749] Output: Specific advice generated

[0750] Step 7:

[0751] A means for the device to notify the user of advice

[0752] Specific operation: The device displays the advice received from the server to the user as a push notification. The user checks the notification and adjusts their child's bedtime based on the advice.

[0753] Example of how it works: The device displays a push notification, which the user sees on their smartphone screen.

[0754] Input: Advice received from the server

[0755] Output: Advice given to the user

[0756] (Application example 2)

[0757] 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."

[0758] In the past, it was difficult for parents to determine the appropriate bedtime based on their child's daily activity and physical condition data. It was especially difficult to take into account the child's activity data and emotional state when visiting a physical store. It was also extremely difficult to grasp the child's emotional state in real time and make flexible suggestions based on that. To solve this problem, a system that comprehensively analyzes parent-child activity and emotional data and provides optimal advice is needed.

[0759] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for having a parent input data on the child's daily activities, means for collecting the child's physical condition data from a wearable device, means for transmitting the collected data to the server, means for analyzing the data in the server and calculating optimal rest times and home times, means for generating advice based on the calculated rest times, means for notifying the parent's terminal of the generated advice, means for inputting activity data of the parent and child in a physical store visited via a smartphone, and means for collecting emotional data of the parent and child using facial expression recognition and voice recognition technology. This makes it possible to comprehensively analyze the activity data and emotional data of the parent and child in the physical store and provide optimal advice in real time.

[0760] The "means for allowing parents to input data on their children's daily activities" provides an interface for parents to input information on their children's daily activities.

[0761] "Means for collecting children's health data from wearable devices" refers to a system that collects health data such as heart rate, body temperature, and number of steps in real time from wearable devices worn by children.

[0762] "Means for transmitting collected data to a server" refers to a communication means for transmitting all data collected from the parent and the wearable device to a server via the Internet.

[0763] "Means for analyzing data on the server and calculating optimal break times and times to go home" refers to a function that uses a machine learning algorithm to analyze the data received by the server and calculates optimal break times and times to go home based on the results.

[0764] The "means for generating advice based on the calculated rest time" is a mechanism for generating specific advice based on the optimal rest time calculated by the server.

[0765] The "means for notifying the parent's terminal of the generated advice" refers to a communication means for notifying the parent's terminal, such as a smartphone or tablet, of the advice generated by the server.

[0766] "Means for inputting activity data within physical stores visited by parents and children via smartphone" refers to an interface that allows parents to input information about their children's activities within physical stores using a smartphone app.

[0767] "Means for collecting parent-child emotional data using facial expression and voice recognition technology" refers to technology that uses smartphones or wearable devices to recognize the facial expressions and voices of parents and children and collect that emotional data.

[0768] This invention relates to a system that suggests optimal break times and times to go home in a physical store visited by parents and children, taking into consideration the child's daily activities and physical condition data, as well as the emotional states of the parents and children.

[0769] System Configuration

[0770] Hardware

[0771] Smartphone: Used by parents to input data about their children's daily activities and collect emotional data.

[0772] Wearable device: Worn by children, it collects real-time health data such as heart rate, body temperature, and number of steps taken.

[0773] Server: Analyzes data collected via the Internet and calculates optimal break times and times to go home.

[0774] software

[0775] Application development frameworks (e.g., React Native, Swift, Kotlin): Used to develop smartphone apps.

[0776] Machine learning libraries (e.g. TensorFlow, PyTorch): Used to perform data analysis on the server side.

[0777] Emotion recognition engine (e.g., Microsoft Azure Cognitive Services, IBM Watson): Analyzes parent and child emotion data.

[0778] Operation overview

[0779] Data collection

[0780] Parents enter their child's daily activity data on their smartphone. This data includes playtime, study time, mealtimes, etc. Additionally, the child's wearable device sends real-time health data, such as heart rate, body temperature, and number of steps, to the smartphone.

[0781] Collecting Emotional Data

[0782] The smartphone camera is used to recognize the facial expressions of parents and children, and voice recognition technology is used to collect emotional data, which is then analyzed by an emotion engine to understand the emotional state of parents and children.

[0783] Data transmission and analysis

[0784] All data is sent over the internet to a server, where machine learning algorithms are used to analyze the data and calculate optimal break times and times to go home.

[0785] Advice Generation and Notifications

[0786] Based on the results calculated on the server, specific advice is generated and sent to the parent's smartphone as a push notification. The advice also includes suggestions for making life more convenient for both parent and child.

[0787] Specific examples

[0788] For example, parents can input their child's activity information in a physical store into a smartphone app: for example, playtime (10:00-11:00), mealtime (12:00), etc. Also, if the child is wearing a wearable device, their physical condition data, such as heart rate, body temperature, and number of steps, is collected in real time on the smartphone.

[0789] It then uses the smartphone camera to recognize the facial expressions of the parent and child, and analyzes their voice to collect emotional data, and an emotion engine analyzes the emotional state of the parent and child.

[0790] All this data is sent to a server via the internet, which uses machine learning algorithms to calculate optimal break times and times to go home. For example, if the child is very active and the parent is tired, the server will generate advice such as, "Today, it would be good to take a break at 1 p.m., so that the parent can relax together."

[0791] The generated advice is sent to the parent's smartphone via push notification, and the parent can check this notification and adjust appropriate break times, etc.

[0792] Prompt Sentence Examples

[0793] "How long did your child play this morning?"

[0794] "The child's current temperature and heart rate are as follows."

[0795] "Parents, you seem tired. It would be good to take a break today."

[0796] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0797] Step 1: Data entry

[0798] The user (parent) uses a smartphone app to input their child's daily activity data, including mealtimes, playtime, study time, screen time, etc. The input data is saved in the app and sent to the next step.

[0799] Step 2: Collecting health data

[0800] The terminal (smartphone) collects the child's physical condition data in real time from the wearable device. This data includes heart rate, body temperature, number of steps, etc. The collected data is stored on the smartphone in real time and transmitted to the next step.

[0801] Step 3: Collecting emotion data

[0802] The device (smartphone) uses the smartphone's camera to recognize the facial expressions of the parent and child, and uses voice recognition technology to collect emotional data, which is then stored on the smartphone and sent to the next step.

[0803] Step 4: Send data

[0804] The device (smartphone) sends all collected data (activity data, physical condition data, emotional data) to a server via the Internet, where it is stored and prepared for analysis.

[0805] Step 5: Data analysis

[0806] The server receives all the data and begins analyzing it. Here, it uses machine learning algorithms to analyze the data and calculate the optimal break time and time for the child to return home. In particular, it processes and calculates the data based on past data and the situation on the day.

[0807] Step 6: Advice Generation

[0808] The server generates specific advice based on the results of the data analysis. Using a generative AI model, it creates advice tailored to the parent's mental state and the child's mood. For example, it might generate advice such as, "It's a good idea to take a break at 1 p.m. today. Let's relax together as parents."

[0809] Step 7: Send notification

[0810] The server sends the generated advice to the parent's device (smartphone) as a push notification. The device receives the notification and displays it so that the user can check it. The user can adjust their child's break time and activity schedule based on this notification.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] [Third embodiment]

[0815] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0816] 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.

[0817] 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).

[0818] 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.

[0819] 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.

[0820] 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).

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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."

[0827] MODE FOR CARRYING OUT THE INVENTION

[0828] This invention relates to a system that suggests the optimal time for putting a child to bed based on data on the child's daily activities and physical condition. The system is realized by having a server analyze information entered by the parent and data automatically collected from a wearable device, and then notifying the parent of the optimal time for putting the child to bed.

[0829] System Overview

[0830] 1. Data Collection

[0831] Parental input: Using a dedicated application, the "user" inputs data about their child's daily activities, such as mealtimes, playtime, naptime, study time, and screen time.

[0832] Wearable devices: Children wear wearable devices that collect real-time health data such as heart rate, body temperature, and number of steps taken. This data is automatically sent to a "terminal."

[0833] 2. Data transmission

[0834] All collected data is transmitted from the "terminal" to the "server" via the Internet.

[0835] 3. Data analysis and proposal generation

[0836] The server analyzes the collected data and calculates the optimal time for each child to go to bed. The server uses machine learning algorithms to learn a child's sleep patterns based on past data and predict the optimal time for that day.

[0837] The server generates specific sleep-related advice based on the analysis results, such as "To get your child to bed by 8:30 PM, it's best to take a bath before 8 PM."

[0838] 4. Sending notifications

[0839] The "server" sends the generated advice to the "terminal" and delivers it to the "user" as a push notification.

[0840] Specific examples

[0841] For example, consider a family with a three-year-old child.

[0842] 1. Parent Input and Data Collection

[0843] "Users" use the app to input their children's breakfast time (7:30), playtime (9:00-10:00), naptime (13:00-14:00), dinnertime (18:00), etc.

[0844] As the child wears the wearable device, the number of steps taken, heart rate, and body temperature for the day are automatically recorded on the "terminal."

[0845] 2. Data Transmission

[0846] These data are sent from the "terminal" to the "server."

[0847] 3. Data analysis and proposal generation

[0848] The server analyzes the data and takes into account the child's activity level and physical condition that day, calculating, for example, "Since the child was very active today, the optimal time to put the child to bed is 8:30 p.m."

[0849] The server also generates specific advice, such as "We recommend taking a bath from 7:30 p.m."

[0850] 4. Sending notifications

[0851] The "server" sends this advice to the "terminal", which then notifies the "user".

[0852] The "user" checks this notification, gives the child a bath at 7:30 p.m., and puts the child to bed at 8:30 p.m.

[0853] In this way, the system can calculate the optimal time for putting a child to bed based on the child's daily activities and physical condition data, and provide effective advice to parents in a timely manner, thereby helping parents ensure healthy sleep for their children and supporting their daily childcare.

[0854] The processing flow will be explained below.

[0855] Step 1:

[0856] The "user" launches the smartphone app and inputs their child's daily activity data for the day, including mealtimes, playtime, naptime, screen time, and study time.

[0857] Step 2:

[0858] The "user" puts the wearable device on the child.

[0859] Step 3:

[0860] The "terminal" collects physical data such as heart rate, body temperature, and number of steps from the wearable device in real time.

[0861] Step 4:

[0862] The "terminal" compiles the collected daily activity data and physical condition data and sends it to the "server."

[0863] Step 5:

[0864] The "server" organizes the data it receives by date and time.

[0865] Step 6:

[0866] The "server" references historical data sets and uses machine learning algorithms to analyze the child's sleep patterns.

[0867] Step 7:

[0868] The server uses the latest data to calculate the optimal time for putting a child to bed on that day. For example, if the child was very active on a day like that, it will suggest putting the child to bed earlier.

[0869] Step 8:

[0870] The server generates specific advice based on the calculated sleep time, such as "Take a bath from 7:30 PM and read a picture book in bed by 8:30 PM."

[0871] Step 9:

[0872] The "server" transmits the generated advice to the "terminal."

[0873] Step 10:

[0874] The "terminal" displays the advice as a notification message to the "user."

[0875] Step 11:

[0876] The "user" checks the notification and adjusts the child's bedtime based on the advice, for example, giving the child a bath at 7:30 p.m. and putting them to bed at 8:30 p.m.

[0877] These steps will help parents find the optimal bedtime for their child's individual needs and provide support in their daily parenting.

[0878] Example 1

[0879] 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."

[0880] For today's parents, putting their children to sleep effectively while maintaining their health is a major challenge. It is particularly difficult to centrally manage data on a child's daily activities and physical condition and determine the optimal time for putting them to sleep based on that data. It is also challenging to obtain specific advice that parents can immediately put into practice in their busy daily lives.

[0881] 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.

[0882] In this invention, the server includes means for having parents input data on their child's daily activities, means for collecting data on the child's physical condition from the wearable device, means for transmitting the collected data to an information processing device, means for analyzing the data in the information processing device and calculating an optimal time for putting the child to bed, means for generating advice based on the calculated time for putting the child to bed, means for notifying the parent's terminal of the generated advice, and means for ensuring security of data transmission. This makes it possible to calculate an optimal time for putting the child to bed based on the child's daily activities and physical condition data, and to provide effective advice to the parent.

[0883] "Means for parents to input data regarding their child's daily activities" refers to a function that allows parents to record their child's daily activities through a dedicated application or interface.

[0884] "Means for collecting children's health data from wearable devices" refers to a function that uses sensor-equipped devices (such as wristbands or smartwatches) worn by children to obtain health information such as heart rate, body temperature, and number of steps.

[0885] The "means for transmitting collected data to an information processing device" is a communication function for transmitting the daily activity data and physical condition data collected by the terminal to a server via the Internet.

[0886] "Means for analyzing data using an information processing device and calculating the optimal time for putting a child to bed" refers to a function that uses statistical analysis and machine learning models to calculate the optimal time for putting a child to bed based on data collected by the server.

[0887] The "means for generating advice based on the calculated time to put a child to sleep" is a function in which the server generates specific methods for putting a child to sleep and advice on daily life in text or image format based on the analysis results.

[0888] "Means for notifying the parent's device of the generated advice" is a function that sends the advice generated by the server to the parent's smartphone or tablet as a push notification.

[0889] "Measures to ensure the security of data transmission" refers to the use of encryption technology and secure communication protocols to prevent data from being leaked to third parties during transmission.

[0890] "Means for using machine learning algorithms to analyze past data and learn a child's sleep patterns" refers to a function in which the server uses machine learning algorithms (e.g., random forests and support vector machines) to analyze previously collected data and understand a child's sleep patterns and activity trends.

[0891] "Means for collecting data in real time and dynamically generating suggestions based on that data" refers to a function that processes data obtained from wearable devices and parental input in real time, and based on that, the server generates advice that is appropriate to the current situation.

[0892] MODE FOR CARRYING OUT THE INVENTION

[0893] This invention relates to a system that suggests the optimal time for putting a child to bed based on data on the child's daily activities and physical condition. This system is realized by an information processing device that analyzes information entered by the parent and data automatically collected from a wearable device, and notifies the parent of the optimal time for putting the child to bed.

[0894] System Overview

[0895] The system includes the following main components:

[0896] 1. A dedicated application for parents to enter data about their child's daily activities.

[0897] 2. Wearable devices worn by children (e.g., wristband sensors).

[0898] 3. A network for transmitting collected data to a central information processing device (server).

[0899] 4. A server with machine learning algorithms to analyze the data and calculate and suggest optimal bedtimes.

[0900] 5. A means of communication to notify the parent's device (smartphone or tablet) of calculated results and advice.

[0901] Data collection method

[0902] Parent Input

[0903] Users (parents) use a dedicated application to input data about their child's daily activities. This data includes meal times, playtime, nap time, study time, screen time, etc. For example, a parent might input information into the app such as "Feed breakfast at 7:30," "Played between 9:00 and 10:00," "Took a nap between 1:00 and 2:00," and "Eaten dinner at 6:00." This information is temporarily stored on the smartphone or tablet, and then prepared for the next data transmission.

[0904] Wearable device operation

[0905] The user has their child wear a wearable device (a device with sensors such as a smartwatch). This device collects real-time physical data such as the child's heart rate, body temperature, and number of steps taken, and automatically sends it to a terminal. For example, the device records that the child's number of steps for that day was 3,000, their average heart rate was 120 bpm, and their body temperature was 37.0 degrees.

[0906] Sending data

[0907] All collected data (parent input data and wearable device data) is sent to a server over the internet. The device encrypts the data using the secure HTTP protocol (HTTPS) and sends it to the server.

[0908] Analyzing data and generating recommendations

[0909] The server stores all received data in a database and uses machine learning algorithms to analyze past data and learn the child's sleep patterns. Typical database systems used are MySQL and PostgreSQL. The server analyzes the day's activity level and physical condition data to calculate the optimal time to put the child to bed. For example, it may calculate that "since you exercised a lot today, the optimal time to put the child to bed is 8:30 p.m." Based on the analysis results, the server then generates specific advice, such as "We recommend that you take a bath from 7:30 p.m."

[0910] Sending notifications

[0911] The server sends the generated advice to the device and delivers it to the user as a push notification. The user checks the notification and gives their child a bath at 7:30 PM and puts them to bed by 8:30 PM. In this way, the system can ensure healthy sleep for their child and support parents in their daily childcare.

[0912] Example prompt

[0913] "Enter data such as breakfast time (7:30), playtime (9:00-10:00), naptime (13:00-14:00), and dinnertime (18:00) for a 3-year-old child. Also, if the data from your wearable device shows that the number of steps taken that day was 3,000, the average heart rate was 120 bpm, and the body temperature was 37.0 degrees, please use this data to calculate the optimal time for putting the child to bed."

[0914] When this prompt is fed into a generative AI model, the results suggest optimal bedtimes and other advice.

[0915] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0916] Program processing flow

[0917] Step 1: Entering the Parent

[0918] 1. User (parent) opens the dedicated application:

[0919] The user launches an app called "A Child's Day" on their smartphone.

[0920] Input: Data about the child's daily activities (e.g., mealtimes, playtimes, naptimes, etc.).

[0921] Specific operation: The user enters information such as "breakfast at 7:30," "play from 9:00 to 10:00," "nap from 13:00 to 14:00," and "dinner at 18:00" into the app form.

[0922] Output: The input activity data is temporarily saved in the device.

[0923] Step 2: Collecting data from wearable devices

[0924] 2. Wearable device data collection:

[0925] The user has their child wear a wearable device (e.g., a smart watch).

[0926] Input: Biometric data collected by the worn device (e.g., heart rate, body temperature, steps, etc.).

[0927] Specific operation: The device periodically measures the child's heart rate, body temperature, steps, etc. and transmits the data to the terminal in real time.

[0928] Output: Physical condition data obtained in real time is recorded on the device.

[0929] Step 3: Send data to the server

[0930] 3. Sending data from the device to the server:

[0931] The device sends all collected data to the server.

[0932] Input: Daily activity data entered by parents, health data from wearable devices.

[0933] Specific operation: The device encrypts the data using the HTTPS protocol and sends it to the server over the Internet.

[0934] Output: The data is securely sent to the server and stored in a database.

[0935] Step 4: Data analysis

[0936] 4. Data analysis by the server:

[0937] The server analyzes the received data.

[0938] Input: Daily activity data and physical condition data stored on the server.

[0939] How it works: The server uses machine learning algorithms (e.g., random forests, support vector machines) to calculate sleep patterns and optimal bedtimes based on past and current data.

[0940] Output: The analysis results will generate the optimal time for putting the child to sleep and advice on daily life.

[0941] Step 5: Generate proposals

[0942] 5. Server Proposal Generation:

[0943] The server generates specific advice based on the analysis results.

[0944] Input: The results of the analysis performed by the server (e.g., optimal time to put the child to bed).

[0945] Specific behavior: The server uses the text generation model to generate advice such as, "Since you exercised a lot today, the best time to put your child to bed is 8:30 p.m." or "I recommend taking a bath from 7:30 p.m."

[0946] Output: The generated advice is saved in text format on the server.

[0947] Step 6: Sending notifications

[0948] 6. Sending notifications from the server to the device:

[0949] The server transmits the generated advice to the terminal.

[0950] Input: The generated and saved advice.

[0951] What it does: The server sends data to your smartphone or tablet in the form of a push notification.

[0952] Output: The advice is displayed on the user's terminal.

[0953] Through this process, the system can calculate the optimal time for putting a child to bed based on the child's daily activities and physical condition data, and provide effective advice to parents in a timely manner, helping them ensure healthy sleep for their children and providing effective support in their daily childcare.

[0954] (Application example 1)

[0955] 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."

[0956] In modern families, it is becoming increasingly important to suggest appropriate bedtimes based on daily activity and physical condition data in order to maintain healthy lifestyles for children. However, it is difficult for parents to manage this manually, and there are also few ways to optimize meal content and delivery times. Furthermore, there is no system that comprehensively analyzes this data and makes appropriate suggestions, so children's health is not being adequately supported. There is a need for a system that solves these issues.

[0957] 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.

[0958] In this invention, the server includes means for having a parent input data regarding the child's daily activities, means for collecting data on the child's physical condition from the wearable device, means for transmitting the collected data to the server, means for analyzing the data in the server and calculating an optimal bedtime, means for generating advice based on the calculated bedtime, means for notifying the parent's terminal of the generated advice, means for having a parent input information on dietary preferences and allergies, means for calculating meal contents and delivery times based on the data analyzed by the server, means for generating suggestions based on the calculated meal contents and delivery times, and means for notifying the user's terminal of the generated suggestions. This makes it possible to suggest an optimal bedtime based on the child's daily activities and physical condition data, and to optimize meal contents and delivery times.

[0959] "Means for parents to input data on their children's daily activities" refers to devices or software that allow parents to input data on their children's daily activities, such as mealtimes and playtimes.

[0960] "Means for collecting children's health data from wearable devices" refers to devices or systems for collecting health information such as heart rate, body temperature, and number of steps through wearable devices worn by children.

[0961] The "means for transmitting collected data to a server" refers to a device or protocol for transmitting collected daily activity data and physical condition data to a server via the Internet.

[0962] "Means for analyzing data on a server and calculating the optimal time for putting a child to bed" refers to a method or device in which a server analyzes collected data and calculates the optimal time for putting a child to bed for each individual child.

[0963] "Means for generating advice based on calculated time for putting a child to sleep" refers to a system that generates specific instructions and advice for parents based on the time for putting a child to sleep calculated by the server.

[0964] "Means for notifying the parent's device of the generated advice" refers to means for notifying the parent's device, such as a smartphone or PC, of ​​the generated advice.

[0965] "Means for inputting dietary preferences and allergy information" refers to devices or software that allow parents to input information about their child's dietary preferences and allergies.

[0966] "Means for calculating meal contents and delivery time based on data analyzed by the server" refers to a method or device in which the server analyzes collected data and calculates the optimal meal contents and delivery time.

[0967] "Means for generating suggestions based on calculated meal contents and delivery times" refers to a system that generates specific suggestions to a user based on meal contents and delivery times calculated by the server.

[0968] "Means for notifying the user's device of the generated proposal" refers to means for notifying the user's device, such as a smartphone or PC, of ​​the generated proposal.

[0969] This invention is a system that suggests the optimal time for putting a child to bed based on data on the child's daily activities and physical condition, and also optimizes meal contents and delivery times. The components and operation procedures of the system are described in detail below.

[0970] Data collection

[0971] Parents use a dedicated application to enter data about their child's daily activities, food preferences, and allergies. This data includes, for example, mealtimes, playtimes, naptimes, and dinnertimes. Additionally, the system collects real-time health data (heart rate, body temperature, steps taken, etc.) from a wearable device worn by the child. This data is automatically sent to a smartphone or other device.

[0972] Sending data

[0973] All collected data is transmitted from the device via the internet to a server, where it is processed and analyzed using cloud services, particularly high-performance cloud computing platforms.

[0974] Data analysis and proposal generation

[0975] The server analyzes the collected data using a machine learning algorithm to calculate the optimal bedtime for each child. This machine learning model predicts the child's sleep patterns by studying past data. It also analyzes meal content and delivery times to generate optimal suggestions. Based on user input and real-time data from the wearable device, the server makes specific suggestions, such as "You're feeling well today, so a high-calorie meal would be appropriate for dinner."

[0976] Sending notifications

[0977] The analysis results and recommendations are sent from the server to the device and sent as push notifications to the parent's smartphone, etc. Parents can check these notifications and take specific actions to help their children maintain a healthy lifestyle.

[0978] Hardware and Software

[0979] The main hardware components of this system include a wearable device worn by the child, a smartphone or PC used by the parent, and a cloud server for analyzing the data. Software components include a mobile application for collecting and transmitting data, machine learning algorithms for analyzing the data, and cloud services. Specifically, Python, TensorFlow, and AWS (Amazon Web Services) are used.

[0980] Specific examples

[0981] For example, imagine a family with a three-year-old child. Parents use the app to input their child's breakfast time (8:00), playtime (10:00-11:00), naptime (13:00-14:00), dinnertime (18:00), etc. The wearable device collects data in real time, such as the child's calories burned, heart rate, and body temperature for the day. Based on this data, the server makes suggestions such as, "Today, you exercised a lot, so you need a nutritious meal for dinner to make up for the calories lost, and the optimal time to put your child to bed is 20:30." The parent also receives a link to order the suggested meal via a food delivery service.

[0982] Prompt Sentence Examples

[0983] You can get specific suggestions for your generative AI model by using prompts like the following:

[0984] "Please suggest optimal meals based on the generative AI model and user data.

[0985] User Data:

[0986] Children's meal times: Breakfast 8:00, Lunch 12:00, Dinner 18:00

[0987] Kids' Activities: School, Soccer Practice

[0988] Children's Allergies: Peanuts

[0989] Child's food preference: Vegetarian

[0990] Wearable device data:

[0991] Calories burned: 500

[0992] Body temperature: 36.5°C

[0993] Activity level: High

[0994] As described above, this system allows parents to receive optimal advice based on their child's daily activity and physical condition data, enabling them to effectively manage their health.

[0995] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0996] Step 1:

[0997] Through the application, parents input data about their child's daily activities, including mealtimes, playtimes, naptimes, dinnertimes, and their child's food preferences and allergies, which are then stored in a local database on the device.

[0998] Step 2:

[0999] The wearable device that the child wears collects real-time health data, including heart rate, body temperature, steps taken, calories burned, etc. The collected data is automatically sent to the device.

[1000] Step 3:

[1001] The device transmits all collected data to a server via the Internet, converting the collected daily activity data and physical condition data into an appropriate format and sending it to the server as a POST request using an API.

[1002] Step 4:

[1003] The server stores the received data in a database and analyzes it using a machine learning algorithm. Specifically, it learns a child's sleep and eating patterns based on past data and predicts the optimal time for putting them to bed and what to eat. The server's analysis process uses Python and TensorFlow, from data preprocessing to model training and prediction.

[1004] Step 5:

[1005] Based on the analysis results, the server generates the optimal time to put the child to bed and advice based on that, as well as suggestions based on the optimal meal content and delivery time. For example, specific advice and suggestions may be generated, such as "Since you exercised a lot today, a high-calorie meal would be appropriate for dinner" or "8:30 p.m. is the optimal time to put the child to bed today."

[1006] Step 6:

[1007] The server sends the generated advice and suggestions to the device and sends push notifications to the parent's smartphone, PC, or other device. The notifications include detailed instructions and suggestions for specific actions to take regarding the child's health. The device displays these notifications so that the parent can review the suggestions. Specifically, the device's push notification API is used to display the notifications.

[1008] Step 7:

[1009] The parent can then review the notification and take the action suggested by the server, such as ordering the suggested meal from a food delivery service and getting the child to bed based on the suggested bedtime. During this process, the parent can easily order the meal using the food delivery link within the app.

[1010] Through the above-mentioned series of processing steps, parents can receive optimal advice based on their child's daily activities and physical condition data, and can effectively manage their child's health.

[1011] 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.

[1012] MODE FOR CARRYING OUT THE INVENTION

[1013] This invention relates to a system that suggests the optimal time for putting a child to bed based on the child's daily activities and physical condition data, and also taking into consideration the emotional states of the user (parent) and the child. This system is realized by a server that analyzes input information from the parent, data collected from a wearable device, and emotional data generated by an emotion engine, and notifies the parent of the optimal time for putting the child to bed.

[1014] System Overview

[1015] 1. Data Collection

[1016] Parental input: Through a dedicated application, the "user" inputs their child's daily activity data, including mealtimes, playtime, naptime, study time, and screen time.

[1017] Wearable devices: Children wear wearable devices that collect real-time health data such as heart rate, body temperature, and number of steps taken. This data is automatically sent to a "terminal."

[1018] 2. Collecting Emotional Data

[1019] Parental Emotions: The device uses an emotion engine to recognize parental emotions through the application. Parental emotions are collected using text input and facial expression recognition technology.

[1020] Child's emotions: The Device will use facial and voice recognition technology through wearable devices or other appropriate sensors to recognize the child's emotions.

[1021] 3. Data transmission

[1022] All data is transmitted from the "terminal" to the "server" via the internet. The transmitted data includes parent input data, physical condition data from the wearable device, and emotional data of the parent and child.

[1023] 4. Data analysis and proposal generation

[1024] The "server" analyzes all the data it receives and runs machine learning algorithms to calculate the optimal bedtime for each child, taking into account past data and the current day's situation.

[1025] The server then uses the emotional data collected by the emotion engine to generate advice tailored to the parent's mental state and the child's mood. For example, if the parent is tired or the child is in a bad mood, it will adjust the timing and method of putting the child to bed.

[1026] 5. Sending notifications

[1027] The server generates specific advice based on the calculated optimal sleep time, which is then sent to the device and displayed to the user as a push notification.

[1028] Specific examples

[1029] For example, here is an example of a home with a 3-year-old child.

[1030] 1. Parent Input and Data Collection

[1031] "Users" use the app to input activity data such as their child's breakfast time (7:30), playtime (9:00-10:00), naptime (13:00-14:00), and dinnertime (18:00).

[1032] Children wear wearable devices that record their physical condition data, such as heart rate and body temperature, in real time.

[1033] "Users" input their emotional data into the app through facial recognition and text input.

[1034] Similarly, children's emotional data will be collected through wearable devices and other sensors.

[1035] 2. Data Transmission

[1036] All collected data is sent from the "terminal" to the "server."

[1037] 3. Data analysis and proposal generation

[1038] The server analyzes the data and calculates, for example, "Today, the child is very active and the parents are tired, so the optimal time to put the child to bed is 8:30 p.m." based on the child's activity level and physical condition that day, as well as the emotional state of the parent and child.

[1039] The "server" takes into account the parent's emotional state and generates specific advice such as "Play relaxing sounds at 7:30 p.m. and read picture books in bed at 8:30 p.m."

[1040] 4. Sending notifications

[1041] The "server" sends the generated advice to the "terminal," which then notifies the "user." The "user" checks this notification and adjusts the time for putting their child to bed based on the advice.

[1042] This system provides the optimal bedtime based on a child's daily activities and emotional state, reducing the burden on parents and supporting healthy sleep for children.

[1043] The processing flow will be explained below.

[1044] Step 1:

[1045] The "user" launches the smartphone app and enters the child's daily activity data for that day, including mealtimes, playtime, naptime, study time, and screen time.

[1046] Step 2:

[1047] The "user" puts the wearable device on the child.

[1048] Step 3:

[1049] The "terminal" collects real-time health data such as heart rate, body temperature, and number of steps from the wearable device.

[1050] Step 4:

[1051] To recognize the parent's emotions, the device uses an emotion engine to collect emotional data from the parent through the smartphone camera and text input, such as facial expression analysis and input of keywords that indicate emotions.

[1052] Step 5:

[1053] To recognize the child's emotions, the device uses an emotion engine to collect the child's emotional data through sensors in the wearable device and voice input.

[1054] Step 6:

[1055] The "terminal" compiles the collected daily activity data, physical condition data, and emotional data and sends it to the "server."

[1056] Step 7:

[1057] The "server" organizes the received data by date and time and prepares it for analysis.

[1058] Step 8:

[1059] The "server" references historical data sets and uses machine learning algorithms to analyze a child's sleep patterns, including daily activity, physical condition, and emotional data.

[1060] Step 9:

[1061] The "server" calculates the optimal bedtime for that day based on the latest data, for example suggesting an earlier bedtime if the child was more active, and takes into account the child's emotional state.

[1062] Step 10:

[1063] The server generates specific advice tailored to the emotions of the parent and child based on the emotional data collected by the emotion engine. For example, if the parent is tired, the advice may include "creating a relaxing environment."

[1064] Step 11:

[1065] The "server" transmits the generated advice to the "terminal."

[1066] Step 12:

[1067] The device displays advice to the user in the form of a notification message, including the time to put the child to sleep and specific suggested actions.

[1068] Step 13:

[1069] The "user" checks the notification and adjusts the child's bedtime based on the advice, for example, giving the child a bath at 7:30 p.m. and putting them to bed at 8:30 p.m.

[1070] Through these steps, the system will comprehensively analyze the child's daily activities, physical condition, and emotional state, and suggest the optimal bedtime, making it easier for parents to ensure their children get healthy sleep and reducing the burden of childcare.

[1071] Example 2

[1072] 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."

[1073] In modern society, it is important for parents to maintain healthy sleep patterns for their children. However, it is difficult to comprehensively understand a child's daily activities, physical condition, and emotional state to determine the optimal time for putting the child to bed. Furthermore, because a parent's own mental state also affects a child's sleep, there is a need for a method of putting the child to bed that takes this into consideration. Given this background, there is a need for a system that comprehensively analyzes a child's daily activity data, physical condition data, and emotional data to provide the optimal time for putting the child to bed and specific advice.

[1074] 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.

[1075] In this invention, the server includes means for having a parent input data on the child's daily activities, means for collecting the child's physical condition data from the wearable device, means for collecting emotional data of the parent and child, means for transmitting the collected data to a computer device, means for analyzing the data in the computer device and calculating an optimal time for putting the child to sleep, means for generating advice based on the calculated time for putting the child to sleep, and means for notifying the parent's information processing device of the generated advice. This allows the parent to know the optimal time for putting the child to sleep based on the child's daily activities and emotional state, and to receive specific advice.

[1076] "Means for parental input of data regarding the child's daily activities" refers to an input device or software function that allows a parent to input information about the child's daily activities, such as mealtimes, playtime, naptime, study time, and screen time.

[1077] "Means for collecting physical condition data of a child from a wearable device" refers to equipment and its functions for obtaining physical condition data such as heart rate, body temperature, and number of steps using a wearable device worn by a child.

[1078] "Means for collecting parent and child emotional data" refers to technologies and software functions for capturing the emotional states of parents and children using text input, facial expression recognition, and voice recognition technologies.

[1079] "Means for transmitting collected data to a computer device" refers to communication functions and protocols for transferring data collected from the parent's terminal or wearable device to a computer device such as a server via the Internet.

[1080] "Means for analyzing data using a computer device and calculating the optimal time for putting a child to sleep" refers to analytical technology and its software functions that allow a computer device such as a server to analyze collected data and use machine learning algorithms, etc. to derive the optimal time for putting a child to sleep.

[1081] The "means for generating advice based on the calculated sleep time" is an advice generation function for creating advice regarding specific sleep method and timing based on the analyzed optimal sleep time.

[1082] The "means for notifying the parent's information processing device of the generated advice" refers to a push notification function or communication means for notifying the parent's information processing device such as a smartphone or tablet of the generated advice.

[1083] MODE FOR CARRYING OUT THE INVENTION

[1084] The present invention relates to a system that suggests the optimal time for putting a child to bed based on data on the child's daily activities and physical condition, and also taking into consideration the emotional states of the parent and the child. This system is realized by a server that analyzes input information from the parent, data collected from a wearable device, and emotional data generated by an emotion engine, and notifies the parent of the optimal time for putting the child to bed.

[1085] The embodiments for carrying out the present invention are as follows.

[1086] Data collection

[1087] Parental input: Using a dedicated application, users input their child's daily activity data, including mealtimes, playtime, naptime, study time, screen time, etc. For example, breakfast time can be entered as "7:30" and playtime as "9:00-10:00."

[1088] Wearable devices: Children wear wearable devices (e.g., smartwatches, fitness trackers) that collect real-time physical data such as heart rate, body temperature, and number of steps. This data is automatically sent to the device.

[1089] Collecting Emotional Data

[1090] Parental Emotions: The device uses an emotion engine through the application to recognize parental emotions. Parental emotions are collected using text input and facial recognition technology (e.g., facial recognition API).

[1091] Child's emotions: The device uses facial and / or voice recognition technology in wearable devices or other appropriate sensors (e.g., voice recognition APIs) to recognize the child's emotions.

[1092] Sending data

[1093] All collected data is transmitted from the device to a server via the internet, including parental input data, physical condition data from the wearable device, and emotional data of the parent and child.

[1094] Data analysis and proposal generation

[1095] Data analysis: The server analyzes the received data and uses machine learning algorithms (e.g., TensorFlow) to calculate the optimal time for putting the child to sleep, taking into account past data and the current day's situation.

[1096] Suggestion generation: The server generates advice tailored to the parent's mental state and the child's mood based on the emotional data collected by the emotion engine. For example, if the parent is tired or the child is in a bad mood, the server provides advice on adjusting the timing and method of putting the child to bed.

[1097] Sending notifications

[1098] The server sends the generated advice to the device, which displays it to the user as a push notification. The user can then check the notification and adjust the bedtime based on the advice.

[1099] Specific examples

[1100] For example, here's an example from a home with a 3-year-old child:

[1101] 1. Parental input and data collection: Through the app, users input data such as their child's breakfast time (7:30), playtime (9:00-10:00), naptime (13:00-14:00), and dinnertime (18:00). Heart rate and body temperature data are recorded in real time from the wearable device worn by the child. Parents input their own emotional data into the app through facial recognition and text input. Emotional data about the child is also collected through the wearable device and other sensors.

[1102] 2. Data transmission: All collected data is transmitted from the device to the server.

[1103] 3. Data analysis and recommendation generation: The server analyzes the data and calculates, based on the child's activity level and physical condition that day, as well as the emotional state of the parent and child, that "Today, the child is very active and the parent is tired, so the optimal time to put the child to bed is 8:30 p.m." Taking into account the parent's emotional state, it generates specific advice such as "Play relaxing music at 7:30 p.m. and read a picture book in bed at 8:30 p.m."

[1104] 4. Sending notification: The server sends the generated advice to the device, and the device notifies the user as a push notification. The user checks the notification and adjusts their child's bedtime based on the advice.

[1105] Example prompts for generative AI models

[1106] "My child is 3 years old. Today's activity data is as follows: breakfast time is 7:30, playtime is 9:00-10:00, nap time is 13:00-14:00, and dinner time is 18:00. I'm a little tired, and my child is a little cranky. What's the best time to put him to bed? What's your advice?"

[1107] This system provides the optimal bedtime based on a child's daily activities and emotional state, reducing the burden on parents and supporting healthy sleep for children.

[1108] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1109] Program processing flow

[1110] Step 1:

[1111] A means for the user to input their child's daily activity data

[1112] Specific operations: The user opens a dedicated app on their smartphone and enters their child's daily activity data, such as breakfast time, playtime, nap time, and dinner time.

[1113] Example: "7:30" for breakfast time, "9:00-10:00" for playtime, etc.

[1114] Input: Child's daily activity data (e.g., breakfast time, playtime, etc.)

[1115] Output: The entered daily activity data is saved in the system.

[1116] Step 2:

[1117] A means for terminals to collect physical condition data from wearable devices

[1118] Specific operation: The device collects real-time physical condition data such as heart rate, body temperature, and number of steps taken via wearable devices (e.g., smart watches, fitness trackers) worn by children.

[1119] Example of how it works: After a child has completed an hour of exercise, their heart rate and temperature data are sent from the wearable device to the terminal.

[1120] Input: Real-time physical condition data received from a wearable device

[1121] Output: Collected physical condition data is saved to the device.

[1122] Step 3:

[1123] How the device collects parent and child emotional data

[1124] Specific operation: The device uses the application's emotion engine to collect the parent's emotions through text input and facial expression recognition, and the child's emotions through facial expression recognition and voice recognition technology.

[1125] How it works: Parents take pictures of their own faces using their smartphone cameras and use facial recognition technology to collect emotional data. Children's emotional data is analyzed from audio recordings of their play.

[1126] Input: Parent's emotional data (text input, facial expression recognition), Child's emotional data (facial expression recognition, voice recognition)

[1127] Output: Emotion data is recorded on the device.

[1128] Step 4:

[1129] A means for the device to send all collected data to a server

[1130] Specific operation: The terminal packetizes the collected parental input data, physical state data from the wearable device, and emotion data, and sends them to a server via the Internet.

[1131] Example of operation: Various data is collected into packets at regular intervals and transferred to the server.

[1132] Input: Parent input data, physical state data, emotion data

[1133] Output: The transmitted data arrives at the server.

[1134] Step 5:

[1135] The server analyzes the data and calculates the optimal time to put the child to sleep.

[1136] How it works: The server analyzes the received data and uses machine learning algorithms (e.g., TensorFlow) to calculate the optimal time for putting the child to bed based on the child's activity level, physical condition, and emotional state that day. The analysis also takes into account past data.

[1137] Example of operation: When the server receives the data, it first filters the emotional and physical condition data and compares it with the data from the previous day to determine the optimal time to put the child to sleep.

[1138] Input: All data sent to the server

[1139] Output: The optimal bedtime (e.g. 8:30pm) is calculated.

[1140] Step 6:

[1141] A means for the server to generate advice based on the calculation results and notify the device

[1142] Specific operation: Based on the calculated optimal time for putting the child to sleep, the server considers the emotional state of the parent and child and generates specific advice, such as "Play relaxing music at 7:30 PM and read a picture book in bed at 8:30 PM." The generated advice is sent to the device.

[1143] Example of operation: The advice generated by the server is packetized in text message format and notified to the terminal.

[1144] Input: Optimal bedtime, emotional state of parent and child

[1145] Output: Specific advice generated

[1146] Step 7:

[1147] A means for the device to notify the user of advice

[1148] Specific operation: The device displays the advice received from the server to the user as a push notification. The user checks the notification and adjusts their child's bedtime based on the advice.

[1149] Example of how it works: The device displays a push notification, which the user sees on their smartphone screen.

[1150] Input: Advice received from the server

[1151] Output: Advice given to the user

[1152] (Application example 2)

[1153] 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."

[1154] In the past, it was difficult for parents to determine the appropriate bedtime based on their child's daily activity and physical condition data. It was especially difficult to take into account the child's activity data and emotional state when visiting a physical store. It was also extremely difficult to grasp the child's emotional state in real time and make flexible suggestions based on that. To solve this problem, a system that comprehensively analyzes parent-child activity and emotional data and provides optimal advice is needed.

[1155] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for having a parent input data on the child's daily activities, means for collecting the child's physical condition data from a wearable device, means for transmitting the collected data to the server, means for analyzing the data in the server and calculating optimal rest times and home times, means for generating advice based on the calculated rest times, means for notifying the parent's terminal of the generated advice, means for inputting activity data of the parent and child in a physical store visited via a smartphone, and means for collecting emotional data of the parent and child using facial expression recognition and voice recognition technology. This makes it possible to comprehensively analyze the activity data and emotional data of the parent and child in the physical store and provide optimal advice in real time.

[1156] The "means for allowing parents to input data on their children's daily activities" provides an interface for parents to input information on their children's daily activities.

[1157] "Means for collecting children's health data from wearable devices" refers to a system that collects health data such as heart rate, body temperature, and number of steps in real time from wearable devices worn by children.

[1158] "Means for transmitting collected data to a server" refers to a communication means for transmitting all data collected from the parent and the wearable device to a server via the Internet.

[1159] "Means for analyzing data on the server and calculating optimal break times and times to go home" refers to a function that uses a machine learning algorithm to analyze the data received by the server and calculates optimal break times and times to go home based on the results.

[1160] The "means for generating advice based on the calculated rest time" is a mechanism for generating specific advice based on the optimal rest time calculated by the server.

[1161] The "means for notifying the parent's terminal of the generated advice" refers to a communication means for notifying the parent's terminal, such as a smartphone or tablet, of the advice generated by the server.

[1162] "Means for inputting activity data within physical stores visited by parents and children via smartphone" refers to an interface that allows parents to input information about their children's activities within physical stores using a smartphone app.

[1163] "Means for collecting parent-child emotional data using facial expression and voice recognition technology" refers to technology that uses smartphones or wearable devices to recognize the facial expressions and voices of parents and children and collect that emotional data.

[1164] This invention relates to a system that suggests optimal break times and times to go home in a physical store visited by parents and children, taking into consideration the child's daily activities and physical condition data, as well as the emotional states of the parents and children.

[1165] System Configuration

[1166] Hardware

[1167] Smartphone: Used by parents to input data about their children's daily activities and collect emotional data.

[1168] Wearable device: Worn by children, it collects real-time health data such as heart rate, body temperature, and number of steps taken.

[1169] Server: Analyzes data collected via the Internet and calculates optimal break times and times to go home.

[1170] software

[1171] Application development frameworks (e.g., React Native, Swift, Kotlin): Used to develop smartphone apps.

[1172] Machine learning libraries (e.g. TensorFlow, PyTorch): Used to perform data analysis on the server side.

[1173] Emotion recognition engine (e.g., Microsoft Azure Cognitive Services, IBM Watson): Analyzes parent and child emotion data.

[1174] Operation overview

[1175] Data collection

[1176] Parents enter their child's daily activity data on their smartphone. This data includes playtime, study time, mealtimes, etc. Additionally, the child's wearable device sends real-time health data, such as heart rate, body temperature, and number of steps, to the smartphone.

[1177] Collecting Emotional Data

[1178] The smartphone camera is used to recognize the facial expressions of parents and children, and voice recognition technology is used to collect emotional data, which is then analyzed by an emotion engine to understand the emotional state of parents and children.

[1179] Data transmission and analysis

[1180] All data is sent over the internet to a server, where machine learning algorithms are used to analyze the data and calculate optimal break times and times to go home.

[1181] Advice Generation and Notifications

[1182] Based on the results calculated on the server, specific advice is generated and sent to the parent's smartphone as a push notification. The advice also includes suggestions for making life more convenient for both parent and child.

[1183] Specific examples

[1184] For example, parents can input their child's activity information in a physical store into a smartphone app: for example, playtime (10:00-11:00), mealtime (12:00), etc. Also, if the child is wearing a wearable device, their physical condition data, such as heart rate, body temperature, and number of steps, is collected in real time on the smartphone.

[1185] It then uses the smartphone camera to recognize the facial expressions of the parent and child, and analyzes their voice to collect emotional data, and an emotion engine analyzes the emotional state of the parent and child.

[1186] All this data is sent to a server via the internet, which uses machine learning algorithms to calculate optimal break times and times to go home. For example, if the child is very active and the parent is tired, the server will generate advice such as, "Today, it would be good to take a break at 1 p.m., so that the parent can relax together."

[1187] The generated advice is sent to the parent's smartphone via push notification, and the parent can check this notification and adjust appropriate break times, etc.

[1188] Prompt Sentence Examples

[1189] "How long did your child play this morning?"

[1190] "The child's current temperature and heart rate are as follows."

[1191] "Parents, you seem tired. It would be good to take a break today."

[1192] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1193] Step 1: Data entry

[1194] The user (parent) uses a smartphone app to input their child's daily activity data, including mealtimes, playtime, study time, screen time, etc. The input data is saved in the app and sent to the next step.

[1195] Step 2: Collecting health data

[1196] The terminal (smartphone) collects the child's physical condition data in real time from the wearable device. This data includes heart rate, body temperature, number of steps, etc. The collected data is stored on the smartphone in real time and transmitted to the next step.

[1197] Step 3: Collecting emotion data

[1198] The device (smartphone) uses the smartphone's camera to recognize the facial expressions of the parent and child, and uses voice recognition technology to collect emotional data, which is then stored on the smartphone and sent to the next step.

[1199] Step 4: Send data

[1200] The device (smartphone) sends all collected data (activity data, physical condition data, emotional data) to a server via the Internet, where it is stored and prepared for analysis.

[1201] Step 5: Data analysis

[1202] The server receives all the data and begins analyzing it. Here, it uses machine learning algorithms to analyze the data and calculate the optimal break time and time for the child to return home. In particular, it processes and calculates the data based on past data and the situation on the day.

[1203] Step 6: Advice Generation

[1204] The server generates specific advice based on the results of the data analysis. Using a generative AI model, it creates advice tailored to the parent's mental state and the child's mood. For example, it might generate advice such as, "It's a good idea to take a break at 1 p.m. today. Let's relax together as parents."

[1205] Step 7: Send notification

[1206] The server sends the generated advice to the parent's device (smartphone) as a push notification. The device receives the notification and displays it so that the user can check it. The user can adjust their child's break time and activity schedule based on this notification.

[1207] 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.

[1208] 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.

[1209] 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.

[1210] [Fourth embodiment]

[1211] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1212] 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.

[1213] 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).

[1214] 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.

[1215] 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.

[1216] 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).

[1217] 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.

[1218] 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.

[1219] 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.

[1220] 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.

[1221] 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.

[1222] 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.

[1223] 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."

[1224] MODE FOR CARRYING OUT THE INVENTION

[1225] This invention relates to a system that suggests the optimal time for putting a child to bed based on data on the child's daily activities and physical condition. The system is realized by having a server analyze information entered by the parent and data automatically collected from a wearable device, and then notifying the parent of the optimal time for putting the child to bed.

[1226] System Overview

[1227] 1. Data Collection

[1228] Parental input: Using a dedicated application, the "user" inputs data about their child's daily activities, such as mealtimes, playtime, naptime, study time, and screen time.

[1229] Wearable devices: Children wear wearable devices that collect real-time health data such as heart rate, body temperature, and number of steps taken. This data is automatically sent to a "terminal."

[1230] 2. Data transmission

[1231] All collected data is transmitted from the "terminal" to the "server" via the Internet.

[1232] 3. Data analysis and proposal generation

[1233] The server analyzes the collected data and calculates the optimal time for each child to go to bed. The server uses machine learning algorithms to learn a child's sleep patterns based on past data and predict the optimal time for that day.

[1234] The server generates specific sleep-related advice based on the analysis results, such as "To get your child to bed by 8:30 PM, it's best to take a bath before 8 PM."

[1235] 4. Sending notifications

[1236] The "server" sends the generated advice to the "terminal" and delivers it to the "user" as a push notification.

[1237] Specific examples

[1238] For example, consider a family with a three-year-old child.

[1239] 1. Parent Input and Data Collection

[1240] "Users" use the app to input their children's breakfast time (7:30), playtime (9:00-10:00), naptime (13:00-14:00), dinnertime (18:00), etc.

[1241] As the child wears the wearable device, the number of steps taken, heart rate, and body temperature for the day are automatically recorded on the "terminal."

[1242] 2. Data Transmission

[1243] These data are sent from the "terminal" to the "server."

[1244] 3. Data analysis and proposal generation

[1245] The server analyzes the data and takes into account the child's activity level and physical condition that day, calculating, for example, "Since the child was very active today, the optimal time to put the child to bed is 8:30 p.m."

[1246] The server also generates specific advice, such as "We recommend taking a bath from 7:30 p.m."

[1247] 4. Sending notifications

[1248] The "server" sends this advice to the "terminal", which then notifies the "user".

[1249] The "user" checks this notification, gives the child a bath at 7:30 p.m., and puts the child to bed at 8:30 p.m.

[1250] In this way, the system can calculate the optimal time for putting a child to bed based on the child's daily activities and physical condition data, and provide effective advice to parents in a timely manner, thereby helping parents ensure healthy sleep for their children and supporting their daily childcare.

[1251] The processing flow will be explained below.

[1252] Step 1:

[1253] The "user" launches the smartphone app and inputs their child's daily activity data for the day, including mealtimes, playtime, naptime, screen time, and study time.

[1254] Step 2:

[1255] The "user" puts the wearable device on the child.

[1256] Step 3:

[1257] The "terminal" collects physical data such as heart rate, body temperature, and number of steps from the wearable device in real time.

[1258] Step 4:

[1259] The "terminal" compiles the collected daily activity data and physical condition data and sends it to the "server."

[1260] Step 5:

[1261] The "server" organizes the data it receives by date and time.

[1262] Step 6:

[1263] The "server" references historical data sets and uses machine learning algorithms to analyze the child's sleep patterns.

[1264] Step 7:

[1265] The server uses the latest data to calculate the optimal time for putting a child to bed on that day. For example, if the child was very active on a day like that, it will suggest putting the child to bed earlier.

[1266] Step 8:

[1267] The server generates specific advice based on the calculated sleep time, such as "Take a bath from 7:30 PM and read a picture book in bed by 8:30 PM."

[1268] Step 9:

[1269] The "server" transmits the generated advice to the "terminal."

[1270] Step 10:

[1271] The "terminal" displays the advice as a notification message to the "user."

[1272] Step 11:

[1273] The "user" checks the notification and adjusts the child's bedtime based on the advice, for example, giving the child a bath at 7:30 p.m. and putting them to bed at 8:30 p.m.

[1274] These steps will help parents find the optimal bedtime for their child's individual needs and provide support in their daily parenting.

[1275] Example 1

[1276] 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."

[1277] For today's parents, putting their children to sleep effectively while maintaining their health is a major challenge. It is particularly difficult to centrally manage data on a child's daily activities and physical condition and determine the optimal time for putting them to sleep based on that data. It is also challenging to obtain specific advice that parents can immediately put into practice in their busy daily lives.

[1278] 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.

[1279] In this invention, the server includes means for having parents input data on their child's daily activities, means for collecting data on the child's physical condition from the wearable device, means for transmitting the collected data to an information processing device, means for analyzing the data in the information processing device and calculating an optimal time for putting the child to bed, means for generating advice based on the calculated time for putting the child to bed, means for notifying the parent's terminal of the generated advice, and means for ensuring security of data transmission. This makes it possible to calculate an optimal time for putting the child to bed based on the child's daily activities and physical condition data, and to provide effective advice to the parent.

[1280] "Means for parents to input data regarding their child's daily activities" refers to a function that allows parents to record their child's daily activities through a dedicated application or interface.

[1281] "Means for collecting children's health data from wearable devices" refers to a function that uses sensor-equipped devices (such as wristbands or smartwatches) worn by children to obtain health information such as heart rate, body temperature, and number of steps.

[1282] The "means for transmitting collected data to an information processing device" is a communication function for transmitting the daily activity data and physical condition data collected by the terminal to a server via the Internet.

[1283] "Means for analyzing data using an information processing device and calculating the optimal time for putting a child to bed" refers to a function that uses statistical analysis and machine learning models to calculate the optimal time for putting a child to bed based on data collected by the server.

[1284] The "means for generating advice based on the calculated time to put a child to sleep" is a function in which the server generates specific methods for putting a child to sleep and advice on daily life in text or image format based on the analysis results.

[1285] "Means for notifying the parent's device of the generated advice" is a function that sends the advice generated by the server to the parent's smartphone or tablet as a push notification.

[1286] "Measures to ensure the security of data transmission" refers to the use of encryption technology and secure communication protocols to prevent data from being leaked to third parties during transmission.

[1287] "Means for using machine learning algorithms to analyze past data and learn a child's sleep patterns" refers to a function in which the server uses machine learning algorithms (e.g., random forests and support vector machines) to analyze previously collected data and understand a child's sleep patterns and activity trends.

[1288] "Means for collecting data in real time and dynamically generating suggestions based on that data" refers to a function that processes data obtained from wearable devices and parental input in real time, and based on that, the server generates advice that is appropriate to the current situation.

[1289] MODE FOR CARRYING OUT THE INVENTION

[1290] This invention relates to a system that suggests the optimal time for putting a child to bed based on data on the child's daily activities and physical condition. This system is realized by an information processing device that analyzes information entered by the parent and data automatically collected from a wearable device, and notifies the parent of the optimal time for putting the child to bed.

[1291] System Overview

[1292] The system includes the following main components:

[1293] 1. A dedicated application for parents to enter data about their child's daily activities.

[1294] 2. Wearable devices worn by children (e.g., wristband sensors).

[1295] 3. A network for transmitting collected data to a central information processing device (server).

[1296] 4. A server with machine learning algorithms to analyze the data and calculate and suggest optimal bedtimes.

[1297] 5. A means of communication to notify the parent's device (smartphone or tablet) of calculated results and advice.

[1298] Data collection method

[1299] Parent Input

[1300] Users (parents) use a dedicated application to input data about their child's daily activities. This data includes meal times, playtime, nap time, study time, screen time, etc. For example, a parent might input information into the app such as "Feed breakfast at 7:30," "Played between 9:00 and 10:00," "Took a nap between 1:00 and 2:00," and "Eaten dinner at 6:00." This information is temporarily stored on the smartphone or tablet, and then prepared for the next data transmission.

[1301] Wearable device operation

[1302] The user has their child wear a wearable device (a device with sensors such as a smartwatch). This device collects real-time physical data such as the child's heart rate, body temperature, and number of steps taken, and automatically sends it to a terminal. For example, the device records that the child's number of steps for that day was 3,000, their average heart rate was 120 bpm, and their body temperature was 37.0 degrees.

[1303] Sending data

[1304] All collected data (parent input data and wearable device data) is sent to a server over the internet. The device encrypts the data using the secure HTTP protocol (HTTPS) and sends it to the server.

[1305] Analyzing data and generating recommendations

[1306] The server stores all received data in a database and uses machine learning algorithms to analyze past data and learn the child's sleep patterns. Typical database systems used are MySQL and PostgreSQL. The server analyzes the day's activity level and physical condition data to calculate the optimal time to put the child to bed. For example, it may calculate that "since you exercised a lot today, the optimal time to put the child to bed is 8:30 p.m." Based on the analysis results, the server then generates specific advice, such as "We recommend that you take a bath from 7:30 p.m."

[1307] Sending notifications

[1308] The server sends the generated advice to the device and delivers it to the user as a push notification. The user checks the notification and gives their child a bath at 7:30 PM and puts them to bed by 8:30 PM. In this way, the system can ensure healthy sleep for their child and support parents in their daily childcare.

[1309] Example prompt

[1310] "Enter data such as breakfast time (7:30), playtime (9:00-10:00), naptime (13:00-14:00), and dinnertime (18:00) for a 3-year-old child. Also, if the data from your wearable device shows that the number of steps taken that day was 3,000, the average heart rate was 120 bpm, and the body temperature was 37.0 degrees, please use this data to calculate the optimal time for putting the child to bed."

[1311] When this prompt is fed into a generative AI model, the results suggest optimal bedtimes and other advice.

[1312] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1313] Program processing flow

[1314] Step 1: Entering the Parent

[1315] 1. User (parent) opens the dedicated application:

[1316] The user launches an app called "A Child's Day" on their smartphone.

[1317] Input: Data about the child's daily activities (e.g., mealtimes, playtimes, naptimes, etc.).

[1318] Specific operation: The user enters information such as "breakfast at 7:30," "play from 9:00 to 10:00," "nap from 13:00 to 14:00," and "dinner at 18:00" into the app form.

[1319] Output: The input activity data is temporarily saved in the device.

[1320] Step 2: Collecting data from wearable devices

[1321] 2. Wearable device data collection:

[1322] The user has their child wear a wearable device (e.g., a smart watch).

[1323] Input: Biometric data collected by the worn device (e.g., heart rate, body temperature, steps, etc.).

[1324] Specific operation: The device periodically measures the child's heart rate, body temperature, steps, etc. and transmits the data to the terminal in real time.

[1325] Output: Physical condition data obtained in real time is recorded on the device.

[1326] Step 3: Send data to the server

[1327] 3. Sending data from the device to the server:

[1328] The device sends all collected data to the server.

[1329] Input: Daily activity data entered by parents, health data from wearable devices.

[1330] Specific operation: The device encrypts the data using the HTTPS protocol and sends it to the server over the Internet.

[1331] Output: The data is securely sent to the server and stored in a database.

[1332] Step 4: Data analysis

[1333] 4. Data analysis by the server:

[1334] The server analyzes the received data.

[1335] Input: Daily activity data and physical condition data stored on the server.

[1336] How it works: The server uses machine learning algorithms (e.g., random forests, support vector machines) to calculate sleep patterns and optimal bedtimes based on past and current data.

[1337] Output: The analysis results will generate the optimal time for putting the child to sleep and advice on daily life.

[1338] Step 5: Generate proposals

[1339] 5. Server Proposal Generation:

[1340] The server generates specific advice based on the analysis results.

[1341] Input: The results of the analysis performed by the server (e.g., optimal time to put the child to bed).

[1342] Specific behavior: The server uses the text generation model to generate advice such as, "Since you exercised a lot today, the best time to put your child to bed is 8:30 p.m." or "I recommend taking a bath from 7:30 p.m."

[1343] Output: The generated advice is saved in text format on the server.

[1344] Step 6: Sending notifications

[1345] 6. Sending notifications from the server to the device:

[1346] The server transmits the generated advice to the terminal.

[1347] Input: The generated and saved advice.

[1348] What it does: The server sends data to your smartphone or tablet in the form of a push notification.

[1349] Output: The advice is displayed on the user's terminal.

[1350] Through this process, the system can calculate the optimal time for putting a child to bed based on the child's daily activities and physical condition data, and provide effective advice to parents in a timely manner, helping them ensure healthy sleep for their children and providing effective support in their daily childcare.

[1351] (Application example 1)

[1352] 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."

[1353] In modern families, it is becoming increasingly important to suggest appropriate bedtimes based on daily activity and physical condition data in order to maintain healthy lifestyles for children. However, it is difficult for parents to manage this manually, and there are also few ways to optimize meal content and delivery times. Furthermore, there is no system that comprehensively analyzes this data and makes appropriate suggestions, so children's health is not being adequately supported. There is a need for a system that solves these issues.

[1354] 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.

[1355] In this invention, the server includes means for having a parent input data regarding the child's daily activities, means for collecting data on the child's physical condition from the wearable device, means for transmitting the collected data to the server, means for analyzing the data in the server and calculating an optimal bedtime, means for generating advice based on the calculated bedtime, means for notifying the parent's terminal of the generated advice, means for having a parent input information on dietary preferences and allergies, means for calculating meal contents and delivery times based on the data analyzed by the server, means for generating suggestions based on the calculated meal contents and delivery times, and means for notifying the user's terminal of the generated suggestions. This makes it possible to suggest an optimal bedtime based on the child's daily activities and physical condition data, and to optimize meal contents and delivery times.

[1356] "Means for parents to input data on their children's daily activities" refers to devices or software that allow parents to input data on their children's daily activities, such as mealtimes and playtimes.

[1357] "Means for collecting children's health data from wearable devices" refers to devices or systems for collecting health information such as heart rate, body temperature, and number of steps through wearable devices worn by children.

[1358] The "means for transmitting collected data to a server" refers to a device or protocol for transmitting collected daily activity data and physical condition data to a server via the Internet.

[1359] "Means for analyzing data on a server and calculating the optimal time for putting a child to bed" refers to a method or device in which a server analyzes collected data and calculates the optimal time for putting a child to bed for each individual child.

[1360] "Means for generating advice based on calculated time for putting a child to sleep" refers to a system that generates specific instructions and advice for parents based on the time for putting a child to sleep calculated by the server.

[1361] "Means for notifying the parent's device of the generated advice" refers to means for notifying the parent's device, such as a smartphone or PC, of ​​the generated advice.

[1362] "Means for inputting dietary preferences and allergy information" refers to devices or software that allow parents to input information about their child's dietary preferences and allergies.

[1363] "Means for calculating meal contents and delivery time based on data analyzed by the server" refers to a method or device in which the server analyzes collected data and calculates the optimal meal contents and delivery time.

[1364] "Means for generating suggestions based on calculated meal contents and delivery times" refers to a system that generates specific suggestions to a user based on meal contents and delivery times calculated by the server.

[1365] "Means for notifying the user's device of the generated proposal" refers to means for notifying the user's device, such as a smartphone or PC, of ​​the generated proposal.

[1366] This invention is a system that suggests the optimal time for putting a child to bed based on data on the child's daily activities and physical condition, and also optimizes meal contents and delivery times. The components and operation procedures of the system are described in detail below.

[1367] Data collection

[1368] Parents use a dedicated application to enter data about their child's daily activities, food preferences, and allergies. This data includes, for example, mealtimes, playtimes, naptimes, and dinnertimes. Additionally, the system collects real-time health data (heart rate, body temperature, steps taken, etc.) from a wearable device worn by the child. This data is automatically sent to a smartphone or other device.

[1369] Sending data

[1370] All collected data is transmitted from the device via the internet to a server, where it is processed and analyzed using cloud services, particularly high-performance cloud computing platforms.

[1371] Data analysis and proposal generation

[1372] The server analyzes the collected data using a machine learning algorithm to calculate the optimal bedtime for each child. This machine learning model predicts the child's sleep patterns by studying past data. It also analyzes meal content and delivery times to generate optimal suggestions. Based on user input and real-time data from the wearable device, the server makes specific suggestions, such as "You're feeling well today, so a high-calorie meal would be appropriate for dinner."

[1373] Sending notifications

[1374] The analysis results and recommendations are sent from the server to the device and sent as push notifications to the parent's smartphone, etc. Parents can check these notifications and take specific actions to help their children maintain a healthy lifestyle.

[1375] Hardware and Software

[1376] The main hardware components of this system include a wearable device worn by the child, a smartphone or PC used by the parent, and a cloud server for analyzing the data. Software components include a mobile application for collecting and transmitting data, machine learning algorithms for analyzing the data, and cloud services. Specifically, Python, TensorFlow, and AWS (Amazon Web Services) are used.

[1377] Specific examples

[1378] For example, imagine a family with a three-year-old child. Parents use the app to input their child's breakfast time (8:00), playtime (10:00-11:00), naptime (13:00-14:00), dinnertime (18:00), etc. The wearable device collects data in real time, such as the child's calories burned, heart rate, and body temperature for the day. Based on this data, the server makes suggestions such as, "Today, you exercised a lot, so you need a nutritious meal for dinner to make up for the calories lost, and the optimal time to put your child to bed is 20:30." The parent also receives a link to order the suggested meal via a food delivery service.

[1379] Prompt Sentence Examples

[1380] You can get specific suggestions for your generative AI model by using prompts like the following:

[1381] "Please suggest optimal meals based on the generative AI model and user data.

[1382] User Data:

[1383] Children's meal times: Breakfast 8:00, Lunch 12:00, Dinner 18:00

[1384] Kids' Activities: School, Soccer Practice

[1385] Children's Allergies: Peanuts

[1386] Child's food preference: Vegetarian

[1387] Wearable device data:

[1388] Calories burned: 500

[1389] Body temperature: 36.5°C

[1390] Activity level: High

[1391] As described above, this system allows parents to receive optimal advice based on their child's daily activity and physical condition data, enabling them to effectively manage their health.

[1392] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1393] Step 1:

[1394] Through the application, parents input data about their child's daily activities, including mealtimes, playtimes, naptimes, dinnertimes, and their child's food preferences and allergies, which are then stored in a local database on the device.

[1395] Step 2:

[1396] The wearable device that the child wears collects real-time health data, including heart rate, body temperature, steps taken, calories burned, etc. The collected data is automatically sent to the device.

[1397] Step 3:

[1398] The device transmits all collected data to a server via the Internet, converting the collected daily activity data and physical condition data into an appropriate format and sending it to the server as a POST request using an API.

[1399] Step 4:

[1400] The server stores the received data in a database and analyzes it using a machine learning algorithm. Specifically, it learns a child's sleep and eating patterns based on past data and predicts the optimal time for putting them to bed and what to eat. The server's analysis process uses Python and TensorFlow, from data preprocessing to model training and prediction.

[1401] Step 5:

[1402] Based on the analysis results, the server generates the optimal time to put the child to bed and advice based on that, as well as suggestions based on the optimal meal content and delivery time. For example, specific advice and suggestions may be generated, such as "Since you exercised a lot today, a high-calorie meal would be appropriate for dinner" or "8:30 p.m. is the optimal time to put the child to bed today."

[1403] Step 6:

[1404] The server sends the generated advice and suggestions to the device and sends push notifications to the parent's smartphone, PC, or other device. The notifications include detailed instructions and suggestions for specific actions to take regarding the child's health. The device displays these notifications so that the parent can review the suggestions. Specifically, the device's push notification API is used to display the notifications.

[1405] Step 7:

[1406] The parent can then review the notification and take the action suggested by the server, such as ordering the suggested meal from a food delivery service and getting the child to bed based on the suggested bedtime. During this process, the parent can easily order the meal using the food delivery link within the app.

[1407] Through the above-mentioned series of processing steps, parents can receive optimal advice based on their child's daily activities and physical condition data, and can effectively manage their child's health.

[1408] 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.

[1409] MODE FOR CARRYING OUT THE INVENTION

[1410] This invention relates to a system that suggests the optimal time for putting a child to bed based on the child's daily activities and physical condition data, and also taking into consideration the emotional states of the user (parent) and the child. This system is realized by a server that analyzes input information from the parent, data collected from a wearable device, and emotional data generated by an emotion engine, and notifies the parent of the optimal time for putting the child to bed.

[1411] System Overview

[1412] 1. Data Collection

[1413] Parental input: Through a dedicated application, the "user" inputs their child's daily activity data, including mealtimes, playtime, naptime, study time, and screen time.

[1414] Wearable devices: Children wear wearable devices that collect real-time health data such as heart rate, body temperature, and number of steps taken. This data is automatically sent to a "terminal."

[1415] 2. Collecting Emotional Data

[1416] Parental Emotions: The device uses an emotion engine to recognize parental emotions through the application. Parental emotions are collected using text input and facial expression recognition technology.

[1417] Child's emotions: The Device will use facial and voice recognition technology through wearable devices or other appropriate sensors to recognize the child's emotions.

[1418] 3. Data transmission

[1419] All data is transmitted from the "terminal" to the "server" via the internet. The transmitted data includes parent input data, physical condition data from the wearable device, and emotional data of the parent and child.

[1420] 4. Data analysis and proposal generation

[1421] The "server" analyzes all the data it receives and runs machine learning algorithms to calculate the optimal bedtime for each child, taking into account past data and the current day's situation.

[1422] The server then uses the emotional data collected by the emotion engine to generate advice tailored to the parent's mental state and the child's mood. For example, if the parent is tired or the child is in a bad mood, it will adjust the timing and method of putting the child to bed.

[1423] 5. Sending notifications

[1424] The server generates specific advice based on the calculated optimal sleep time, which is then sent to the device and displayed to the user as a push notification.

[1425] Specific examples

[1426] For example, here is an example of a home with a 3-year-old child.

[1427] 1. Parent Input and Data Collection

[1428] "Users" use the app to input activity data such as their child's breakfast time (7:30), playtime (9:00-10:00), naptime (13:00-14:00), and dinnertime (18:00).

[1429] Children wear wearable devices that record their physical condition data, such as heart rate and body temperature, in real time.

[1430] "Users" input their emotional data into the app through facial recognition and text input.

[1431] Similarly, children's emotional data will be collected through wearable devices and other sensors.

[1432] 2. Data Transmission

[1433] All collected data is sent from the "terminal" to the "server."

[1434] 3. Data analysis and proposal generation

[1435] The server analyzes the data and calculates, for example, "Today, the child is very active and the parents are tired, so the optimal time to put the child to bed is 8:30 p.m." based on the child's activity level and physical condition that day, as well as the emotional state of the parent and child.

[1436] The "server" takes into account the parent's emotional state and generates specific advice such as "Play relaxing sounds at 7:30 p.m. and read picture books in bed at 8:30 p.m."

[1437] 4. Sending notifications

[1438] The "server" sends the generated advice to the "terminal," which then notifies the "user." The "user" checks this notification and adjusts the time for putting their child to bed based on the advice.

[1439] This system provides the optimal bedtime based on a child's daily activities and emotional state, reducing the burden on parents and supporting healthy sleep for children.

[1440] The processing flow will be explained below.

[1441] Step 1:

[1442] The "user" launches the smartphone app and enters the child's daily activity data for that day, including mealtimes, playtime, naptime, study time, and screen time.

[1443] Step 2:

[1444] The "user" puts the wearable device on the child.

[1445] Step 3:

[1446] The "terminal" collects real-time health data such as heart rate, body temperature, and number of steps from the wearable device.

[1447] Step 4:

[1448] To recognize the parent's emotions, the device uses an emotion engine to collect emotional data from the parent through the smartphone camera and text input, such as facial expression analysis and input of keywords that indicate emotions.

[1449] Step 5:

[1450] To recognize the child's emotions, the device uses an emotion engine to collect the child's emotional data through sensors in the wearable device and voice input.

[1451] Step 6:

[1452] The "terminal" compiles the collected daily activity data, physical condition data, and emotional data and sends it to the "server."

[1453] Step 7:

[1454] The "server" organizes the received data by date and time and prepares it for analysis.

[1455] Step 8:

[1456] The "server" references historical data sets and uses machine learning algorithms to analyze a child's sleep patterns, including daily activity, physical condition, and emotional data.

[1457] Step 9:

[1458] The "server" calculates the optimal bedtime for that day based on the latest data, for example suggesting an earlier bedtime if the child was more active, and takes into account the child's emotional state.

[1459] Step 10:

[1460] The server generates specific advice tailored to the emotions of the parent and child based on the emotional data collected by the emotion engine. For example, if the parent is tired, the advice may include "creating a relaxing environment."

[1461] Step 11:

[1462] The "server" transmits the generated advice to the "terminal."

[1463] Step 12:

[1464] The device displays advice to the user in the form of a notification message, including the time to put the child to sleep and specific suggested actions.

[1465] Step 13:

[1466] The "user" checks the notification and adjusts the child's bedtime based on the advice, for example, giving the child a bath at 7:30 p.m. and putting them to bed at 8:30 p.m.

[1467] Through these steps, the system will comprehensively analyze the child's daily activities, physical condition, and emotional state, and suggest the optimal bedtime, making it easier for parents to ensure their children get healthy sleep and reducing the burden of childcare.

[1468] Example 2

[1469] 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."

[1470] In modern society, it is important for parents to maintain healthy sleep patterns for their children. However, it is difficult to comprehensively understand a child's daily activities, physical condition, and emotional state to determine the optimal time for putting the child to bed. Furthermore, because a parent's own mental state also affects a child's sleep, there is a need for a method of putting the child to bed that takes this into consideration. Given this background, there is a need for a system that comprehensively analyzes a child's daily activity data, physical condition data, and emotional data to provide the optimal time for putting the child to bed and specific advice.

[1471] 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.

[1472] In this invention, the server includes means for having a parent input data on the child's daily activities, means for collecting the child's physical condition data from the wearable device, means for collecting emotional data of the parent and child, means for transmitting the collected data to a computer device, means for analyzing the data in the computer device and calculating an optimal time for putting the child to sleep, means for generating advice based on the calculated time for putting the child to sleep, and means for notifying the parent's information processing device of the generated advice. This allows the parent to know the optimal time for putting the child to sleep based on the child's daily activities and emotional state, and to receive specific advice.

[1473] "Means for parental input of data regarding the child's daily activities" refers to an input device or software function that allows a parent to input information about the child's daily activities, such as mealtimes, playtime, naptime, study time, and screen time.

[1474] "Means for collecting physical condition data of a child from a wearable device" refers to equipment and its functions for obtaining physical condition data such as heart rate, body temperature, and number of steps using a wearable device worn by a child.

[1475] "Means for collecting parent and child emotional data" refers to technologies and software functions for capturing the emotional states of parents and children using text input, facial expression recognition, and voice recognition technologies.

[1476] "Means for transmitting collected data to a computer device" refers to communication functions and protocols for transferring data collected from the parent's terminal or wearable device to a computer device such as a server via the Internet.

[1477] "Means for analyzing data using a computer device and calculating the optimal time for putting a child to sleep" refers to analytical technology and its software functions that allow a computer device such as a server to analyze collected data and use machine learning algorithms, etc. to derive the optimal time for putting a child to sleep.

[1478] The "means for generating advice based on the calculated sleep time" is an advice generation function for creating advice regarding specific sleep method and timing based on the analyzed optimal sleep time.

[1479] The "means for notifying the parent's information processing device of the generated advice" refers to a push notification function or communication means for notifying the parent's information processing device such as a smartphone or tablet of the generated advice.

[1480] MODE FOR CARRYING OUT THE INVENTION

[1481] The present invention relates to a system that suggests the optimal time for putting a child to bed based on data on the child's daily activities and physical condition, and also taking into consideration the emotional states of the parent and the child. This system is realized by a server that analyzes input information from the parent, data collected from a wearable device, and emotional data generated by an emotion engine, and notifies the parent of the optimal time for putting the child to bed.

[1482] The embodiments for carrying out the present invention are as follows.

[1483] Data collection

[1484] Parental input: Using a dedicated application, users input their child's daily activity data, including mealtimes, playtime, naptime, study time, screen time, etc. For example, breakfast time can be entered as "7:30" and playtime as "9:00-10:00."

[1485] Wearable devices: Children wear wearable devices (e.g., smartwatches, fitness trackers) that collect real-time physical data such as heart rate, body temperature, and number of steps. This data is automatically sent to the device.

[1486] Collecting Emotional Data

[1487] Parental Emotions: The device uses an emotion engine through the application to recognize parental emotions. Parental emotions are collected using text input and facial recognition technology (e.g., facial recognition API).

[1488] Child's emotions: The device uses facial and / or voice recognition technology in wearable devices or other appropriate sensors (e.g., voice recognition APIs) to recognize the child's emotions.

[1489] Sending data

[1490] All collected data is transmitted from the device to a server via the internet, including parental input data, physical condition data from the wearable device, and emotional data of the parent and child.

[1491] Data analysis and proposal generation

[1492] Data analysis: The server analyzes the received data and uses machine learning algorithms (e.g., TensorFlow) to calculate the optimal time for putting the child to sleep, taking into account past data and the current day's situation.

[1493] Suggestion generation: The server generates advice tailored to the parent's mental state and the child's mood based on the emotional data collected by the emotion engine. For example, if the parent is tired or the child is in a bad mood, the server provides advice on adjusting the timing and method of putting the child to bed.

[1494] Sending notifications

[1495] The server sends the generated advice to the device, which displays it to the user as a push notification. The user can then check the notification and adjust the bedtime based on the advice.

[1496] Specific examples

[1497] For example, here's an example from a home with a 3-year-old child:

[1498] 1. Parental input and data collection: Through the app, users input data such as their child's breakfast time (7:30), playtime (9:00-10:00), naptime (13:00-14:00), and dinnertime (18:00). Heart rate and body temperature data are recorded in real time from the wearable device worn by the child. Parents input their own emotional data into the app through facial recognition and text input. Emotional data about the child is also collected through the wearable device and other sensors.

[1499] 2. Data transmission: All collected data is transmitted from the device to the server.

[1500] 3. Data analysis and recommendation generation: The server analyzes the data and calculates, based on the child's activity level and physical condition that day, as well as the emotional state of the parent and child, that "Today, the child is very active and the parent is tired, so the optimal time to put the child to bed is 8:30 p.m." Taking into account the parent's emotional state, it generates specific advice such as "Play relaxing music at 7:30 p.m. and read a picture book in bed at 8:30 p.m."

[1501] 4. Sending notification: The server sends the generated advice to the device, and the device notifies the user as a push notification. The user checks the notification and adjusts their child's bedtime based on the advice.

[1502] Example prompts for generative AI models

[1503] "My child is 3 years old. Today's activity data is as follows: breakfast time is 7:30, playtime is 9:00-10:00, nap time is 13:00-14:00, and dinner time is 18:00. I'm a little tired, and my child is a little cranky. What's the best time to put him to bed? What's your advice?"

[1504] This system provides the optimal bedtime based on a child's daily activities and emotional state, reducing the burden on parents and supporting healthy sleep for children.

[1505] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1506] Program processing flow

[1507] Step 1:

[1508] A means for the user to input their child's daily activity data

[1509] Specific operations: The user opens a dedicated app on their smartphone and enters their child's daily activity data, such as breakfast time, playtime, nap time, and dinner time.

[1510] Example: "7:30" for breakfast time, "9:00-10:00" for playtime, etc.

[1511] Input: Child's daily activity data (e.g., breakfast time, playtime, etc.)

[1512] Output: The entered daily activity data is saved in the system.

[1513] Step 2:

[1514] A means for terminals to collect physical condition data from wearable devices

[1515] Specific operation: The device collects real-time physical condition data such as heart rate, body temperature, and number of steps taken via wearable devices (e.g., smart watches, fitness trackers) worn by children.

[1516] Example of how it works: After a child has completed an hour of exercise, their heart rate and temperature data are sent from the wearable device to the terminal.

[1517] Input: Real-time physical condition data received from a wearable device

[1518] Output: Collected physical condition data is saved to the device.

[1519] Step 3:

[1520] How the device collects parent and child emotional data

[1521] Specific operation: The device uses the application's emotion engine to collect the parent's emotions through text input and facial expression recognition, and the child's emotions through facial expression recognition and voice recognition technology.

[1522] How it works: Parents take pictures of their own faces using their smartphone cameras and use facial recognition technology to collect emotional data. Children's emotional data is analyzed from audio recordings of their play.

[1523] Input: Parent's emotional data (text input, facial expression recognition), Child's emotional data (facial expression recognition, voice recognition)

[1524] Output: Emotion data is recorded on the device.

[1525] Step 4:

[1526] A means for the device to send all collected data to a server

[1527] Specific operation: The terminal packetizes the collected parental input data, physical state data from the wearable device, and emotion data, and sends them to a server via the Internet.

[1528] Example of operation: Various data is collected into packets at regular intervals and transferred to the server.

[1529] Input: Parent input data, physical state data, emotion data

[1530] Output: The transmitted data arrives at the server.

[1531] Step 5:

[1532] The server analyzes the data and calculates the optimal time to put the child to sleep.

[1533] How it works: The server analyzes the received data and uses machine learning algorithms (e.g., TensorFlow) to calculate the optimal time for putting the child to bed based on the child's activity level, physical condition, and emotional state that day. The analysis also takes into account past data.

[1534] Example of operation: When the server receives the data, it first filters the emotional and physical condition data and compares it with the data from the previous day to determine the optimal time to put the child to sleep.

[1535] Input: All data sent to the server

[1536] Output: The optimal bedtime (e.g. 8:30pm) is calculated.

[1537] Step 6:

[1538] A means for the server to generate advice based on the calculation results and notify the device

[1539] Specific operation: Based on the calculated optimal time for putting the child to sleep, the server considers the emotional state of the parent and child and generates specific advice, such as "Play relaxing music at 7:30 PM and read a picture book in bed at 8:30 PM." The generated advice is sent to the device.

[1540] Example of operation: The advice generated by the server is packetized in text message format and notified to the terminal.

[1541] Input: Optimal bedtime, emotional state of parent and child

[1542] Output: Specific advice generated

[1543] Step 7:

[1544] A means for the device to notify the user of advice

[1545] Specific operation: The device displays the advice received from the server to the user as a push notification. The user checks the notification and adjusts their child's bedtime based on the advice.

[1546] Example of how it works: The device displays a push notification, which the user sees on their smartphone screen.

[1547] Input: Advice received from the server

[1548] Output: Advice given to the user

[1549] (Application example 2)

[1550] 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."

[1551] In the past, it was difficult for parents to determine the appropriate bedtime based on their child's daily activity and physical condition data. It was especially difficult to take into account the child's activity data and emotional state when visiting a physical store. It was also extremely difficult to grasp the child's emotional state in real time and make flexible suggestions based on that. To solve this problem, a system that comprehensively analyzes parent-child activity and emotional data and provides optimal advice is needed.

[1552] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for having a parent input data on the child's daily activities, means for collecting the child's physical condition data from a wearable device, means for transmitting the collected data to the server, means for analyzing the data in the server and calculating optimal rest times and home times, means for generating advice based on the calculated rest times, means for notifying the parent's terminal of the generated advice, means for inputting activity data of the parent and child in a physical store visited via a smartphone, and means for collecting emotional data of the parent and child using facial expression recognition and voice recognition technology. This makes it possible to comprehensively analyze the activity data and emotional data of the parent and child in the physical store and provide optimal advice in real time.

[1553] The "means for allowing parents to input data on their children's daily activities" provides an interface for parents to input information on their children's daily activities.

[1554] "Means for collecting children's health data from wearable devices" refers to a system that collects health data such as heart rate, body temperature, and number of steps in real time from wearable devices worn by children.

[1555] "Means for transmitting collected data to a server" refers to a communication means for transmitting all data collected from the parent and the wearable device to a server via the Internet.

[1556] "Means for analyzing data on the server and calculating optimal break times and times to go home" refers to a function that uses a machine learning algorithm to analyze the data received by the server and calculates optimal break times and times to go home based on the results.

[1557] The "means for generating advice based on the calculated rest time" is a mechanism for generating specific advice based on the optimal rest time calculated by the server.

[1558] The "means for notifying the parent's terminal of the generated advice" refers to a communication means for notifying the parent's terminal, such as a smartphone or tablet, of the advice generated by the server.

[1559] "Means for inputting activity data within physical stores visited by parents and children via smartphone" refers to an interface that allows parents to input information about their children's activities within physical stores using a smartphone app.

[1560] "Means for collecting parent-child emotional data using facial expression and voice recognition technology" refers to technology that uses smartphones or wearable devices to recognize the facial expressions and voices of parents and children and collect that emotional data.

[1561] This invention relates to a system that suggests optimal break times and times to go home in a physical store visited by parents and children, taking into consideration the child's daily activities and physical condition data, as well as the emotional states of the parents and children.

[1562] System Configuration

[1563] Hardware

[1564] Smartphone: Used by parents to input data about their children's daily activities and collect emotional data.

[1565] Wearable device: Worn by children, it collects real-time health data such as heart rate, body temperature, and number of steps taken.

[1566] Server: Analyzes data collected via the Internet and calculates optimal break times and times to go home.

[1567] software

[1568] Application development frameworks (e.g., React Native, Swift, Kotlin): Used to develop smartphone apps.

[1569] Machine learning libraries (e.g. TensorFlow, PyTorch): Used to perform data analysis on the server side.

[1570] Emotion recognition engine (e.g., Microsoft Azure Cognitive Services, IBM Watson): Analyzes parent and child emotion data.

[1571] Operation overview

[1572] Data collection

[1573] Parents enter their child's daily activity data on their smartphone. This data includes playtime, study time, mealtimes, etc. Additionally, the child's wearable device sends real-time health data, such as heart rate, body temperature, and number of steps, to the smartphone.

[1574] Collecting Emotional Data

[1575] The smartphone camera is used to recognize the facial expressions of parents and children, and voice recognition technology is used to collect emotional data, which is then analyzed by an emotion engine to understand the emotional state of parents and children.

[1576] Data transmission and analysis

[1577] All data is sent over the internet to a server, where machine learning algorithms are used to analyze the data and calculate optimal break times and times to go home.

[1578] Advice Generation and Notifications

[1579] Based on the results calculated on the server, specific advice is generated and sent to the parent's smartphone as a push notification. The advice also includes suggestions for making life more convenient for both parent and child.

[1580] Specific examples

[1581] For example, parents can input their child's activity information in a physical store into a smartphone app: for example, playtime (10:00-11:00), mealtime (12:00), etc. Also, if the child is wearing a wearable device, their physical condition data, such as heart rate, body temperature, and number of steps, is collected in real time on the smartphone.

[1582] It then uses the smartphone camera to recognize the facial expressions of the parent and child, and analyzes their voice to collect emotional data, and an emotion engine analyzes the emotional state of the parent and child.

[1583] All this data is sent to a server via the internet, which uses machine learning algorithms to calculate optimal break times and times to go home. For example, if the child is very active and the parent is tired, the server will generate advice such as, "Today, it would be good to take a break at 1 p.m., so that the parent can relax together."

[1584] The generated advice is sent to the parent's smartphone via push notification, and the parent can check this notification and adjust appropriate break times, etc.

[1585] Prompt Sentence Examples

[1586] "How long did your child play this morning?"

[1587] "The child's current temperature and heart rate are as follows."

[1588] "Parents, you seem tired. It would be good to take a break today."

[1589] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1590] Step 1: Data entry

[1591] The user (parent) uses a smartphone app to input their child's daily activity data, including mealtimes, playtime, study time, screen time, etc. The input data is saved in the app and sent to the next step.

[1592] Step 2: Collecting health data

[1593] The terminal (smartphone) collects the child's physical condition data in real time from the wearable device. This data includes heart rate, body temperature, number of steps, etc. The collected data is stored on the smartphone in real time and transmitted to the next step.

[1594] Step 3: Collecting emotion data

[1595] The device (smartphone) uses the smartphone's camera to recognize the facial expressions of the parent and child, and uses voice recognition technology to collect emotional data, which is then stored on the smartphone and sent to the next step.

[1596] Step 4: Send data

[1597] The device (smartphone) sends all collected data (activity data, physical condition data, emotional data) to a server via the Internet, where it is stored and prepared for analysis.

[1598] Step 5: Data analysis

[1599] The server receives all the data and begins analyzing it. Here, it uses machine learning algorithms to analyze the data and calculate the optimal break time and time for the child to return home. In particular, it processes and calculates the data based on past data and the situation on the day.

[1600] Step 6: Advice Generation

[1601] The server generates specific advice based on the results of the data analysis. Using a generative AI model, it creates advice tailored to the parent's mental state and the child's mood. For example, it might generate advice such as, "It's a good idea to take a break at 1 p.m. today. Let's relax together as parents."

[1602] Step 7: Send notification

[1603] The server sends the generated advice to the parent's device (smartphone) as a push notification. The device receives the notification and displays it so that the user can check it. The user can adjust their child's break time and activity schedule based on this notification.

[1604] 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.

[1605] 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.

[1606] 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.

[1607] 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.

[1608] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1609] 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.

[1610] 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).

[1611] 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.

[1612] 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."

[1613] 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.

[1614] 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).

[1615] 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.

[1616] 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.

[1617] 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.

[1618] 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.

[1619] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1620] 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.

[1621] 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.

[1622] 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.

[1623] 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.

[1624] 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.

[1625] The following is further disclosed regarding the above embodiment.

[1626] (Claim 1)

[1627] a means for parental input of data regarding the child's daily activities;

[1628] A means for collecting child health data from a wearable device;

[1629] means for transmitting the collected data to a server;

[1630] A method to analyze data on the server and calculate the optimal time to put the child to sleep.

[1631] means for generating advice based on the calculated sleep time;

[1632] a means for notifying the parent terminal of the generated advice;

[1633] A system including:

[1634] (Claim 2)

[1635] 10. The system of claim 1, further comprising means for using a machine learning algorithm to analyze past data to learn the child's sleep patterns.

[1636] (Claim 3)

[1637] 10. The system of claim 1, further comprising means for collecting data in real time and dynamically generating recommendations based thereon.

[1638] "Example 1"

[1639] (Claim 1)

[1640] a means for parental input of data regarding the child's daily activities;

[1641] A means for collecting child health data from a wearable device;

[1642] means for transmitting the collected data to an information processing device;

[1643] A means for analyzing data with an information processing device and calculating an optimal time for putting the child to sleep;

[1644] means for generating advice based on the calculated sleep time;

[1645] a means for notifying the parent terminal of the generated advice;

[1646] Measures to ensure security of data transmission;

[1647] A system including:

[1648] (Claim 2)

[1649] 10. The system of claim 1, further comprising means for using a machine learning algorithm to analyze past data to learn the child's sleep patterns.

[1650] (Claim 3)

[1651] 10. The system of claim 1, further comprising means for collecting data in real time and dynamically generating recommendations based thereon.

[1652] "Application Example 1"

[1653] (Claim 1)

[1654] a means for parental input of data regarding the child's daily activities;

[1655] A means for collecting child health data from a wearable device;

[1656] means for transmitting the collected data to a server;

[1657] A method to analyze data on the server and calculate the optimal time to put the child to sleep.

[1658] means for generating advice based on the calculated sleep time;

[1659] a means for notifying the parent terminal of the generated advice;

[1660] A means for inputting dietary preferences and allergy information;

[1661] A means for calculating meal contents and delivery time based on the data analyzed by the server;

[1662] means for generating recommendations based on the calculated meal contents and delivery times;

[1663] means for notifying a user terminal of the generated proposal;

[1664] A system including:

[1665] (Claim 2)

[1666] 10. The system of claim 1, further comprising means for using machine learning algorithms to analyze past data to learn the child's sleeping and eating patterns.

[1667] (Claim 3)

[1668] 10. The system of claim 1, further comprising means for collecting data in real time and dynamically generating recommendations based thereon.

[1669] "Example 2: Combining Emotion Engines"

[1670] (Claim 1)

[1671] a means for parental input of data regarding the child's daily activities;

[1672] a means for collecting physical condition data of the child from the wearable device;

[1673] a means for collecting parent and child emotional data;

[1674] means for transmitting the collected data to a computer device;

[1675] A means for analyzing the data with a computer device and calculating the optimal sleep time;

[1676] means for generating advice based on the calculated sleep time;

[1677] means for notifying the parent's information processing device of the generated advice;

[1678] A system including:

[1679] (Claim 2)

[1680] 10. The system of claim 1, further comprising means for using a machine learning algorithm to analyze past data to learn the child's sleep patterns.

[1681] (Claim 3)

[1682] 10. The system of claim 1, further comprising means for collecting data in real time and dynamically generating recommendations based thereon.

[1683] "Application example 2 when combining emotion engines"

[1684] (Claim 1)

[1685] a means for parental input of data regarding the child's daily activities;

[1686] A means for collecting child health data from a wearable device;

[1687] means for transmitting the collected data to a server;

[1688] A method to analyze data on the server and calculate the optimal time to put the child to sleep.

[1689] means for generating advice based on the calculated sleep time;

[1690] a means for notifying the parent terminal of the generated advice;

[1691] A means to input activity data in physical stores visited by parents and children via smartphone, and

[1692] means for collecting parent-child emotion data using facial expression and voice recognition technology;

[1693] A system including:

[1694] (Claim 2)

[1695] 10. The system of claim 1, further comprising means for using a machine learning algorithm to analyze past data to learn the child's sleep patterns.

[1696] (Claim 3)

[1697] 10. The system of claim 1, further comprising means for collecting data in real time and dynamically generating recommendations based thereon. [Explanation of symbols]

[1698] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for parental input of data regarding the child's daily activities; A means for collecting child health data from a wearable device; means for transmitting the collected data to a server; A method to analyze data on the server and calculate the optimal time to put the child to sleep. means for generating advice based on the calculated sleep time; a means for notifying the parent terminal of the generated advice; A system including:

2. 10. The system of claim 1, further comprising means for using a machine learning algorithm to analyze past data to learn the child's sleep patterns.

3. The system of claim 1 further comprising means for collecting data in real time and dynamically generating recommendations based thereon.

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