system

Abstract

Provide a system. A method for obtaining child health information; A means for analyzing the acquired health information; means for generating predictions and advice based on the analysis results; a means for communicating the generated predictions and 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] Traditional health care systems are often designed for adults, making it difficult to predict and provide advice on health issues specific to children and epidemics. Furthermore, for parents and teachers to gain a detailed understanding of their children's health, regular checkups at medical institutions are required, which is time-consuming and laborious. This makes it difficult to take early preventive measures, potentially increasing children's health risks. [Means for solving the problem]

[0005] The present invention provides a system for acquiring health information of children and analyzing the acquired data to predict and provide advice on epidemics and diseases tailored to the child's development. Specifically, the system includes the following means:

[0006] How to obtain your child's health information

[0007] A means of analyzing acquired health information

[0008] A means of generating predictions and advice based on analytical results

[0009] A means of communicating the generated predictions and advice

[0010] This will enable parents and teachers to easily understand their children's health status and take necessary measures promptly. Furthermore, through regular data collection and analysis of epidemic information, more accurate predictions and advice can be provided, reducing health risks for children.

[0011] "Children's health information" refers to detailed data such as a child's height, weight, body temperature, symptoms, diet, and amount of exercise.

[0012] "Means of acquisition" refers to interfaces or devices used to collect health information and input or transmit it into the system.

[0013] "Means of analysis" refers to the group of processes within the system that perform statistical analysis and machine learning based on the acquired health information to generate assessments and predictions of health status.

[0014] "Means for generating predictions and advice" refers to the algorithms and logic used to automatically generate a child's future health risks and optimal course of action based on the analysis results.

[0015] "Means for notification" refers to communication technologies and devices for transmitting the generated predictions and advice to parent and teacher devices. [Brief explanation of the drawings]

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

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

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

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

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

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

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

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

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

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

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0037] The processing flow of the program that constitutes the AI ​​system for predicting children's health in order to put the present invention into practice will be described below.

[0038] User data entry

[0039] Users (parents or teachers) input their child's latest health information through a smartphone app or PC interface. This includes various data such as height, weight, daily temperature, symptoms (cough, runny nose, fever, etc.), diet, and amount of exercise. The device temporarily stores this information and formats it appropriately.

[0040] Sending data

[0041] The device then transmits the formatted health data to a server using encrypted protocols such as HTTPS to ensure security.

[0042] Receiving and storing data

[0043] The server stores the received data in a database, where it is simultaneously checked for errors and corrected for inconsistencies. For example, any missing data or formatting errors are corrected appropriately.

[0044] Collecting trend information

[0045] The server collects disease epidemic information by region and time period from public health agencies and other data sources, and this epidemic information is combined with children's health data for analysis.

[0046] Data Analysis and Prediction

[0047] The server uses machine learning algorithms to analyze children's health data and epidemic information. For example, it predicts future health risks based on past body temperature data and current symptoms. This prediction is made using time series analysis and pattern recognition techniques.

[0048] Generating personalized advice

[0049] The server generates specific, individualized advice based on the analysis results, such as "You are at high risk of developing influenza, so please wash your hands thoroughly, wear a mask, and consider getting vaccinated."

[0050] Notification Delivery

[0051] The generated predictions and advice are sent from the server to home devices (parents' smartphones or PCs) and school devices via email, push notifications, in-app notifications, and other means.

[0052] User Action

[0053] Users receive notifications and can take specific actions based on the advice, such as scheduling a vaccination appointment or making sure their child has a mask. Users can also provide feedback on the advice provided, helping to improve the system's accuracy.

[0054] Continuous data collection

[0055] The device periodically prompts the user to enter their child's latest health information and continuously sends it to the server, allowing the server to provide predictions and advice based on the most up-to-date information.

[0056] Specific examples

[0057] For example, a user (parent) might enter into a smartphone app, "My 5-year-old child has had a slight fever of 37.5°C each morning for the past three days, and has symptoms of a cough and runny nose." This data is sent to a server, which analyzes it based on local and epidemic information, and predicts that "Influenza is currently prevalent in the area, so there is a high risk of the child developing the disease within the next week." The server then generates advice such as "wash your hands thoroughly, wear a mask, and get vaccinated," and sends it to the parent's smartphone. The parent can receive the notification and immediately consult a medical institution to reduce their child's health risks.

[0058] In this way, the present invention effectively manages children's health and provides a reassuring environment for parents and teachers.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] Users (parents or teachers) enter their child's health information into a smartphone app or computer, including height, weight, body temperature, symptoms (e.g., cough, runny nose, fever), dietary habits, and exercise.

[0062] Step 2:

[0063] The terminal temporarily stores the entered data and formats it appropriately, and also performs error checking to ensure the data is of the correct type and range.

[0064] Step 3:

[0065] The terminal transmits the formatted data to the server using a secure communication protocol (e.g., HTTPS).

[0066] Step 4:

[0067] The server stores the received data in a database, checks for inconsistencies and missing values, and completes or corrects the data as necessary.

[0068] Step 5:

[0069] The server collects regional and seasonal epidemic information from public health agencies and other data sources. This information is used for analysis.

[0070] Step 6:

[0071] The server uses the child's health information stored in the database and the collected epidemic information to run machine learning algorithms, including time series analysis and pattern recognition techniques.

[0072] Step 7:

[0073] Based on the analysis results, the server predicts the child's future health risks, such as the probability of developing influenza and calculating the necessary preventive measures.

[0074] Step 8:

[0075] The server generates specific advice based on the prediction results, such as "You are at high risk of influenza, so we recommend washing your hands, wearing a mask, and getting vaccinated."

[0076] Step 9:

[0077] The server then sends the generated predictions and advice to the parent or teacher's device via email, push notification, in-app notification, or other methods.

[0078] Step 10:

[0079] The user (parent or teacher) checks the notification received on the device and takes necessary action based on the advice, such as making an appointment for a vaccination or making sure that the child has a mask.

[0080] Step 11:

[0081] Users can provide feedback on the advice provided via the app or their device, which allows the system to continually improve.

[0082] Step 12:

[0083] The device periodically prompts the user to enter their latest health information and continuously transmits the acquired data to the server, allowing the server to provide predictions and advice based on the most up-to-date information.

[0084] These are the specific processing steps of the child health prediction AI system.

[0085] Example 1

[0086] 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."

[0087] Conventional child health management systems have limited methods for collecting and analyzing health information, and lack the ability to accurately predict children's health conditions and provide appropriate advice. Furthermore, because they do not collect epidemic information, they are unable to make predictions that take into account health risks by region or time of year. Furthermore, there are insufficient measures to format the collected health information or ensure its security, resulting in problems with the accuracy and safety of the data.

[0088] 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.

[0089] In this invention, the server includes means for acquiring the child's health information, means for formatting the information into an appropriate format, means for transmitting the formatted health information to the server using a secure protocol, means for error checking and correcting inconsistencies in the received health information and storing it in a database, means for collecting disease epidemic information by region and time from external data sources, means for analyzing the child's health information and epidemic information using a machine learning algorithm and predicting health risks, means for generating individual advice using natural language generation technology based on the analysis results, and means for notifying the generated predictions and advice. This enables efficient collection and analysis of a variety of health information, and enables highly accurate predictions of health risks that take epidemic information into account and the provision of specific advice.

[0090] "Children's health information" refers to data such as height, weight, daily body temperature, symptoms (cough, runny nose, fever, etc.), diet, and amount of exercise.

[0091] "Means of acquisition" refers to the means by which users input and collect health information using a smartphone app or computer interface.

[0092] The "formatting means" is a means for converting the input health information into a unified format and for performing error checks and corrections as appropriate.

[0093] "Secure protocol" refers to encrypted communication methods such as HTTPS, and is a technology that ensures security when sending and receiving data.

[0094] "Error checking and inconsistency correction" is the process of verifying the accuracy of received data and correcting missing data or formatting errors.

[0095] "Database" refers to a system for storing and managing collected health and epidemic information. Specifically, it includes relational databases such as MySQL (registered trademark) and PostgreSQL.

[0096] "External data sources" refers to public health agencies and other reliable data sources that provide disease outbreak information by region and time.

[0097] "Machine learning algorithms" are statistical methods or artificial intelligence techniques used to analyze collected data and predict health risks. Examples include random forests and long short-term memory (LSTM).

[0098] "Natural language generation technology" refers to technology that generates advice in natural language that is easy for humans to understand based on the analysis results.

[0099] "Means of notification" refers to the method for communicating the generated predictions and advice to the user, and includes email, push notification, in-app notification, etc.

[0100] To implement the present invention, the following system configuration and operating procedures are required. First, a system is constructed for the purpose of collecting and analyzing children's health information. This system consists of three main elements: a user, a terminal, and a server. The detailed operation of each element and the necessary hardware and software are explained below.

[0101] User data entry

[0102] Users (parents or educators) enter their child's latest health information through a smartphone app or PC interface, including their height, weight, daily temperature, symptoms (cough, runny nose, fever, etc.), diet, and exercise.

[0103] Hardware: Consumer devices such as smartphones, tablets, and PCs.

[0104] Software: React Native apps, HTML5-based web apps, etc.

[0105] Sending data

[0106] The entered health information is temporarily stored on the device and formatted appropriately. For example, the device standardizes date formats and checks the format of numeric data. The device then converts the data into JSON format and sends it to the server using the HTTPS protocol.

[0107] Hardware: Same as above.

[0108] Software: JSON encoding, HTTPS communication, REST API, etc.

[0109] Receiving and storing data

[0110] The server captures the received data and performs error checking and inconsistency correction, specifically checking for missing data or formatting errors, before storing the data in a database.

[0111] Hardware: Servers, storage systems.

[0112] Software: Relational databases such as MySQL and PostgreSQL, error checking algorithms.

[0113] Collecting trend information

[0114] The server collects disease outbreak information by region and time period from public health agencies and other data sources (e.g., WHO, CDC). This information is stored in a database for later analysis.

[0115] Hardware: Server

[0116] Software: External API integration, data collection module.

[0117] Data Analysis and Prediction

[0118] The server uses the user's health information and trend information to apply machine learning algorithms to predict health risks, using time series analysis and pattern recognition.

[0119] Hardware: High performance computing server.

[0120] Software: Machine learning algorithms such as Random Forest, LSTM, etc.

[0121] Generating personalized advice

[0122] Based on the analysis results, specific advice is generated using natural language generation (NLG) technology, such as "You are at high risk of developing influenza, so please be sure to wash your hands and wear a mask."

[0123] Hardware: Server

[0124] Software: NLG toolkit, custom scripts.

[0125] Notification Delivery

[0126] The generated predictions and advice are sent from the server to home and school devices via email, push notifications, in-app notifications, and other methods.

[0127] Hardware: Servers, devices (smartphones, PCs, etc.).

[0128] Software: Push notification server, mail server.

[0129] User Action

[0130] Users receive notifications and take specific actions based on the advice, such as consulting a medical institution or making an appointment for a vaccination. Users can also enter feedback into the system to help improve its accuracy.

[0131] Hardware: Smartphone, PC

[0132] Software: Feedback forms, data collection systems

[0133] Continuous data collection

[0134] The device periodically prompts the user to enter their child's latest health information and continuously sends that data to the server, allowing the server to provide up-to-date predictions and advice.

[0135] Hardware: Smartphone, PC

[0136] Software: notification systems, data collection applications

[0137] Examples of concrete examples and prompts

[0138] For example, a user (parent) might enter into a smartphone app, "My 5-year-old child has had a slight fever of 37.5°C each morning for the past three days, and has symptoms of a cough and runny nose." This data is sent to a server, which analyzes it based on local and epidemic information, and predicts that "Influenza is currently prevalent in the area, so there is a high risk of the child developing the disease within the next week." The server then generates advice such as "Please wash your hands thoroughly, wear a mask, and consider getting vaccinated," and sends it to the parent's smartphone. The parent can receive the notification and immediately consult a medical institution to reduce their child's health risks.

[0139] Example prompt sentence:

[0140] "My 5-year-old child has had a temperature of 37.5°C every morning for the past three days, and is experiencing symptoms of a cough and runny nose. Please predict the future health risks associated with this condition and advise me on appropriate measures to take."

[0141] This system will enable the efficient collection and analysis of a wide range of health information, making it possible to accurately predict health risks taking into account current trends and provide specific advice.

[0142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0143] Step 1: User Data Entry

[0144] Input: Using a smartphone app or PC interface, users input their child's health information, including their height, weight, daily temperature, symptoms (e.g., cough, runny nose, fever), diet, and exercise.

[0145] Specific behavior: The user enters information into a form on the app, for example, entering specific symptoms such as "I have had a slight fever of 37.5°C for three days."

[0146] Output: The terminal temporarily stores the input health information in its memory.

[0147] Step 2: Shaping the data

[0148] Input: Health information entered by the user.

[0149] Specific operations: The device formats the temporarily stored data, for example, standardizing date formats, checking the format of numeric data, and correcting inaccurate data.

[0150] Output: Formatted health information data.

[0151] Step 3: Encode and encrypt the data

[0152] Input: Formatted health information data.

[0153] What it does: The device converts the formatted data into JSON format and encrypts it using the HTTPS protocol, which allows for secure data transmission.

[0154] Output: Health information data encoded in JSON format and encrypted.

[0155] Step 4: Sending data

[0156] Input: Encrypted health information data.

[0157] How it works: The device sends encrypted data to the server using a communication protocol implemented through a REST API.

[0158] Output: The data is sent to the server.

[0159] Step 5: Receiving data and checking for errors

[0160] Input: Encrypted health information data.

[0161] Specific behavior: The server captures incoming data via a REST API endpoint, performs error checking to ensure the data is in the correct format, and corrects any inconsistencies.

[0162] Output: Health information data that has been error checked and corrected.

[0163] Step 6: Save your data

[0164] Input: Health information data that has been error checked and corrected.

[0165] Specific operation: The server stores the data in a database (e.g., MySQL or PostgreSQL).

[0166] Output: Health information data stored in a database.

[0167] Step 7: Gathering trend information

[0168] Input: External data sources providing epidemic information (e.g., public health agency APIs).

[0169] Specific operation: The server accesses external data sources and collects disease epidemic information by region and time period.

[0170] Output: A dataset containing epidemic information.

[0171] Step 8: Save Trend Information

[0172] Input: A dataset containing epidemic information.

[0173] Specific operation: The server stores the collected trend information in a database.

[0174] Output: Trend information data stored in a database.

[0175] Step 9: Data analysis and prediction

[0176] Input: Health information data, epidemic information data.

[0177] Specific operation: The server uses machine learning algorithms (e.g., random forest and LSTM) to analyze children's health and epidemic information, thereby predicting future health risks.

[0178] Output: Health risk prediction results.

[0179] Step 10: Generate personalized advice

[0180] Input: Health risk prediction results.

[0181] Specific operation: Based on the analysis results, the server uses natural language generation technology (NLG) to generate personalized advice, such as "wash your hands thoroughly, wear a mask, and consider getting vaccinated."

[0182] Output: The generated personalized advice.

[0183] Step 11: Creating and Delivering Notifications

[0184] Input: Generated personalized advice.

[0185] Specific operation: The server incorporates the generated advice into a notification message and delivers it to the home device via a push notification server or mail server.

[0186] Output: Notification message delivered to the user's device.

[0187] Step 12: User Action

[0188] Input: Notification message delivered to the user's device.

[0189] Specific actions: The user checks the notification and takes specific actions based on the advice, such as consulting a medical institution or making an appointment for a vaccination, and also enters feedback on the advice.

[0190] Output: Feedback is sent to the system.

[0191] Step 13: Facilitate ongoing data collection

[0192] Enter: your system's notification settings.

[0193] Specific operation: The device periodically sends notifications to the user prompting them to enter their child's latest health information. By entering new data, the device keeps the information up to date.

[0194] Output: The latest health information data is added to the system.

[0195] (Application example 1)

[0196] 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."

[0197] In modern society, managing children's health is extremely important. However, it is not easy for parents and teachers to manually collect daily health information and contact medical institutions at the appropriate time. It is particularly difficult to check children's health status in real time and take necessary measures when shopping in a physical store or going out. For this reason, there is a need for a system that can efficiently collect and analyze children's health information and provide appropriate advice.

[0198] 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.

[0199] In this invention, the server includes a means for acquiring child health information, a means for analyzing the acquired health information, and a means for generating predictions and advice based on the analysis results. This enables a means for entering a customer's health information using a smart device and sending it to the server, a means for receiving analysis results from the server and displaying them on the smart device, a means for periodically collecting data on the child's health information and sending it to the server, and a means for collecting information on current disease trends and using it for analysis. This allows parents shopping in a physical store to easily check their child's health status and quickly take appropriate measures.

[0200] "Children's health information" refers to data related to a child's health condition, such as height, weight, body temperature, symptoms, diet, and amount of exercise.

[0201] "Means of acquisition" refers to devices or software that allow users to input and record health information.

[0202] The "means of analysis" refers to algorithms or software that predict health risks and generate appropriate advice based on the acquired health information.

[0203] The "means for generating predictions and advice" is software that automatically suggests future health risks and appropriate measures based on the analysis results.

[0204] "Means of notification" refers to technologies such as email, push notifications, and in-app notifications used to notify users of generated predictions and advice.

[0205] "Smart devices" are mobile terminals with advanced computing power and communication functions, such as smartphones, smart glasses, and head-mounted displays.

[0206] A "server" refers to a computer system that stores and analyzes data over a network.

[0207] The "means for inputting health information of customers" refers to a user interface that allows customers to input health information via a smart device.

[0208] "Means for transmitting to the server" refers to the communication protocol and functions for securely transmitting the acquired health information to the server.

[0209] "Means for receiving and displaying analysis results from a server" refers to technology for receiving analysis results from a server and displaying them on the display of a smart device.

[0210] "Means for periodically collecting data and transmitting it to a server" refers to a system or program that periodically collects children's health data and transmits it to a server.

[0211] "Means for collecting disease epidemic information and using it for analysis" refers to software and databases for collecting disease epidemic information by region and time period and using it for analysis.

[0212] To practice the invention, we describe an implementation of a child health prediction system that can be used in a brick-and-mortar environment.

[0213] Hardware and software used

[0214] Hardware: Smart glasses (e.g., regular smart glasses)

[0215] Software: Python 3.x, HTTP / HTTPS API

[0216] Server: Data analysis server on the cloud

[0217] Communication protocol: HTTPS

[0218] Program processing flow

[0219] 1. Data entry by the user (parent or store staff)

[0220] The user uses the smart glasses' interface to input their child's latest health information, including height, weight, daily temperature, symptoms (e.g., cough, runny nose, fever), diet, and exercise. This information is temporarily stored in the smart glasses' memory and formatted accordingly.

[0221] 2. Data transmission

[0222] The smart glasses device transmits the processed health data to a server, using encrypted protocols such as HTTPS to ensure security.

[0223] 3. Receipt and storage of data

[0224] The server stores the received data in a database, where it is simultaneously checked for errors and corrected for inconsistencies.

[0225] 4. Collecting trend information

[0226] The server collects disease epidemic information by region and time period from public health agencies and other data sources, and this epidemic information is combined with children's health data for analysis.

[0227] 5. Data analysis and prediction

[0228] The server uses machine learning algorithms to analyze children's health data and epidemic information. For example, it predicts future health risks based on past body temperature data and current symptoms. This prediction is made using time series analysis and pattern recognition techniques.

[0229] 6. Generating personalized advice

[0230] The server generates specific, individualized advice based on the analysis results, such as "You are at high risk of developing influenza, so please wash your hands thoroughly, wear a mask, and consider getting vaccinated."

[0231] 7. Delivery of Notices

[0232] The generated predictions and advice are communicated from the server to the smart glasses display using suitable push notification technology.

[0233] Specific examples

[0234] For example, if a parent inputs into their smart glasses, "My temperature has been 37.5°C for the past three days, and I have symptoms of a cough and runny nose," that data is sent from the smart glasses to a server. The server analyzes local epidemic information and past data and predicts that "Influenza is currently spreading in the area, so there is a high risk of developing the disease within the next week." The server then generates advice such as "wash your hands thoroughly, wear a mask, and get vaccinated," and sends it to the parent's smart glasses. The parent can receive this notification and take immediate action.

[0235] Prompt Sentence Examples

[0236] Your temperature has been 37.5°C for the past three days, and you have a cough and runny nose. There is currently an influenza outbreak in your area, so you are at high risk of developing influenza. We recommend that you wash your hands thoroughly, wear a mask, and get vaccinated.

[0237] As described above, the present invention effectively manages children's health and provides an environment where parents and store staff can respond quickly through use in physical stores.

[0238] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0239] Step 1:

[0240] The user uses the smart glasses interface to input their child's latest health information.

[0241] Input: Height, weight, temperature, symptoms (cough, runny nose, fever, etc.), diet, amount of exercise

[0242] Data processing: The input data is converted into an appropriate format and temporarily stored in the smart glasses' memory.

[0243] Output: Formatted health data

[0244] Step 2:

[0245] The device transmits the formatted health data to the server.

[0246] Input: Formatted health data

[0247] Data Transmission: Transmit data using encrypted protocols such as HTTPS.

[0248] Output: Data sent to the server

[0249] Step 3:

[0250] The server stores the received data in a database, checks for errors, and corrects inconsistencies.

[0251] Input: Submitted health data

[0252] Data storage and processing: storing data in a database and correcting any missing or formatting errors.

[0253] Output: Accurate health data stored

[0254] Step 4:

[0255] The server collects disease outbreak information by region and time period from public health agencies and other data sources.

[0256] Input: Trend information by region and time

[0257] Data collection: Obtaining epidemic information from public health agencies and other data sources.

[0258] Output: Disease epidemic information by region and time period

[0259] Step 5:

[0260] The server uses machine learning algorithms to analyze children's health data and trend information to predict health risks.

[0261] Input: Health data, epidemic information

[0262] Data analysis: Predicting health risks using time series analysis and pattern recognition techniques.

[0263] Output: Predicted health risk

[0264] Step 6:

[0265] The server generates specific individual advice based on the analysis results.

[0266] Input: Health risk prediction results

[0267] Data generation: Generate specific advice to address health risks.

[0268] Output: The generated advice

[0269] Step 7:

[0270] The generated predictions and advice are communicated from the server to the display of the smart glasses.

[0271] Input: Prediction results and advice

[0272] Data transmission: Push notification technology is used to transmit data to the smart glasses.

[0273] Output: Predictions and advice displayed on smart glasses

[0274] Step 8:

[0275] The user receives a notification and takes specific measures based on the advice displayed on the smart glasses.

[0276] Input: Predictions and advice displayed on smart glasses

[0277] Take action: Based on the advice provided, take specific measures such as washing your hands, wearing a mask, and booking a vaccination appointment.

[0278] Output: The situation in which the countermeasure was implemented

[0279] 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.

[0280] In order to implement the present invention, a specific embodiment in which an emotion engine is combined with the processing flow of a child health prediction AI system will be described below.

[0281] User data entry

[0282] Users (parents or teachers) enter their child's health information through a smartphone app or PC interface. The information includes various data such as height, weight, body temperature, symptoms (cough, runny nose, fever, etc.), dietary habits, and exercise. The emotion engine also recognizes the user's emotional information and adds it to the data. The device temporarily stores this information and formats it appropriately.

[0283] Sending data

[0284] The device transmits the formatted health and emotion data to a server using a secure communication protocol (e.g., HTTPS).

[0285] Receiving and storing data

[0286] The server stores the received health information and emotion data in a database. The stored data is checked for errors and corrected for inconsistencies. If there are any missing or erroneous data, they are automatically supplemented and corrected.

[0287] Collecting trend information

[0288] The server collects disease prevalence information by region and time period from public health agencies and other data sources, which is used for further analysis.

[0289] Data Analysis and Prediction

[0290] The server uses the child's stored health, emotional, and trend information to run machine learning algorithms, including time series analysis and pattern recognition techniques. The server compares and analyzes the child's health data with past emotional data to more accurately predict health risks.

[0291] Generating personalized advice

[0292] The server generates specific advice based on the analysis results. By taking into account the emotional data, the advice content can be tailored to suit the user's psychological state. For example, if the user is feeling stressed, more detailed explanations and warnings will be provided.

[0293] Notification Delivery

[0294] The generated predictions and advice are sent from the server to home devices (parents' smartphones or PCs) and school devices via email, push notification, in-app notification, and other methods.

[0295] User Action

[0296] The user (parent or teacher) checks the notification received on their device and takes specific action based on the advice. For example, they may schedule a vaccination or provide a mask for their child. The user also enters feedback on the advice provided. The emotion engine also recognizes the user's emotions when giving feedback, and this information is used for future analysis.

[0297] Continuous data collection

[0298] The device periodically prompts the user to enter their latest health and emotional information and continuously transmits it to the server, allowing the server to provide predictions and advice based on the most up-to-date information.

[0299] Specific examples

[0300] For example, a user (parent) might enter into a smartphone app, "My 5-year-old child has had a slight fever of 37.5°C every morning for the past three days, and has symptoms of a cough and runny nose." At the same time, the emotion engine determines the parent's stress level as "high." This data is sent to a server, where it is analyzed based on local and epidemic information. The server then predicts that "Influenza is currently prevalent in the area, so there is a high risk of the child developing the disease within the next week." The server then generates advice such as "Please wash your hands thoroughly, wear a mask, and consider getting vaccinated." Since the parent is feeling stressed, the server also adds additional advice on how to reduce stress. The parent receives a notification and can immediately consult a medical institution to reduce their child's health risks.

[0301] In this way, the present invention not only effectively manages children's health and provides a reassuring environment for parents and teachers, but also provides comprehensive support that takes into account the user's own emotional state.

[0302] The processing flow will be explained below.

[0303] Step 1:

[0304] The user (parent or teacher) enters their child's health information through a smartphone app or PC interface. The information entered includes height, weight, temperature, symptoms (e.g., cough, runny nose, fever), dietary habits, and exercise. The device temporarily stores this information and formats it in an appropriate format.

[0305] Step 2:

[0306] The device uses an emotion engine to recognize the user's emotional state, including facial expressions, tone of voice, and the speed and content of input. Emotional data is temporarily stored along with health information.

[0307] Step 3:

[0308] The device transmits the formatted health and emotion data to a server using a secure communication protocol (e.g., HTTPS).

[0309] Step 4:

[0310] The server stores the received health information and emotion data in a database. The stored data is checked for errors and corrected for inconsistencies, and data is automatically supplemented or corrected as necessary.

[0311] Step 5:

[0312] The server collects disease prevalence information by region and time period from public health agencies and other data sources, which is then used in the analysis process.

[0313] Step 6:

[0314] The server uses stored child health information, emotional data, and trend information to run machine learning algorithms, using time series analysis and pattern recognition techniques to predict children's health risks.

[0315] Step 7:

[0316] The server then uses the analysis results to predict health risks, such as the probability of developing influenza and necessary preventive measures, and generates advice at the optimal time and in the most appropriate way, taking into account emotional data.

[0317] Step 8:

[0318] The server generates specific advice based on the prediction results. For example, "Due to the high risk of influenza, we recommend washing your hands, wearing a mask, and getting vaccinated." The content and tone of the advice are also adjusted appropriately based on the emotional data.

[0319] Step 9:

[0320] The server then sends the generated predictions and advice to home devices (parents' smartphones or PCs) and school devices via email, push notification, in-app notification, and other methods.

[0321] Step 10:

[0322] The user (parent or teacher) checks the notification received on the device and takes specific action based on the advice, such as making an appointment for a vaccination or making sure their child has a mask. The user provides feedback to the system about their actions based on the advice.

[0323] Step 11:

[0324] User feedback is sent to the server and stored in a database, allowing the system to continually improve and make future predictions and recommendations more accurate.

[0325] Step 12:

[0326] The device periodically prompts the user to enter their latest health and emotional information and continuously transmits this data to the server, allowing the device to provide predictions and advice based on the most up-to-date information.

[0327] As a specific example, suppose a user (parent) inputs into a smartphone app, "My 5-year-old child has had a slight fever of 37.5°C every morning for the past three days, and has symptoms of a cough and runny nose," and at the same time, the emotion engine detects a high stress level in the parent. This data is sent to the server and analyzed, and a prediction is made that "Influenza is currently spreading in the area, so there is a high risk of the child developing the disease within the next week." The server then generates advice such as "Please wash your hands thoroughly, wear a mask, and consider getting vaccinated," and, since the parent is feeling stressed, provides additional advice on how to reduce stress. The parent receives the notification and can reduce their child's health risks by consulting a medical institution as soon as possible.

[0328] The above are the specific processing steps of the child health prediction AI system that combines an emotion engine.

[0329] Example 2

[0330] 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."

[0331] Conventional child health information management systems simply input and manage health data, making it difficult to provide predictions and advice that take into account the user's emotional information. Furthermore, it is difficult to accurately predict health risks that incorporate current disease epidemic information, and there are issues with not being able to provide specific actions that users should actually take. For these reasons, there is a need for an effective system that allows users to manage their children's health with peace of mind.

[0332] 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.

[0333] In this invention, the server includes means for acquiring the child's health information, means for analyzing the acquired health information and the user's emotional information, means for collecting current disease epidemic information, means for generating a health risk prediction and individually tailored advice based on the analysis results, and means for notifying the generated prediction and advice. This enables highly accurate health risk prediction based on current disease epidemic information and provision of specific advice while taking the user's emotional information into consideration.

[0334] "Children's health information" refers to specific data about a child's health condition, such as height, weight, body temperature, symptoms (cough, runny nose, fever, etc.), diet, and amount of exercise.

[0335] "User emotion information" is data that indicates the user's (parent's or teacher's) emotional state (stress level, anxiety, etc.) and is recognized by the emotion engine.

[0336] "Disease outbreak information" means information collected from public health agencies and other data sources about the prevalence of a disease in a particular region or time.

[0337] "Health risk prediction" refers to predicting the likelihood that a child will contract a disease in the future based on the child's health information, disease prevalence information, and the user's emotional information.

[0338] "Personally tailored advice" refers to specific behavioral instructions or advice that are optimized based on the user's individual emotional state and health information.

[0339] "Notification" refers to the act of informing the user of the generated health risk predictions and advice, and is done by means of email, push notification, in-app notification, etc.

[0340] To implement the present invention, a specific embodiment of a child health prediction AI system combined with an emotion engine will be described below.

[0341] User data entry

[0342] The user (parent or teacher) enters their child's health information through a smartphone app or PC interface. Specific data entered includes height, weight, body temperature, symptoms (cough, runny nose, fever, etc.), diet, and amount of exercise. The emotion engine simultaneously recognizes the user's emotional information and adds it to the data. The device temporarily stores this information and formats it appropriately.

[0343] Sending data

[0344] The device transmits the formatted health and emotion data to a server using a secure communication protocol (e.g., HTTPS).

[0345] Receiving and storing data

[0346] The server stores the received health information and emotion data in a database. The stored data is checked for errors and corrected for inconsistencies. If there are any missing or erroneous data, they are automatically supplemented and corrected.

[0347] Collecting trend information

[0348] The server collects disease prevalence information by region and time period from public health agencies and other data sources, which is used for further analysis.

[0349] Data Analysis and Prediction

[0350] The server uses stored child health, emotional, and trend information to run machine learning algorithms, including time series analysis and pattern recognition techniques. This analysis compares the child's health data with past emotional data to more accurately predict health risks.

[0351] Generating personalized advice

[0352] The server generates specific advice based on the analysis results. By taking into account the emotional data, the advice content can be adjusted to suit the user's psychological state. For example, if the user is feeling stressed, more detailed explanations and warnings will be provided.

[0353] Notification Delivery

[0354] The generated predictions and advice are sent from the server to home devices (parents' smartphones or PCs) and school devices via email, push notification, in-app notification, and other methods.

[0355] User Action

[0356] The user (parent or teacher) checks the notification received on their device and takes specific action based on the advice. For example, they may schedule a vaccination or provide a mask for their child. The user also enters feedback on the advice provided. The emotion engine also recognizes the user's emotions when giving feedback, and this information is used for future analysis.

[0357] Continuous data collection

[0358] The device periodically prompts the user to enter their latest health and emotional information and continuously transmits it to the server, allowing the server to provide predictions and advice based on the most up-to-date information.

[0359] Specific examples

[0360] For example, suppose a user (parent) enters into a smartphone app, "My 5-year-old child has had a slight fever of 37.5°C every morning for the past three days, and has symptoms of a cough and runny nose." At the same time, the emotion engine determines the parent's stress level as "high." This data is sent to a server, where it is analyzed based on local and epidemic information. The server then predicts, "Influenza is currently prevalent in the area, so there is a high risk of the child developing the disease within the next week." The server then generates specific advice, such as "Please wash your hands thoroughly, wear a mask, and consider getting vaccinated." Furthermore, because the parent is feeling stressed, it also adds additional advice on how to reduce stress. The parent receives a notification on their device and can immediately consult a medical institution to reduce their child's health risks.

[0361] In this way, the present invention not only effectively manages children's health and provides a reassuring environment for parents and teachers, but also provides comprehensive support that takes into account the user's own emotional state.

[0362] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0363] Step 1: User Data Entry

[0364] The user (parent or teacher) uses a smartphone or PC interface to enter their child's health information. For example, the user enters information such as "height: 110 cm, weight: 20 kg, body temperature: 37.5°C, symptoms: cough, runny nose, fever, diet: normal, amount of exercise: low" into a form on the screen.

[0365] The emotion engine analyzes the emotion (e.g., stress level) entered by the user and recognizes "stress level: high" as emotional information.

[0366] These health and emotion information are stored in the device's temporary memory. Health and emotion information is acquired as input data and output in a formatted data format.

[0367] Step 2: Sending data

[0368] The device sends the formatted health and emotion data to the server using the HTTPS protocol. During this process, the formatted JSON formatted health and emotion information is used as input data. Specifically, the following data is sent to the server: { "Height": 110, "Weight": 20, "Temperature": 37.5, "Symptoms": ["Cough", "Runny nose", "Fever"], "Diet": "Normal", "Exercise": "Low", "Emotion": "Stress level: High"}.

[0369] Step 3: Receiving and storing data

[0370] The server receives health information and emotion data sent from the device. It receives formatted JSON data as input data and obtains data in a format that can be stored in a database as output data.

[0371] The received data is stored in a database within the server (e.g., a PostgreSQL database).

[0372] The server checks for errors and corrects inconsistencies in the stored data, and if weight data is missing, it fills in the missing data with an estimated value.

[0373] Step 4: Gathering trend information

[0374] The server collects disease epidemic information by region and time period from public health organizations and other data sources. It sends API requests to health organizations as input data and obtains disease epidemic information for the region as output data. In this process, it obtains influenza epidemic data from the "WHO API" and "CDC API," etc. Example of collection: "Influenza is currently spreading in the region."

[0375] Step 5: Data analysis and prediction

[0376] The server runs a machine learning algorithm based on the stored child's health information, emotional data, and epidemic information. The child's health information, emotional data, and epidemic information are used as input data, and health risk predictions are obtained as output data. Specifically, machine learning models such as ARIMA and LSTM are used, and the analysis results predict that there is a 70% risk of catching influenza within the next week.

[0377] Step 6: Generate personalized advice

[0378] The server generates specific advice to address health risks based on the analysis results. Taking emotional data into account, it generates specific behavioral instructions, such as "wash your hands thoroughly, wear a mask, and consider getting vaccinated." It also adds advice on how to reduce stress based on emotional information. The analysis results and emotional information are used as input data, and individually tailored advice is obtained as output data.

[0379] Step 7: Delivering notifications

[0380] The server then sends the generated predictions and advice to the home device. Individual advice and health risk predictions are used as input data, and notification messages are obtained as output data. Notification methods can be selected from email, push notification, in-app notification, and more, and specific messages such as "Please wash your hands thoroughly, wear a mask, and consider getting vaccinated. In addition, try relaxation techniques to reduce stress" are delivered.

[0381] Step 8: User Action

[0382] The user (parent or teacher) checks the notification they receive and takes specific measures based on the advice. Specific actions include "washing their hands thoroughly," "making sure their child wears a mask," and "making an appointment for a vaccination." Feedback, such as "I washed my hands and wore a mask," is also entered through the app. The feedback information is sent as input data to the server and used as output data to improve future advice.

[0383] Step 9: Continuous data collection

[0384] The device periodically reminds the user to enter their latest health and emotional data. The data is based on previously entered health information, and the device prompts the user to enter new temperature and symptom data every morning at 9:00 AM. The new data is then sent back to the server to keep the database up to date.

[0385] (Application example 2)

[0386] 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."

[0387] Conventional child health management systems are limited to collecting and analyzing health information and do not take into account the emotional information of parents and teachers. This can result in inappropriate advice to users. Furthermore, feedback collection and subsequent responses are insufficient, often resulting in a lack of continuous improvement. Furthermore, even in physical stores, effective support is not provided, making it difficult for parents and teachers to respond in a timely manner.

[0388] 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 acquiring health information, means for adding parent or teacher emotional information to the health information, means for analyzing the health information and emotional information, means for generating predictions and advice based on the analysis results, means for notifying the generated predictions and advice, and means for collecting feedback from the parent or teacher based on the content of the notification. This enables the provision of detailed advice that takes into account the emotional state of the parent or teacher, and enables fast and effective support in physical stores.

[0389] "Child health information" is data about a child's physical and health status, such as height, weight, temperature, symptoms, diet, and activity level.

[0390] "Emotional information" is data that represents the parent's or teacher's current emotional state (e.g., stress, relief, etc.).

[0391] "Acquisition" is the process by which a system captures data through sensors and input interfaces.

[0392] "Analysis" is the process of analyzing acquired data using algorithms and machine learning models to find specific patterns and trends.

[0393] "Prediction" is the process of estimating future health risks and conditions based on analytical results.

[0394] "Advice" refers to recommended actions or precautions provided to the user (parent or teacher) based on analysis results and predictions.

[0395] "Notification" is the process of sending prediction and advice information to the user's terminal and displaying it.

[0396] "Feedback" is data that users input into the system regarding their responses to and thoughts on advice.

[0397] "Server" means a central processing unit for storing data, analyzing, predicting and generating advice.

[0398] The system for implementing the present invention consists of a smartphone application that allows parents and teachers to input and manage their children's health and emotional information, and a cloud server for data analysis and prediction.

[0399] First, parents or teachers use a smartphone app to input their child's health data (height, weight, temperature, symptoms, etc.) and emotional information. This information is temporarily stored on the smartphone and then sent to a cloud server using a secure protocol such as HTTPS.

[0400] The cloud server stores the health and emotion information in a database, automatically checking and correcting for errors and inconsistencies. It then collects local epidemic information and provides this data to a machine learning model that performs data analysis. The machine learning model uses time series analysis and pattern recognition to predict future health risks.

[0401] Based on the prediction results, the cloud server generates specific advice that takes into account the emotional information of the parent or teacher. This advice is sent to the parent or teacher's smartphone via push notification or in-app notification. The parent or teacher who receives the advice takes appropriate action at a physical store or other location, and then enters the results and emotional feedback back into the smartphone app. The cloud server uses this feedback for further analysis to improve the accuracy of the system.

[0402] For example, if a parent types into their smartphone, "My 5-year-old child has had a slight fever of 37.5°C every morning for the past three days, and has symptoms of a cough and runny nose," and the emotion engine determines the parent's stress level as "high," that data is sent to a cloud server. The server references local epidemic information and generates a prediction that "Influenza is currently spreading in the area, so there is a high risk of the child developing the disease within the next week." The server then generates specific advice, such as "Please wash your hands thoroughly, wear a mask, and consider getting vaccinated," along with additional advice on how to reduce stress. This advice is then sent to the parent's smartphone, allowing them to immediately consult a medical institution and reduce their child's health risks.

[0403] An example of a prompt sentence to be input into the generative AI model is, "Please predict the risk of influenza and generate advice taking into consideration the child's health data (low-grade fever of 37.5°C for three days, with symptoms of cough and runny nose) and the parent's emotional information (high stress)."

[0404] In this way, the system of the present invention combines a child's health information with the emotional information of parents and teachers to provide more accurate health risk predictions and specific advice.

[0405] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0406] Step 1:

[0407] A user uses a smartphone app to input their child's health data (height, weight, temperature, symptoms, diet, amount of exercise, etc.) and emotional information (stress, peace of mind, etc.). This data is entered, formatted, and prepared for transmission to a cloud server. Entry is done manually by a parent or teacher, and the application checks for input errors and converts the data format.

[0408] Step 2:

[0409] The device sends the formatted health and emotional data to a cloud server using the HTTPS protocol. The data sent includes the child's health information (e.g., a slight fever of 37.5°C has lasted for three days) and emotional information (stress level is "high"). After sending, the server receives the data and stores it in a database.

[0410] Step 3:

[0411] The server stores the received health and emotion information in a database, and also performs error checks and corrects inconsistencies. If missing or errors are detected, automatic completion and correction processes are performed. The input here is the transmitted data, and the output is the error-corrected data.

[0412] Step 4:

[0413] The server collects current disease epidemic information from local medical institutions and public health centers. This epidemic information is stored in a database and used in later analysis processes. The input of this step is epidemic information from public institutions, and the output is the stored epidemic information.

[0414] Step 5:

[0415] The server runs a machine learning algorithm using the stored child's health information, emotional data, and trend information. The algorithm uses time series analysis and pattern recognition techniques to predict future health risks. The inputs are health information, emotional information, and trend information, and the output is a health risk prediction result.

[0416] Step 6:

[0417] Based on the analysis results, the server generates specific advice that takes into account the emotional state of the user (parent or teacher). For example, if a high risk of influenza is predicted, the server generates advice such as "wash your hands thoroughly, wear a mask, and consider getting vaccinated." The input to this step is the health risk prediction result, and the output is a written advice statement.

[0418] Step 7:

[0419] The server sends the generated predictions and advice to the parent or teacher's smartphone via push notification or in-app notification. The notification includes specific advice and precautions. The input here is the generated advice text, and the output is the notification to the user.

[0420] Step 8:

[0421] The user checks the notification and takes specific action based on the advice, such as consulting a medical institution or having their child wear a mask. The user then again inputs the results of their actions and emotional feedback into the smartphone app. This feedback is used to improve the accuracy of the system. The input here is the user's behavioral results and feedback, and the output is further data collection.

[0422] 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.

[0423] 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.

[0424] 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.

[0425] [Second embodiment]

[0426] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0427] 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.

[0428] 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).

[0429] 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.

[0430] 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.

[0431] 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).

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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."

[0438] The processing flow of the program that constitutes the AI ​​system for predicting children's health in order to put the present invention into practice will be described below.

[0439] User data entry

[0440] Users (parents or teachers) input their child's latest health information through a smartphone app or PC interface. This includes various data such as height, weight, daily temperature, symptoms (cough, runny nose, fever, etc.), diet, and amount of exercise. The device temporarily stores this information and formats it appropriately.

[0441] Sending data

[0442] The device then transmits the formatted health data to a server using encrypted protocols such as HTTPS to ensure security.

[0443] Receiving and storing data

[0444] The server stores the received data in a database, where it is simultaneously checked for errors and corrected for inconsistencies. For example, any missing data or formatting errors are corrected appropriately.

[0445] Collecting trend information

[0446] The server collects disease epidemic information by region and time period from public health agencies and other data sources, and this epidemic information is combined with children's health data for analysis.

[0447] Data Analysis and Prediction

[0448] The server uses machine learning algorithms to analyze children's health data and epidemic information. For example, it predicts future health risks based on past body temperature data and current symptoms. This prediction is made using time series analysis and pattern recognition techniques.

[0449] Generating personalized advice

[0450] The server generates specific, individualized advice based on the analysis results, such as "You are at high risk of developing influenza, so please wash your hands thoroughly, wear a mask, and consider getting vaccinated."

[0451] Notification Delivery

[0452] The generated predictions and advice are sent from the server to home devices (parents' smartphones or PCs) and school devices via email, push notifications, in-app notifications, and other means.

[0453] User Action

[0454] Users receive notifications and can take specific actions based on the advice, such as scheduling a vaccination appointment or making sure their child has a mask. Users can also provide feedback on the advice provided, helping to improve the system's accuracy.

[0455] Continuous data collection

[0456] The device periodically prompts the user to enter their child's latest health information and continuously sends it to the server, allowing the server to provide predictions and advice based on the most up-to-date information.

[0457] Specific examples

[0458] For example, a user (parent) might enter into a smartphone app, "My 5-year-old child has had a slight fever of 37.5°C each morning for the past three days, and has symptoms of a cough and runny nose." This data is sent to a server, which analyzes it based on local and epidemic information, and predicts that "Influenza is currently prevalent in the area, so there is a high risk of the child developing the disease within the next week." The server then generates advice such as "wash your hands thoroughly, wear a mask, and get vaccinated," and sends it to the parent's smartphone. The parent can receive the notification and immediately consult a medical institution to reduce their child's health risks.

[0459] In this way, the present invention effectively manages children's health and provides a reassuring environment for parents and teachers.

[0460] The processing flow will be explained below.

[0461] Step 1:

[0462] Users (parents or teachers) enter their child's health information into a smartphone app or computer, including height, weight, body temperature, symptoms (e.g., cough, runny nose, fever), dietary habits, and exercise.

[0463] Step 2:

[0464] The terminal temporarily stores the entered data and formats it appropriately, and also performs error checking to ensure the data is of the correct type and range.

[0465] Step 3:

[0466] The terminal transmits the formatted data to the server using a secure communication protocol (e.g., HTTPS).

[0467] Step 4:

[0468] The server stores the received data in a database, checks for inconsistencies and missing values, and completes or corrects the data as necessary.

[0469] Step 5:

[0470] The server collects regional and seasonal epidemic information from public health agencies and other data sources. This information is used for analysis.

[0471] Step 6:

[0472] The server uses the child's health information stored in the database and the collected epidemic information to run machine learning algorithms, including time series analysis and pattern recognition techniques.

[0473] Step 7:

[0474] Based on the analysis results, the server predicts the child's future health risks, such as the probability of developing influenza and calculating the necessary preventive measures.

[0475] Step 8:

[0476] The server generates specific advice based on the prediction results, such as "You are at high risk of influenza, so we recommend washing your hands, wearing a mask, and getting vaccinated."

[0477] Step 9:

[0478] The server then sends the generated predictions and advice to the parent or teacher's device via email, push notification, in-app notification, or other methods.

[0479] Step 10:

[0480] The user (parent or teacher) checks the notification received on the device and takes necessary action based on the advice, such as making an appointment for a vaccination or making sure that the child has a mask.

[0481] Step 11:

[0482] Users can provide feedback on the advice provided via the app or their device, which allows the system to continually improve.

[0483] Step 12:

[0484] The device periodically prompts the user to enter their latest health information and continuously transmits the acquired data to the server, allowing the server to provide predictions and advice based on the most up-to-date information.

[0485] These are the specific processing steps of the child health prediction AI system.

[0486] Example 1

[0487] 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."

[0488] Conventional child health management systems have limited methods for collecting and analyzing health information, and lack the ability to accurately predict children's health conditions and provide appropriate advice. Furthermore, because they do not collect epidemic information, they are unable to make predictions that take into account health risks by region or time of year. Furthermore, there are insufficient measures to format the collected health information or ensure its security, resulting in problems with the accuracy and safety of the data.

[0489] 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.

[0490] In this invention, the server includes means for acquiring the child's health information, means for formatting the information into an appropriate format, means for transmitting the formatted health information to the server using a secure protocol, means for error checking and correcting inconsistencies in the received health information and storing it in a database, means for collecting disease epidemic information by region and time from external data sources, means for analyzing the child's health information and epidemic information using a machine learning algorithm and predicting health risks, means for generating individual advice using natural language generation technology based on the analysis results, and means for notifying the generated predictions and advice. This enables efficient collection and analysis of a variety of health information, and enables highly accurate predictions of health risks that take epidemic information into account and the provision of specific advice.

[0491] "Children's health information" refers to data such as height, weight, daily body temperature, symptoms (cough, runny nose, fever, etc.), diet, and amount of exercise.

[0492] "Means of acquisition" refers to the means by which users input and collect health information using a smartphone app or computer interface.

[0493] The "formatting means" is a means for converting the input health information into a unified format and for performing error checks and corrections as appropriate.

[0494] "Secure protocol" refers to encrypted communication methods such as HTTPS, and is a technology that ensures security when sending and receiving data.

[0495] "Error checking and inconsistency correction" is the process of verifying the accuracy of received data and correcting missing data or formatting errors.

[0496] A "database" is a system for storing and managing collected health and epidemic information. Specifically, it includes relational databases such as MySQL and PostgreSQL.

[0497] "External data sources" refers to public health agencies and other reliable data sources that provide disease outbreak information by region and time.

[0498] "Machine learning algorithms" are statistical methods or artificial intelligence techniques used to analyze collected data and predict health risks. Examples include random forests and long short-term memory (LSTM).

[0499] "Natural language generation technology" refers to technology that generates advice in natural language that is easy for humans to understand based on the analysis results.

[0500] "Means of notification" refers to the method for communicating the generated predictions and advice to the user, and includes email, push notification, in-app notification, etc.

[0501] To implement the present invention, the following system configuration and operating procedures are required. First, a system is constructed for the purpose of collecting and analyzing children's health information. This system consists of three main elements: a user, a terminal, and a server. The detailed operation of each element and the necessary hardware and software are explained below.

[0502] User data entry

[0503] Users (parents or educators) enter their child's latest health information through a smartphone app or PC interface, including their height, weight, daily temperature, symptoms (cough, runny nose, fever, etc.), diet, and exercise.

[0504] Hardware: Consumer devices such as smartphones, tablets, and PCs.

[0505] Software: React Native apps, HTML5-based web apps, etc.

[0506] Sending data

[0507] The entered health information is temporarily stored on the device and formatted appropriately. For example, the device standardizes date formats and checks the format of numeric data. The device then converts the data into JSON format and sends it to the server using the HTTPS protocol.

[0508] Hardware: Same as above.

[0509] Software: JSON encoding, HTTPS communication, REST API, etc.

[0510] Receiving and storing data

[0511] The server captures the received data and performs error checking and inconsistency correction, specifically checking for missing data or formatting errors, before storing the data in a database.

[0512] Hardware: Servers, storage systems.

[0513] Software: Relational databases such as MySQL and PostgreSQL, error checking algorithms.

[0514] Collecting trend information

[0515] The server collects disease outbreak information by region and time period from public health agencies and other data sources (e.g., WHO, CDC). This information is stored in a database for later analysis.

[0516] Hardware: Server

[0517] Software: External API integration, data collection module.

[0518] Data Analysis and Prediction

[0519] The server uses the user's health information and trend information to apply machine learning algorithms to predict health risks, using time series analysis and pattern recognition.

[0520] Hardware: High performance computing server.

[0521] Software: Machine learning algorithms such as Random Forest, LSTM, etc.

[0522] Generating personalized advice

[0523] Based on the analysis results, specific advice is generated using natural language generation (NLG) technology, such as "You are at high risk of developing influenza, so please be sure to wash your hands and wear a mask."

[0524] Hardware: Server

[0525] Software: NLG toolkit, custom scripts.

[0526] Notification Delivery

[0527] The generated predictions and advice are sent from the server to home and school devices via email, push notifications, in-app notifications, and other methods.

[0528] Hardware: Servers, devices (smartphones, PCs, etc.).

[0529] Software: Push notification server, mail server.

[0530] User Action

[0531] Users receive notifications and take specific actions based on the advice, such as consulting a medical institution or making an appointment for a vaccination. Users can also enter feedback into the system to help improve its accuracy.

[0532] Hardware: Smartphone, PC

[0533] Software: Feedback forms, data collection systems

[0534] Continuous data collection

[0535] The device periodically prompts the user to enter their child's latest health information and continuously sends that data to the server, allowing the server to provide up-to-date predictions and advice.

[0536] Hardware: Smartphone, PC

[0537] Software: notification systems, data collection applications

[0538] Examples of concrete examples and prompts

[0539] For example, a user (parent) might enter into a smartphone app, "My 5-year-old child has had a slight fever of 37.5°C each morning for the past three days, and has symptoms of a cough and runny nose." This data is sent to a server, which analyzes it based on local and epidemic information, and predicts that "Influenza is currently prevalent in the area, so there is a high risk of the child developing the disease within the next week." The server then generates advice such as "Please wash your hands thoroughly, wear a mask, and consider getting vaccinated," and sends it to the parent's smartphone. The parent can receive the notification and immediately consult a medical institution to reduce their child's health risks.

[0540] Example prompt sentence:

[0541] "My 5-year-old child has had a temperature of 37.5°C every morning for the past three days, and is experiencing symptoms of a cough and runny nose. Please predict the future health risks associated with this condition and advise me on appropriate measures to take."

[0542] This system will enable the efficient collection and analysis of a wide range of health information, making it possible to accurately predict health risks taking into account current trends and provide specific advice.

[0543] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0544] Step 1: User Data Entry

[0545] Input: Using a smartphone app or PC interface, users input their child's health information, including their height, weight, daily temperature, symptoms (e.g., cough, runny nose, fever), diet, and exercise.

[0546] Specific behavior: The user enters information into a form on the app, for example, entering specific symptoms such as "I have had a slight fever of 37.5°C for three days."

[0547] Output: The terminal temporarily stores the input health information in its memory.

[0548] Step 2: Shaping the data

[0549] Input: Health information entered by the user.

[0550] Specific operations: The device formats the temporarily stored data, for example, standardizing date formats, checking the format of numeric data, and correcting inaccurate data.

[0551] Output: Formatted health information data.

[0552] Step 3: Encode and encrypt the data

[0553] Input: Formatted health information data.

[0554] What it does: The device converts the formatted data into JSON format and encrypts it using the HTTPS protocol, which allows for secure data transmission.

[0555] Output: Health information data encoded in JSON format and encrypted.

[0556] Step 4: Sending data

[0557] Input: Encrypted health information data.

[0558] How it works: The device sends encrypted data to the server using a communication protocol implemented through a REST API.

[0559] Output: The data is sent to the server.

[0560] Step 5: Receiving data and checking for errors

[0561] Input: Encrypted health information data.

[0562] Specific behavior: The server captures incoming data via a REST API endpoint, performs error checking to ensure the data is in the correct format, and corrects any inconsistencies.

[0563] Output: Health information data that has been error checked and corrected.

[0564] Step 6: Save your data

[0565] Input: Health information data that has been error checked and corrected.

[0566] Specific operation: The server stores the data in a database (e.g., MySQL or PostgreSQL).

[0567] Output: Health information data stored in a database.

[0568] Step 7: Gathering trend information

[0569] Input: External data sources providing epidemic information (e.g., public health agency APIs).

[0570] Specific operation: The server accesses external data sources and collects disease epidemic information by region and time period.

[0571] Output: A dataset containing epidemic information.

[0572] Step 8: Save Trend Information

[0573] Input: A dataset containing epidemic information.

[0574] Specific operation: The server stores the collected trend information in a database.

[0575] Output: Trend information data stored in a database.

[0576] Step 9: Data analysis and prediction

[0577] Input: Health information data, epidemic information data.

[0578] Specific operation: The server uses machine learning algorithms (e.g., random forest and LSTM) to analyze children's health and epidemic information, thereby predicting future health risks.

[0579] Output: Health risk prediction results.

[0580] Step 10: Generate personalized advice

[0581] Input: Health risk prediction results.

[0582] Specific operation: Based on the analysis results, the server uses natural language generation technology (NLG) to generate personalized advice, such as "wash your hands thoroughly, wear a mask, and consider getting vaccinated."

[0583] Output: The generated personalized advice.

[0584] Step 11: Creating and Delivering Notifications

[0585] Input: Generated personalized advice.

[0586] Specific operation: The server incorporates the generated advice into a notification message and delivers it to the home device via a push notification server or mail server.

[0587] Output: Notification message delivered to the user's device.

[0588] Step 12: User Action

[0589] Input: Notification message delivered to the user's device.

[0590] Specific actions: The user checks the notification and takes specific actions based on the advice, such as consulting a medical institution or making an appointment for a vaccination, and also enters feedback on the advice.

[0591] Output: Feedback is sent to the system.

[0592] Step 13: Facilitate ongoing data collection

[0593] Enter: your system's notification settings.

[0594] Specific operation: The device periodically sends notifications to the user prompting them to enter their child's latest health information. By entering new data, the device keeps the information up to date.

[0595] Output: The latest health information data is added to the system.

[0596] (Application example 1)

[0597] 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."

[0598] In modern society, managing children's health is extremely important. However, it is not easy for parents and teachers to manually collect daily health information and contact medical institutions at the appropriate time. It is particularly difficult to check children's health status in real time and take necessary measures when shopping in a physical store or going out. For this reason, there is a need for a system that can efficiently collect and analyze children's health information and provide appropriate advice.

[0599] 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.

[0600] In this invention, the server includes a means for acquiring child health information, a means for analyzing the acquired health information, and a means for generating predictions and advice based on the analysis results. This enables a means for entering a customer's health information using a smart device and sending it to the server, a means for receiving analysis results from the server and displaying them on the smart device, a means for periodically collecting data on the child's health information and sending it to the server, and a means for collecting information on current disease trends and using it for analysis. This allows parents shopping in a physical store to easily check their child's health status and quickly take appropriate measures.

[0601] "Children's health information" refers to data related to a child's health condition, such as height, weight, body temperature, symptoms, diet, and amount of exercise.

[0602] "Means of acquisition" refers to devices or software that allow users to input and record health information.

[0603] The "means of analysis" refers to algorithms or software that predict health risks and generate appropriate advice based on the acquired health information.

[0604] The "means for generating predictions and advice" is software that automatically suggests future health risks and appropriate measures based on the analysis results.

[0605] "Means of notification" refers to technologies such as email, push notifications, and in-app notifications used to notify users of generated predictions and advice.

[0606] "Smart devices" are mobile terminals with advanced computing power and communication functions, such as smartphones, smart glasses, and head-mounted displays.

[0607] A "server" refers to a computer system that stores and analyzes data over a network.

[0608] The "means for inputting health information of customers" refers to a user interface that allows customers to input health information via a smart device.

[0609] "Means for transmitting to the server" refers to the communication protocol and functions for securely transmitting the acquired health information to the server.

[0610] "Means for receiving and displaying analysis results from a server" refers to technology for receiving analysis results from a server and displaying them on the display of a smart device.

[0611] "Means for periodically collecting data and transmitting it to a server" refers to a system or program that periodically collects children's health data and transmits it to a server.

[0612] "Means for collecting disease epidemic information and using it for analysis" refers to software and databases for collecting disease epidemic information by region and time period and using it for analysis.

[0613] To practice the invention, we describe an implementation of a child health prediction system that can be used in a brick-and-mortar environment.

[0614] Hardware and software used

[0615] Hardware: Smart glasses (e.g., regular smart glasses)

[0616] Software: Python 3.x, HTTP / HTTPS API

[0617] Server: Data analysis server on the cloud

[0618] Communication protocol: HTTPS

[0619] Program processing flow

[0620] 1. Data entry by the user (parent or store staff)

[0621] The user uses the smart glasses' interface to input their child's latest health information, including height, weight, daily temperature, symptoms (e.g., cough, runny nose, fever), diet, and exercise. This information is temporarily stored in the smart glasses' memory and formatted accordingly.

[0622] 2. Data transmission

[0623] The smart glasses device transmits the processed health data to a server, using encrypted protocols such as HTTPS to ensure security.

[0624] 3. Receipt and storage of data

[0625] The server stores the received data in a database, where it is simultaneously checked for errors and corrected for inconsistencies.

[0626] 4. Collecting trend information

[0627] The server collects disease epidemic information by region and time period from public health agencies and other data sources, and this epidemic information is combined with children's health data for analysis.

[0628] 5. Data analysis and prediction

[0629] The server uses machine learning algorithms to analyze children's health data and epidemic information. For example, it predicts future health risks based on past body temperature data and current symptoms. This prediction is made using time series analysis and pattern recognition techniques.

[0630] 6. Generating personalized advice

[0631] The server generates specific, individualized advice based on the analysis results, such as "You are at high risk of developing influenza, so please wash your hands thoroughly, wear a mask, and consider getting vaccinated."

[0632] 7. Delivery of Notices

[0633] The generated predictions and advice are communicated from the server to the smart glasses display using suitable push notification technology.

[0634] Specific examples

[0635] For example, if a parent inputs into their smart glasses, "My temperature has been 37.5°C for the past three days, and I have symptoms of a cough and runny nose," that data is sent from the smart glasses to a server. The server analyzes local epidemic information and past data and predicts that "Influenza is currently spreading in the area, so there is a high risk of developing the disease within the next week." The server then generates advice such as "wash your hands thoroughly, wear a mask, and get vaccinated," and sends it to the parent's smart glasses. The parent can receive this notification and take immediate action.

[0636] Prompt Sentence Examples

[0637] Your temperature has been 37.5°C for the past three days, and you have a cough and runny nose. There is currently an influenza outbreak in your area, so you are at high risk of developing influenza. We recommend that you wash your hands thoroughly, wear a mask, and get vaccinated.

[0638] As described above, the present invention effectively manages children's health and provides an environment where parents and store staff can respond quickly through use in physical stores.

[0639] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0640] Step 1:

[0641] The user uses the smart glasses interface to input their child's latest health information.

[0642] Input: Height, weight, temperature, symptoms (cough, runny nose, fever, etc.), diet, amount of exercise

[0643] Data processing: The input data is converted into an appropriate format and temporarily stored in the smart glasses' memory.

[0644] Output: Formatted health data

[0645] Step 2:

[0646] The device transmits the formatted health data to the server.

[0647] Input: Formatted health data

[0648] Data Transmission: Transmit data using encrypted protocols such as HTTPS.

[0649] Output: Data sent to the server

[0650] Step 3:

[0651] The server stores the received data in a database, checks for errors, and corrects inconsistencies.

[0652] Input: Submitted health data

[0653] Data storage and processing: storing data in a database and correcting any missing or formatting errors.

[0654] Output: Accurate health data stored

[0655] Step 4:

[0656] The server collects disease outbreak information by region and time period from public health agencies and other data sources.

[0657] Input: Trend information by region and time

[0658] Data collection: Obtaining epidemic information from public health agencies and other data sources.

[0659] Output: Disease epidemic information by region and time period

[0660] Step 5:

[0661] The server uses machine learning algorithms to analyze children's health data and trend information to predict health risks.

[0662] Input: Health data, epidemic information

[0663] Data analysis: Predicting health risks using time series analysis and pattern recognition techniques.

[0664] Output: Predicted health risk

[0665] Step 6:

[0666] The server generates specific individual advice based on the analysis results.

[0667] Input: Health risk prediction results

[0668] Data generation: Generate specific advice to address health risks.

[0669] Output: The generated advice

[0670] Step 7:

[0671] The generated predictions and advice are communicated from the server to the display of the smart glasses.

[0672] Input: Prediction results and advice

[0673] Data transmission: Push notification technology is used to transmit data to the smart glasses.

[0674] Output: Predictions and advice displayed on smart glasses

[0675] Step 8:

[0676] The user receives a notification and takes specific measures based on the advice displayed on the smart glasses.

[0677] Input: Predictions and advice displayed on smart glasses

[0678] Take action: Based on the advice provided, take specific measures such as washing your hands, wearing a mask, and booking a vaccination appointment.

[0679] Output: The situation in which the countermeasure was implemented

[0680] 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.

[0681] In order to implement the present invention, a specific embodiment in which an emotion engine is combined with the processing flow of a child health prediction AI system will be described below.

[0682] User data entry

[0683] Users (parents or teachers) enter their child's health information through a smartphone app or PC interface. The information includes various data such as height, weight, body temperature, symptoms (cough, runny nose, fever, etc.), dietary habits, and exercise. The emotion engine also recognizes the user's emotional information and adds it to the data. The device temporarily stores this information and formats it appropriately.

[0684] Sending data

[0685] The device transmits the formatted health and emotion data to a server using a secure communication protocol (e.g., HTTPS).

[0686] Receiving and storing data

[0687] The server stores the received health information and emotion data in a database. The stored data is checked for errors and corrected for inconsistencies. If there are any missing or erroneous data, they are automatically supplemented and corrected.

[0688] Collecting trend information

[0689] The server collects disease prevalence information by region and time period from public health agencies and other data sources, which is used for further analysis.

[0690] Data Analysis and Prediction

[0691] The server uses the child's stored health, emotional, and trend information to run machine learning algorithms, including time series analysis and pattern recognition techniques. The server compares and analyzes the child's health data with past emotional data to more accurately predict health risks.

[0692] Generating personalized advice

[0693] The server generates specific advice based on the analysis results. By taking into account the emotional data, the advice content can be tailored to suit the user's psychological state. For example, if the user is feeling stressed, more detailed explanations and warnings will be provided.

[0694] Notification Delivery

[0695] The generated predictions and advice are sent from the server to home devices (parents' smartphones or PCs) and school devices via email, push notification, in-app notification, and other methods.

[0696] User Action

[0697] The user (parent or teacher) checks the notification received on their device and takes specific action based on the advice. For example, they may schedule a vaccination or provide a mask for their child. The user also enters feedback on the advice provided. The emotion engine also recognizes the user's emotions when giving feedback, and this information is used for future analysis.

[0698] Continuous data collection

[0699] The device periodically prompts the user to enter their latest health and emotional information and continuously transmits it to the server, allowing the server to provide predictions and advice based on the most up-to-date information.

[0700] Specific examples

[0701] For example, a user (parent) might enter into a smartphone app, "My 5-year-old child has had a slight fever of 37.5°C every morning for the past three days, and has symptoms of a cough and runny nose." At the same time, the emotion engine determines the parent's stress level as "high." This data is sent to a server, where it is analyzed based on local and epidemic information. The server then predicts that "Influenza is currently prevalent in the area, so there is a high risk of the child developing the disease within the next week." The server then generates advice such as "Please wash your hands thoroughly, wear a mask, and consider getting vaccinated." Since the parent is feeling stressed, the server also adds additional advice on how to reduce stress. The parent receives a notification and can immediately consult a medical institution to reduce their child's health risks.

[0702] In this way, the present invention not only effectively manages children's health and provides a reassuring environment for parents and teachers, but also provides comprehensive support that takes into account the user's own emotional state.

[0703] The processing flow will be explained below.

[0704] Step 1:

[0705] The user (parent or teacher) enters their child's health information through a smartphone app or PC interface. The information entered includes height, weight, temperature, symptoms (e.g., cough, runny nose, fever), dietary habits, and exercise. The device temporarily stores this information and formats it in an appropriate format.

[0706] Step 2:

[0707] The device uses an emotion engine to recognize the user's emotional state, including facial expressions, tone of voice, and the speed and content of input. Emotional data is temporarily stored along with health information.

[0708] Step 3:

[0709] The device transmits the formatted health and emotion data to a server using a secure communication protocol (e.g., HTTPS).

[0710] Step 4:

[0711] The server stores the received health information and emotion data in a database. The stored data is checked for errors and corrected for inconsistencies, and data is automatically supplemented or corrected as necessary.

[0712] Step 5:

[0713] The server collects disease prevalence information by region and time period from public health agencies and other data sources, which is then used in the analysis process.

[0714] Step 6:

[0715] The server uses stored child health information, emotional data, and trend information to run machine learning algorithms, using time series analysis and pattern recognition techniques to predict children's health risks.

[0716] Step 7:

[0717] The server then uses the analysis results to predict health risks, such as the probability of developing influenza and necessary preventive measures, and generates advice at the optimal time and in the most appropriate way, taking into account emotional data.

[0718] Step 8:

[0719] The server generates specific advice based on the prediction results. For example, "Due to the high risk of influenza, we recommend washing your hands, wearing a mask, and getting vaccinated." The content and tone of the advice are also adjusted appropriately based on the emotional data.

[0720] Step 9:

[0721] The server then sends the generated predictions and advice to home devices (parents' smartphones or PCs) and school devices via email, push notification, in-app notification, and other methods.

[0722] Step 10:

[0723] The user (parent or teacher) checks the notification received on the device and takes specific action based on the advice, such as making an appointment for a vaccination or making sure their child has a mask. The user provides feedback to the system about their actions based on the advice.

[0724] Step 11:

[0725] User feedback is sent to the server and stored in a database, allowing the system to continually improve and make future predictions and recommendations more accurate.

[0726] Step 12:

[0727] The device periodically prompts the user to enter their latest health and emotional information and continuously transmits this data to the server, allowing the device to provide predictions and advice based on the most up-to-date information.

[0728] As a specific example, suppose a user (parent) inputs into a smartphone app, "My 5-year-old child has had a slight fever of 37.5°C every morning for the past three days, and has symptoms of a cough and runny nose," and at the same time, the emotion engine detects a high stress level in the parent. This data is sent to the server and analyzed, and a prediction is made that "Influenza is currently spreading in the area, so there is a high risk of the child developing the disease within the next week." The server then generates advice such as "Please wash your hands thoroughly, wear a mask, and consider getting vaccinated," and, since the parent is feeling stressed, provides additional advice on how to reduce stress. The parent receives the notification and can reduce their child's health risks by consulting a medical institution as soon as possible.

[0729] The above are the specific processing steps of the child health prediction AI system that combines an emotion engine.

[0730] Example 2

[0731] 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."

[0732] Conventional child health information management systems simply input and manage health data, making it difficult to provide predictions and advice that take into account the user's emotional information. Furthermore, it is difficult to accurately predict health risks that incorporate current disease epidemic information, and there are issues with not being able to provide specific actions that users should actually take. For these reasons, there is a need for an effective system that allows users to manage their children's health with peace of mind.

[0733] 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.

[0734] In this invention, the server includes means for acquiring the child's health information, means for analyzing the acquired health information and the user's emotional information, means for collecting current disease epidemic information, means for generating a health risk prediction and individually tailored advice based on the analysis results, and means for notifying the generated prediction and advice. This enables highly accurate health risk prediction based on current disease epidemic information and provision of specific advice while taking the user's emotional information into consideration.

[0735] "Children's health information" refers to specific data about a child's health condition, such as height, weight, body temperature, symptoms (cough, runny nose, fever, etc.), diet, and amount of exercise.

[0736] "User emotion information" is data that indicates the user's (parent's or teacher's) emotional state (stress level, anxiety, etc.) and is recognized by the emotion engine.

[0737] "Disease outbreak information" means information collected from public health agencies and other data sources about the prevalence of a disease in a particular region or time.

[0738] "Health risk prediction" refers to predicting the likelihood that a child will contract a disease in the future based on the child's health information, disease prevalence information, and the user's emotional information.

[0739] "Personally tailored advice" refers to specific behavioral instructions or advice that are optimized based on the user's individual emotional state and health information.

[0740] "Notification" refers to the act of informing the user of the generated health risk predictions and advice, and is done by means of email, push notification, in-app notification, etc.

[0741] To implement the present invention, a specific embodiment of a child health prediction AI system combined with an emotion engine will be described below.

[0742] User data entry

[0743] The user (parent or teacher) enters their child's health information through a smartphone app or PC interface. Specific data entered includes height, weight, body temperature, symptoms (cough, runny nose, fever, etc.), diet, and amount of exercise. The emotion engine simultaneously recognizes the user's emotional information and adds it to the data. The device temporarily stores this information and formats it appropriately.

[0744] Sending data

[0745] The device transmits the formatted health and emotion data to a server using a secure communication protocol (e.g., HTTPS).

[0746] Receiving and storing data

[0747] The server stores the received health information and emotion data in a database. The stored data is checked for errors and corrected for inconsistencies. If there are any missing or erroneous data, they are automatically supplemented and corrected.

[0748] Collecting trend information

[0749] The server collects disease prevalence information by region and time period from public health agencies and other data sources, which is used for further analysis.

[0750] Data Analysis and Prediction

[0751] The server uses stored child health, emotional, and trend information to run machine learning algorithms, including time series analysis and pattern recognition techniques. This analysis compares the child's health data with past emotional data to more accurately predict health risks.

[0752] Generating personalized advice

[0753] The server generates specific advice based on the analysis results. By taking into account the emotional data, the advice content can be adjusted to suit the user's psychological state. For example, if the user is feeling stressed, more detailed explanations and warnings will be provided.

[0754] Notification Delivery

[0755] The generated predictions and advice are sent from the server to home devices (parents' smartphones or PCs) and school devices via email, push notification, in-app notification, and other methods.

[0756] User Action

[0757] The user (parent or teacher) checks the notification received on their device and takes specific action based on the advice. For example, they may schedule a vaccination or provide a mask for their child. The user also enters feedback on the advice provided. The emotion engine also recognizes the user's emotions when giving feedback, and this information is used for future analysis.

[0758] Continuous data collection

[0759] The device periodically prompts the user to enter their latest health and emotional information and continuously transmits it to the server, allowing the server to provide predictions and advice based on the most up-to-date information.

[0760] Specific examples

[0761] For example, suppose a user (parent) enters into a smartphone app, "My 5-year-old child has had a slight fever of 37.5°C every morning for the past three days, and has symptoms of a cough and runny nose." At the same time, the emotion engine determines the parent's stress level as "high." This data is sent to a server, where it is analyzed based on local and epidemic information. The server then predicts, "Influenza is currently prevalent in the area, so there is a high risk of the child developing the disease within the next week." The server then generates specific advice, such as "Please wash your hands thoroughly, wear a mask, and consider getting vaccinated." Furthermore, because the parent is feeling stressed, it also adds additional advice on how to reduce stress. The parent receives a notification on their device and can immediately consult a medical institution to reduce their child's health risks.

[0762] In this way, the present invention not only effectively manages children's health and provides a reassuring environment for parents and teachers, but also provides comprehensive support that takes into account the user's own emotional state.

[0763] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0764] Step 1: User Data Entry

[0765] The user (parent or teacher) uses a smartphone or PC interface to enter their child's health information. For example, the user enters information such as "height: 110 cm, weight: 20 kg, body temperature: 37.5°C, symptoms: cough, runny nose, fever, diet: normal, amount of exercise: low" into a form on the screen.

[0766] The emotion engine analyzes the emotion (e.g., stress level) entered by the user and recognizes "stress level: high" as emotional information.

[0767] These health and emotion information are stored in the device's temporary memory. Health and emotion information is acquired as input data and output in a formatted data format.

[0768] Step 2: Sending data

[0769] The device sends the formatted health and emotion data to the server using the HTTPS protocol. During this process, the formatted JSON formatted health and emotion information is used as input data. Specifically, the following data is sent to the server: { "Height": 110, "Weight": 20, "Temperature": 37.5, "Symptoms": ["Cough", "Runny nose", "Fever"], "Diet": "Normal", "Exercise": "Low", "Emotion": "Stress level: High"}.

[0770] Step 3: Receiving and storing data

[0771] The server receives health information and emotion data sent from the device. It receives formatted JSON data as input data and obtains data in a format that can be stored in a database as output data.

[0772] The received data is stored in a database within the server (e.g., a PostgreSQL database).

[0773] The server checks for errors and corrects inconsistencies in the stored data, and if weight data is missing, it fills in the missing data with an estimated value.

[0774] Step 4: Gathering trend information

[0775] The server collects disease epidemic information by region and time period from public health organizations and other data sources. It sends API requests to health organizations as input data and obtains disease epidemic information for the region as output data. In this process, it obtains influenza epidemic data from the "WHO API" and "CDC API," etc. Example of collection: "Influenza is currently spreading in the region."

[0776] Step 5: Data analysis and prediction

[0777] The server runs a machine learning algorithm based on the stored child's health information, emotional data, and epidemic information. The child's health information, emotional data, and epidemic information are used as input data, and health risk predictions are obtained as output data. Specifically, machine learning models such as ARIMA and LSTM are used, and the analysis results predict that there is a 70% risk of catching influenza within the next week.

[0778] Step 6: Generate personalized advice

[0779] The server generates specific advice to address health risks based on the analysis results. Taking emotional data into account, it generates specific behavioral instructions, such as "wash your hands thoroughly, wear a mask, and consider getting vaccinated." It also adds advice on how to reduce stress based on emotional information. The analysis results and emotional information are used as input data, and individually tailored advice is obtained as output data.

[0780] Step 7: Delivering notifications

[0781] The server then sends the generated predictions and advice to the home device. Individual advice and health risk predictions are used as input data, and notification messages are obtained as output data. Notification methods can be selected from email, push notification, in-app notification, and more, and specific messages such as "Please wash your hands thoroughly, wear a mask, and consider getting vaccinated. In addition, try relaxation techniques to reduce stress" are delivered.

[0782] Step 8: User Action

[0783] The user (parent or teacher) checks the notification they receive and takes specific measures based on the advice. Specific actions include "washing their hands thoroughly," "making sure their child wears a mask," and "making an appointment for a vaccination." Feedback, such as "I washed my hands and wore a mask," is also entered through the app. The feedback information is sent as input data to the server and used as output data to improve future advice.

[0784] Step 9: Continuous data collection

[0785] The device periodically reminds the user to enter their latest health and emotional data. The data is based on previously entered health information, and the device prompts the user to enter new temperature and symptom data every morning at 9:00 AM. The new data is then sent back to the server to keep the database up to date.

[0786] (Application example 2)

[0787] 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."

[0788] Conventional child health management systems are limited to collecting and analyzing health information and do not take into account the emotional information of parents and teachers. This can result in inappropriate advice to users. Furthermore, feedback collection and subsequent responses are insufficient, often resulting in a lack of continuous improvement. Furthermore, even in physical stores, effective support is not provided, making it difficult for parents and teachers to respond in a timely manner.

[0789] 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 acquiring health information, means for adding parent or teacher emotional information to the health information, means for analyzing the health information and emotional information, means for generating predictions and advice based on the analysis results, means for notifying the generated predictions and advice, and means for collecting feedback from the parent or teacher based on the content of the notification. This enables the provision of detailed advice that takes into account the emotional state of the parent or teacher, and enables fast and effective support in physical stores.

[0790] "Child health information" is data about a child's physical and health status, such as height, weight, temperature, symptoms, diet, and activity level.

[0791] "Emotional information" is data that represents the parent's or teacher's current emotional state (e.g., stress, relief, etc.).

[0792] "Acquisition" is the process by which a system captures data through sensors and input interfaces.

[0793] "Analysis" is the process of analyzing acquired data using algorithms and machine learning models to find specific patterns and trends.

[0794] "Prediction" is the process of estimating future health risks and conditions based on analytical results.

[0795] "Advice" refers to recommended actions or precautions provided to the user (parent or teacher) based on analysis results and predictions.

[0796] "Notification" is the process of sending prediction and advice information to the user's terminal and displaying it.

[0797] "Feedback" is data that users input into the system regarding their responses to and thoughts on advice.

[0798] "Server" means a central processing unit for storing data, analyzing, predicting and generating advice.

[0799] The system for implementing the present invention consists of a smartphone application that allows parents and teachers to input and manage their children's health and emotional information, and a cloud server for data analysis and prediction.

[0800] First, parents or teachers use a smartphone app to input their child's health data (height, weight, temperature, symptoms, etc.) and emotional information. This information is temporarily stored on the smartphone and then sent to a cloud server using a secure protocol such as HTTPS.

[0801] The cloud server stores the health and emotion information in a database, automatically checking and correcting for errors and inconsistencies. It then collects local epidemic information and provides this data to a machine learning model that performs data analysis. The machine learning model uses time series analysis and pattern recognition to predict future health risks.

[0802] Based on the prediction results, the cloud server generates specific advice that takes into account the emotional information of the parent or teacher. This advice is sent to the parent or teacher's smartphone via push notification or in-app notification. The parent or teacher who receives the advice takes appropriate action at a physical store or other location, and then enters the results and emotional feedback back into the smartphone app. The cloud server uses this feedback for further analysis to improve the accuracy of the system.

[0803] For example, if a parent types into their smartphone, "My 5-year-old child has had a slight fever of 37.5°C every morning for the past three days, and has symptoms of a cough and runny nose," and the emotion engine determines the parent's stress level as "high," that data is sent to a cloud server. The server references local epidemic information and generates a prediction that "Influenza is currently spreading in the area, so there is a high risk of the child developing the disease within the next week." The server then generates specific advice, such as "Please wash your hands thoroughly, wear a mask, and consider getting vaccinated," along with additional advice on how to reduce stress. This advice is then sent to the parent's smartphone, allowing them to immediately consult a medical institution and reduce their child's health risks.

[0804] An example of a prompt sentence to be input into the generative AI model is, "Please predict the risk of influenza and generate advice taking into consideration the child's health data (low-grade fever of 37.5°C for three days, with symptoms of cough and runny nose) and the parent's emotional information (high stress)."

[0805] In this way, the system of the present invention combines a child's health information with the emotional information of parents and teachers to provide more accurate health risk predictions and specific advice.

[0806] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0807] Step 1:

[0808] A user uses a smartphone app to input their child's health data (height, weight, temperature, symptoms, diet, amount of exercise, etc.) and emotional information (stress, peace of mind, etc.). This data is entered, formatted, and prepared for transmission to a cloud server. Entry is done manually by a parent or teacher, and the application checks for input errors and converts the data format.

[0809] Step 2:

[0810] The device sends the formatted health and emotional data to a cloud server using the HTTPS protocol. The data sent includes the child's health information (e.g., a slight fever of 37.5°C has lasted for three days) and emotional information (stress level is "high"). After sending, the server receives the data and stores it in a database.

[0811] Step 3:

[0812] The server stores the received health and emotion information in a database, and also performs error checks and corrects inconsistencies. If missing or errors are detected, automatic completion and correction processes are performed. The input here is the transmitted data, and the output is the error-corrected data.

[0813] Step 4:

[0814] The server collects current disease epidemic information from local medical institutions and public health centers. This epidemic information is stored in a database and used in later analysis processes. The input of this step is epidemic information from public institutions, and the output is the stored epidemic information.

[0815] Step 5:

[0816] The server runs a machine learning algorithm using the stored child's health information, emotional data, and trend information. The algorithm uses time series analysis and pattern recognition techniques to predict future health risks. The inputs are health information, emotional information, and trend information, and the output is a health risk prediction result.

[0817] Step 6:

[0818] Based on the analysis results, the server generates specific advice that takes into account the emotional state of the user (parent or teacher). For example, if a high risk of influenza is predicted, the server generates advice such as "wash your hands thoroughly, wear a mask, and consider getting vaccinated." The input to this step is the health risk prediction result, and the output is a written advice statement.

[0819] Step 7:

[0820] The server sends the generated predictions and advice to the parent or teacher's smartphone via push notification or in-app notification. The notification includes specific advice and precautions. The input here is the generated advice text, and the output is the notification to the user.

[0821] Step 8:

[0822] The user checks the notification and takes specific action based on the advice, such as consulting a medical institution or having their child wear a mask. The user then again inputs the results of their actions and emotional feedback into the smartphone app. This feedback is used to improve the accuracy of the system. The input here is the user's behavioral results and feedback, and the output is further data collection.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] [Third embodiment]

[0827] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0828] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0829] 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).

[0830] 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.

[0831] 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.

[0832] 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).

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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."

[0839] The processing flow of the program that constitutes the AI ​​system for predicting children's health in order to put the present invention into practice will be described below.

[0840] User data entry

[0841] Users (parents or teachers) input their child's latest health information through a smartphone app or PC interface. This includes various data such as height, weight, daily temperature, symptoms (cough, runny nose, fever, etc.), diet, and amount of exercise. The device temporarily stores this information and formats it appropriately.

[0842] Sending data

[0843] The device then transmits the formatted health data to a server using encrypted protocols such as HTTPS to ensure security.

[0844] Receiving and storing data

[0845] The server stores the received data in a database, where it is simultaneously checked for errors and corrected for inconsistencies. For example, any missing data or formatting errors are corrected appropriately.

[0846] Collecting trend information

[0847] The server collects disease epidemic information by region and time period from public health agencies and other data sources, and this epidemic information is combined with children's health data for analysis.

[0848] Data Analysis and Prediction

[0849] The server uses machine learning algorithms to analyze children's health data and epidemic information. For example, it predicts future health risks based on past body temperature data and current symptoms. This prediction is made using time series analysis and pattern recognition techniques.

[0850] Generating personalized advice

[0851] The server generates specific, individualized advice based on the analysis results, such as "You are at high risk of developing influenza, so please wash your hands thoroughly, wear a mask, and consider getting vaccinated."

[0852] Notification Delivery

[0853] The generated predictions and advice are sent from the server to home devices (parents' smartphones or PCs) and school devices via email, push notifications, in-app notifications, and other means.

[0854] User Action

[0855] Users receive notifications and can take specific actions based on the advice, such as scheduling a vaccination appointment or making sure their child has a mask. Users can also provide feedback on the advice provided, helping to improve the system's accuracy.

[0856] Continuous data collection

[0857] The device periodically prompts the user to enter their child's latest health information and continuously sends it to the server, allowing the server to provide predictions and advice based on the most up-to-date information.

[0858] Specific examples

[0859] For example, a user (parent) might enter into a smartphone app, "My 5-year-old child has had a slight fever of 37.5°C each morning for the past three days, and has symptoms of a cough and runny nose." This data is sent to a server, which analyzes it based on local and epidemic information, and predicts that "Influenza is currently prevalent in the area, so there is a high risk of the child developing the disease within the next week." The server then generates advice such as "wash your hands thoroughly, wear a mask, and get vaccinated," and sends it to the parent's smartphone. The parent can receive the notification and immediately consult a medical institution to reduce their child's health risks.

[0860] In this way, the present invention effectively manages children's health and provides a reassuring environment for parents and teachers.

[0861] The processing flow will be explained below.

[0862] Step 1:

[0863] Users (parents or teachers) enter their child's health information into a smartphone app or computer, including height, weight, body temperature, symptoms (e.g., cough, runny nose, fever), dietary habits, and exercise.

[0864] Step 2:

[0865] The terminal temporarily stores the entered data and formats it appropriately, and also performs error checking to ensure the data is of the correct type and range.

[0866] Step 3:

[0867] The terminal transmits the formatted data to the server using a secure communication protocol (e.g., HTTPS).

[0868] Step 4:

[0869] The server stores the received data in a database, checks for inconsistencies and missing values, and completes or corrects the data as necessary.

[0870] Step 5:

[0871] The server collects regional and seasonal epidemic information from public health agencies and other data sources. This information is used for analysis.

[0872] Step 6:

[0873] The server uses the child's health information stored in the database and the collected epidemic information to run machine learning algorithms, including time series analysis and pattern recognition techniques.

[0874] Step 7:

[0875] Based on the analysis results, the server predicts the child's future health risks, such as the probability of developing influenza and calculating the necessary preventive measures.

[0876] Step 8:

[0877] The server generates specific advice based on the prediction results, such as "You are at high risk of influenza, so we recommend washing your hands, wearing a mask, and getting vaccinated."

[0878] Step 9:

[0879] The server then sends the generated predictions and advice to the parent or teacher's device via email, push notification, in-app notification, or other methods.

[0880] Step 10:

[0881] The user (parent or teacher) checks the notification received on the device and takes necessary action based on the advice, such as making an appointment for a vaccination or making sure that the child has a mask.

[0882] Step 11:

[0883] Users can provide feedback on the advice provided via the app or their device, which allows the system to continually improve.

[0884] Step 12:

[0885] The device periodically prompts the user to enter their latest health information and continuously transmits the acquired data to the server, allowing the server to provide predictions and advice based on the most up-to-date information.

[0886] These are the specific processing steps of the child health prediction AI system.

[0887] Example 1

[0888] 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."

[0889] Conventional child health management systems have limited methods for collecting and analyzing health information, and lack the ability to accurately predict children's health conditions and provide appropriate advice. Furthermore, because they do not collect epidemic information, they are unable to make predictions that take into account health risks by region or time of year. Furthermore, there are insufficient measures to format the collected health information or ensure its security, resulting in problems with the accuracy and safety of the data.

[0890] 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.

[0891] In this invention, the server includes means for acquiring the child's health information, means for formatting the information into an appropriate format, means for transmitting the formatted health information to the server using a secure protocol, means for error checking and correcting inconsistencies in the received health information and storing it in a database, means for collecting disease epidemic information by region and time from external data sources, means for analyzing the child's health information and epidemic information using a machine learning algorithm and predicting health risks, means for generating individual advice using natural language generation technology based on the analysis results, and means for notifying the generated predictions and advice. This enables efficient collection and analysis of a variety of health information, and enables highly accurate predictions of health risks that take epidemic information into account and the provision of specific advice.

[0892] "Children's health information" refers to data such as height, weight, daily body temperature, symptoms (cough, runny nose, fever, etc.), diet, and amount of exercise.

[0893] "Means of acquisition" refers to the means by which users input and collect health information using a smartphone app or computer interface.

[0894] The "formatting means" is a means for converting the input health information into a unified format and for performing error checks and corrections as appropriate.

[0895] "Secure protocol" refers to encrypted communication methods such as HTTPS, and is a technology that ensures security when sending and receiving data.

[0896] "Error checking and inconsistency correction" is the process of verifying the accuracy of received data and correcting missing data or formatting errors.

[0897] A "database" is a system for storing and managing collected health and epidemic information. Specifically, it includes relational databases such as MySQL and PostgreSQL.

[0898] "External data sources" refers to public health agencies and other reliable data sources that provide disease outbreak information by region and time.

[0899] "Machine learning algorithms" are statistical methods or artificial intelligence techniques used to analyze collected data and predict health risks. Examples include random forests and long short-term memory (LSTM).

[0900] "Natural language generation technology" refers to technology that generates advice in natural language that is easy for humans to understand based on the analysis results.

[0901] "Means of notification" refers to the method for communicating the generated predictions and advice to the user, and includes email, push notification, in-app notification, etc.

[0902] To implement the present invention, the following system configuration and operating procedures are required. First, a system is constructed for the purpose of collecting and analyzing children's health information. This system consists of three main elements: a user, a terminal, and a server. The detailed operation of each element and the necessary hardware and software are explained below.

[0903] User data entry

[0904] Users (parents or educators) enter their child's latest health information through a smartphone app or PC interface, including their height, weight, daily temperature, symptoms (cough, runny nose, fever, etc.), diet, and exercise.

[0905] Hardware: Consumer devices such as smartphones, tablets, and PCs.

[0906] Software: React Native apps, HTML5-based web apps, etc.

[0907] Sending data

[0908] The entered health information is temporarily stored on the device and formatted appropriately. For example, the device standardizes date formats and checks the format of numeric data. The device then converts the data into JSON format and sends it to the server using the HTTPS protocol.

[0909] Hardware: Same as above.

[0910] Software: JSON encoding, HTTPS communication, REST API, etc.

[0911] Receiving and storing data

[0912] The server captures the received data and performs error checking and inconsistency correction, specifically checking for missing data or formatting errors, before storing the data in a database.

[0913] Hardware: Servers, storage systems.

[0914] Software: Relational databases such as MySQL and PostgreSQL, error checking algorithms.

[0915] Collecting trend information

[0916] The server collects disease outbreak information by region and time period from public health agencies and other data sources (e.g., WHO, CDC). This information is stored in a database for later analysis.

[0917] Hardware: Server

[0918] Software: External API integration, data collection module.

[0919] Data Analysis and Prediction

[0920] The server uses the user's health information and trend information to apply machine learning algorithms to predict health risks, using time series analysis and pattern recognition.

[0921] Hardware: High performance computing server.

[0922] Software: Machine learning algorithms such as Random Forest, LSTM, etc.

[0923] Generating personalized advice

[0924] Based on the analysis results, specific advice is generated using natural language generation (NLG) technology, such as "You are at high risk of developing influenza, so please be sure to wash your hands and wear a mask."

[0925] Hardware: Server

[0926] Software: NLG toolkit, custom scripts.

[0927] Notification Delivery

[0928] The generated predictions and advice are sent from the server to home and school devices via email, push notifications, in-app notifications, and other methods.

[0929] Hardware: Servers, devices (smartphones, PCs, etc.).

[0930] Software: Push notification server, mail server.

[0931] User Action

[0932] Users receive notifications and take specific actions based on the advice, such as consulting a medical institution or making an appointment for a vaccination. Users can also enter feedback into the system to help improve its accuracy.

[0933] Hardware: Smartphone, PC

[0934] Software: Feedback forms, data collection systems

[0935] Continuous data collection

[0936] The device periodically prompts the user to enter their child's latest health information and continuously sends that data to the server, allowing the server to provide up-to-date predictions and advice.

[0937] Hardware: Smartphone, PC

[0938] Software: notification systems, data collection applications

[0939] Examples of concrete examples and prompts

[0940] For example, a user (parent) might enter into a smartphone app, "My 5-year-old child has had a slight fever of 37.5°C each morning for the past three days, and has symptoms of a cough and runny nose." This data is sent to a server, which analyzes it based on local and epidemic information, and predicts that "Influenza is currently prevalent in the area, so there is a high risk of the child developing the disease within the next week." The server then generates advice such as "Please wash your hands thoroughly, wear a mask, and consider getting vaccinated," and sends it to the parent's smartphone. The parent can receive the notification and immediately consult a medical institution to reduce their child's health risks.

[0941] Example prompt sentence:

[0942] "My 5-year-old child has had a temperature of 37.5°C every morning for the past three days, and is experiencing symptoms of a cough and runny nose. Please predict the future health risks associated with this condition and advise me on appropriate measures to take."

[0943] This system will enable the efficient collection and analysis of a wide range of health information, making it possible to accurately predict health risks taking into account current trends and provide specific advice.

[0944] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0945] Step 1: User Data Entry

[0946] Input: Using a smartphone app or PC interface, users input their child's health information, including their height, weight, daily temperature, symptoms (e.g., cough, runny nose, fever), diet, and exercise.

[0947] Specific behavior: The user enters information into a form on the app, for example, entering specific symptoms such as "I have had a slight fever of 37.5°C for three days."

[0948] Output: The terminal temporarily stores the input health information in its memory.

[0949] Step 2: Shaping the data

[0950] Input: Health information entered by the user.

[0951] Specific operations: The device formats the temporarily stored data, for example, standardizing date formats, checking the format of numeric data, and correcting inaccurate data.

[0952] Output: Formatted health information data.

[0953] Step 3: Encode and encrypt the data

[0954] Input: Formatted health information data.

[0955] What it does: The device converts the formatted data into JSON format and encrypts it using the HTTPS protocol, which allows for secure data transmission.

[0956] Output: Health information data encoded in JSON format and encrypted.

[0957] Step 4: Sending data

[0958] Input: Encrypted health information data.

[0959] How it works: The device sends encrypted data to the server using a communication protocol implemented through a REST API.

[0960] Output: The data is sent to the server.

[0961] Step 5: Receiving data and checking for errors

[0962] Input: Encrypted health information data.

[0963] Specific behavior: The server captures incoming data via a REST API endpoint, performs error checking to ensure the data is in the correct format, and corrects any inconsistencies.

[0964] Output: Health information data that has been error checked and corrected.

[0965] Step 6: Save your data

[0966] Input: Health information data that has been error checked and corrected.

[0967] Specific operation: The server stores the data in a database (e.g., MySQL or PostgreSQL).

[0968] Output: Health information data stored in a database.

[0969] Step 7: Gathering trend information

[0970] Input: External data sources providing epidemic information (e.g., public health agency APIs).

[0971] Specific operation: The server accesses external data sources and collects disease epidemic information by region and time period.

[0972] Output: A dataset containing epidemic information.

[0973] Step 8: Save Trend Information

[0974] Input: A dataset containing epidemic information.

[0975] Specific operation: The server stores the collected trend information in a database.

[0976] Output: Trend information data stored in a database.

[0977] Step 9: Data analysis and prediction

[0978] Input: Health information data, epidemic information data.

[0979] Specific operation: The server uses machine learning algorithms (e.g., random forest and LSTM) to analyze children's health and epidemic information, thereby predicting future health risks.

[0980] Output: Health risk prediction results.

[0981] Step 10: Generate personalized advice

[0982] Input: Health risk prediction results.

[0983] Specific operation: Based on the analysis results, the server uses natural language generation technology (NLG) to generate personalized advice, such as "wash your hands thoroughly, wear a mask, and consider getting vaccinated."

[0984] Output: The generated personalized advice.

[0985] Step 11: Creating and Delivering Notifications

[0986] Input: Generated personalized advice.

[0987] Specific operation: The server incorporates the generated advice into a notification message and delivers it to the home device via a push notification server or mail server.

[0988] Output: Notification message delivered to the user's device.

[0989] Step 12: User Action

[0990] Input: Notification message delivered to the user's device.

[0991] Specific actions: The user checks the notification and takes specific actions based on the advice, such as consulting a medical institution or making an appointment for a vaccination, and also enters feedback on the advice.

[0992] Output: Feedback is sent to the system.

[0993] Step 13: Facilitate ongoing data collection

[0994] Enter: your system's notification settings.

[0995] Specific operation: The device periodically sends notifications to the user prompting them to enter their child's latest health information. By entering new data, the device keeps the information up to date.

[0996] Output: The latest health information data is added to the system.

[0997] (Application example 1)

[0998] 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."

[0999] In modern society, managing children's health is extremely important. However, it is not easy for parents and teachers to manually collect daily health information and contact medical institutions at the appropriate time. It is particularly difficult to check children's health status in real time and take necessary measures when shopping in a physical store or going out. For this reason, there is a need for a system that can efficiently collect and analyze children's health information and provide appropriate advice.

[1000] 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.

[1001] In this invention, the server includes a means for acquiring child health information, a means for analyzing the acquired health information, and a means for generating predictions and advice based on the analysis results. This enables a means for entering a customer's health information using a smart device and sending it to the server, a means for receiving analysis results from the server and displaying them on the smart device, a means for periodically collecting data on the child's health information and sending it to the server, and a means for collecting information on current disease trends and using it for analysis. This allows parents shopping in a physical store to easily check their child's health status and quickly take appropriate measures.

[1002] "Children's health information" refers to data related to a child's health condition, such as height, weight, body temperature, symptoms, diet, and amount of exercise.

[1003] "Means of acquisition" refers to devices or software that allow users to input and record health information.

[1004] The "means of analysis" refers to algorithms or software that predict health risks and generate appropriate advice based on the acquired health information.

[1005] The "means for generating predictions and advice" is software that automatically suggests future health risks and appropriate measures based on the analysis results.

[1006] "Means of notification" refers to technologies such as email, push notifications, and in-app notifications used to notify users of generated predictions and advice.

[1007] "Smart devices" are mobile terminals with advanced computing power and communication functions, such as smartphones, smart glasses, and head-mounted displays.

[1008] A "server" refers to a computer system that stores and analyzes data over a network.

[1009] The "means for inputting health information of customers" refers to a user interface that allows customers to input health information via a smart device.

[1010] "Means for transmitting to the server" refers to the communication protocol and functions for securely transmitting the acquired health information to the server.

[1011] "Means for receiving and displaying analysis results from a server" refers to technology for receiving analysis results from a server and displaying them on the display of a smart device.

[1012] "Means for periodically collecting data and transmitting it to a server" refers to a system or program that periodically collects children's health data and transmits it to a server.

[1013] "Means for collecting disease epidemic information and using it for analysis" refers to software and databases for collecting disease epidemic information by region and time period and using it for analysis.

[1014] To practice the invention, we describe an implementation of a child health prediction system that can be used in a brick-and-mortar environment.

[1015] Hardware and software used

[1016] Hardware: Smart glasses (e.g., regular smart glasses)

[1017] Software: Python 3.x, HTTP / HTTPS API

[1018] Server: Data analysis server on the cloud

[1019] Communication protocol: HTTPS

[1020] Program processing flow

[1021] 1. Data entry by the user (parent or store staff)

[1022] The user uses the smart glasses' interface to input their child's latest health information, including height, weight, daily temperature, symptoms (e.g., cough, runny nose, fever), diet, and exercise. This information is temporarily stored in the smart glasses' memory and formatted accordingly.

[1023] 2. Data transmission

[1024] The smart glasses device transmits the processed health data to a server, using encrypted protocols such as HTTPS to ensure security.

[1025] 3. Receipt and storage of data

[1026] The server stores the received data in a database, where it is simultaneously checked for errors and corrected for inconsistencies.

[1027] 4. Collecting trend information

[1028] The server collects disease epidemic information by region and time period from public health agencies and other data sources, and this epidemic information is combined with children's health data for analysis.

[1029] 5. Data analysis and prediction

[1030] The server uses machine learning algorithms to analyze children's health data and epidemic information. For example, it predicts future health risks based on past body temperature data and current symptoms. This prediction is made using time series analysis and pattern recognition techniques.

[1031] 6. Generating personalized advice

[1032] The server generates specific, individualized advice based on the analysis results, such as "You are at high risk of developing influenza, so please wash your hands thoroughly, wear a mask, and consider getting vaccinated."

[1033] 7. Delivery of Notices

[1034] The generated predictions and advice are communicated from the server to the smart glasses display using suitable push notification technology.

[1035] Specific examples

[1036] For example, if a parent inputs into their smart glasses, "My temperature has been 37.5°C for the past three days, and I have symptoms of a cough and runny nose," that data is sent from the smart glasses to a server. The server analyzes local epidemic information and past data and predicts that "Influenza is currently spreading in the area, so there is a high risk of developing the disease within the next week." The server then generates advice such as "wash your hands thoroughly, wear a mask, and get vaccinated," and sends it to the parent's smart glasses. The parent can receive this notification and take immediate action.

[1037] Prompt Sentence Examples

[1038] Your temperature has been 37.5°C for the past three days, and you have a cough and runny nose. There is currently an influenza outbreak in your area, so you are at high risk of developing influenza. We recommend that you wash your hands thoroughly, wear a mask, and get vaccinated.

[1039] As described above, the present invention effectively manages children's health and provides an environment where parents and store staff can respond quickly through use in physical stores.

[1040] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1041] Step 1:

[1042] The user uses the smart glasses interface to input their child's latest health information.

[1043] Input: Height, weight, temperature, symptoms (cough, runny nose, fever, etc.), diet, amount of exercise

[1044] Data processing: The input data is converted into an appropriate format and temporarily stored in the smart glasses' memory.

[1045] Output: Formatted health data

[1046] Step 2:

[1047] The device transmits the formatted health data to the server.

[1048] Input: Formatted health data

[1049] Data Transmission: Transmit data using encrypted protocols such as HTTPS.

[1050] Output: Data sent to the server

[1051] Step 3:

[1052] The server stores the received data in a database, checks for errors, and corrects inconsistencies.

[1053] Input: Submitted health data

[1054] Data storage and processing: storing data in a database and correcting any missing or formatting errors.

[1055] Output: Accurate health data stored

[1056] Step 4:

[1057] The server collects disease outbreak information by region and time period from public health agencies and other data sources.

[1058] Input: Trend information by region and time

[1059] Data collection: Obtaining epidemic information from public health agencies and other data sources.

[1060] Output: Disease epidemic information by region and time period

[1061] Step 5:

[1062] The server uses machine learning algorithms to analyze children's health data and trend information to predict health risks.

[1063] Input: Health data, epidemic information

[1064] Data analysis: Predicting health risks using time series analysis and pattern recognition techniques.

[1065] Output: Predicted health risk

[1066] Step 6:

[1067] The server generates specific individual advice based on the analysis results.

[1068] Input: Health risk prediction results

[1069] Data generation: Generate specific advice to address health risks.

[1070] Output: The generated advice

[1071] Step 7:

[1072] The generated predictions and advice are communicated from the server to the display of the smart glasses.

[1073] Input: Prediction results and advice

[1074] Data transmission: Push notification technology is used to transmit data to the smart glasses.

[1075] Output: Predictions and advice displayed on smart glasses

[1076] Step 8:

[1077] The user receives a notification and takes specific measures based on the advice displayed on the smart glasses.

[1078] Input: Predictions and advice displayed on smart glasses

[1079] Take action: Based on the advice provided, take specific measures such as washing your hands, wearing a mask, and booking a vaccination appointment.

[1080] Output: The situation in which the countermeasure was implemented

[1081] 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.

[1082] In order to implement the present invention, a specific embodiment in which an emotion engine is combined with the processing flow of a child health prediction AI system will be described below.

[1083] User data entry

[1084] Users (parents or teachers) enter their child's health information through a smartphone app or PC interface. The information includes various data such as height, weight, body temperature, symptoms (cough, runny nose, fever, etc.), dietary habits, and exercise. The emotion engine also recognizes the user's emotional information and adds it to the data. The device temporarily stores this information and formats it appropriately.

[1085] Sending data

[1086] The device transmits the formatted health and emotion data to a server using a secure communication protocol (e.g., HTTPS).

[1087] Receiving and storing data

[1088] The server stores the received health information and emotion data in a database. The stored data is checked for errors and corrected for inconsistencies. If there are any missing or erroneous data, they are automatically supplemented and corrected.

[1089] Collecting trend information

[1090] The server collects disease prevalence information by region and time period from public health agencies and other data sources, which is used for further analysis.

[1091] Data Analysis and Prediction

[1092] The server uses the child's stored health, emotional, and trend information to run machine learning algorithms, including time series analysis and pattern recognition techniques. The server compares and analyzes the child's health data with past emotional data to more accurately predict health risks.

[1093] Generating personalized advice

[1094] The server generates specific advice based on the analysis results. By taking into account the emotional data, the advice content can be tailored to suit the user's psychological state. For example, if the user is feeling stressed, more detailed explanations and warnings will be provided.

[1095] Notification Delivery

[1096] The generated predictions and advice are sent from the server to home devices (parents' smartphones or PCs) and school devices via email, push notification, in-app notification, and other methods.

[1097] User Action

[1098] The user (parent or teacher) checks the notification received on their device and takes specific action based on the advice. For example, they may schedule a vaccination or provide a mask for their child. The user also enters feedback on the advice provided. The emotion engine also recognizes the user's emotions when giving feedback, and this information is used for future analysis.

[1099] Continuous data collection

[1100] The device periodically prompts the user to enter their latest health and emotional information and continuously transmits it to the server, allowing the server to provide predictions and advice based on the most up-to-date information.

[1101] Specific examples

[1102] For example, a user (parent) might enter into a smartphone app, "My 5-year-old child has had a slight fever of 37.5°C every morning for the past three days, and has symptoms of a cough and runny nose." At the same time, the emotion engine determines the parent's stress level as "high." This data is sent to a server, where it is analyzed based on local and epidemic information. The server then predicts that "Influenza is currently prevalent in the area, so there is a high risk of the child developing the disease within the next week." The server then generates advice such as "Please wash your hands thoroughly, wear a mask, and consider getting vaccinated." Since the parent is feeling stressed, the server also adds additional advice on how to reduce stress. The parent receives a notification and can immediately consult a medical institution to reduce their child's health risks.

[1103] In this way, the present invention not only effectively manages children's health and provides a reassuring environment for parents and teachers, but also provides comprehensive support that takes into account the user's own emotional state.

[1104] The processing flow will be explained below.

[1105] Step 1:

[1106] The user (parent or teacher) enters their child's health information through a smartphone app or PC interface. The information entered includes height, weight, temperature, symptoms (e.g., cough, runny nose, fever), dietary habits, and exercise. The device temporarily stores this information and formats it in an appropriate format.

[1107] Step 2:

[1108] The device uses an emotion engine to recognize the user's emotional state, including facial expressions, tone of voice, and the speed and content of input. Emotional data is temporarily stored along with health information.

[1109] Step 3:

[1110] The device transmits the formatted health and emotion data to a server using a secure communication protocol (e.g., HTTPS).

[1111] Step 4:

[1112] The server stores the received health information and emotion data in a database. The stored data is checked for errors and corrected for inconsistencies, and data is automatically supplemented or corrected as necessary.

[1113] Step 5:

[1114] The server collects disease prevalence information by region and time period from public health agencies and other data sources, which is then used in the analysis process.

[1115] Step 6:

[1116] The server uses stored child health information, emotional data, and trend information to run machine learning algorithms, using time series analysis and pattern recognition techniques to predict children's health risks.

[1117] Step 7:

[1118] The server then uses the analysis results to predict health risks, such as the probability of developing influenza and necessary preventive measures, and generates advice at the optimal time and in the most appropriate way, taking into account emotional data.

[1119] Step 8:

[1120] The server generates specific advice based on the prediction results. For example, "Due to the high risk of influenza, we recommend washing your hands, wearing a mask, and getting vaccinated." The content and tone of the advice are also adjusted appropriately based on the emotional data.

[1121] Step 9:

[1122] The server then sends the generated predictions and advice to home devices (parents' smartphones or PCs) and school devices via email, push notification, in-app notification, and other methods.

[1123] Step 10:

[1124] The user (parent or teacher) checks the notification received on the device and takes specific action based on the advice, such as making an appointment for a vaccination or making sure their child has a mask. The user provides feedback to the system about their actions based on the advice.

[1125] Step 11:

[1126] User feedback is sent to the server and stored in a database, allowing the system to continually improve and make future predictions and recommendations more accurate.

[1127] Step 12:

[1128] The device periodically prompts the user to enter their latest health and emotional information and continuously transmits this data to the server, allowing the device to provide predictions and advice based on the most up-to-date information.

[1129] As a specific example, suppose a user (parent) inputs into a smartphone app, "My 5-year-old child has had a slight fever of 37.5°C every morning for the past three days, and has symptoms of a cough and runny nose," and at the same time, the emotion engine detects a high stress level in the parent. This data is sent to the server and analyzed, and a prediction is made that "Influenza is currently spreading in the area, so there is a high risk of the child developing the disease within the next week." The server then generates advice such as "Please wash your hands thoroughly, wear a mask, and consider getting vaccinated," and, since the parent is feeling stressed, provides additional advice on how to reduce stress. The parent receives the notification and can reduce their child's health risks by consulting a medical institution as soon as possible.

[1130] The above are the specific processing steps of the child health prediction AI system that combines an emotion engine.

[1131] Example 2

[1132] 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."

[1133] Conventional child health information management systems simply input and manage health data, making it difficult to provide predictions and advice that take into account the user's emotional information. Furthermore, it is difficult to accurately predict health risks that incorporate current disease epidemic information, and there are issues with not being able to provide specific actions that users should actually take. For these reasons, there is a need for an effective system that allows users to manage their children's health with peace of mind.

[1134] 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.

[1135] In this invention, the server includes means for acquiring the child's health information, means for analyzing the acquired health information and the user's emotional information, means for collecting current disease epidemic information, means for generating a health risk prediction and individually tailored advice based on the analysis results, and means for notifying the generated prediction and advice. This enables highly accurate health risk prediction based on current disease epidemic information and provision of specific advice while taking the user's emotional information into consideration.

[1136] "Children's health information" refers to specific data about a child's health condition, such as height, weight, body temperature, symptoms (cough, runny nose, fever, etc.), diet, and amount of exercise.

[1137] "User emotion information" is data that indicates the user's (parent's or teacher's) emotional state (stress level, anxiety, etc.) and is recognized by the emotion engine.

[1138] "Disease outbreak information" means information collected from public health agencies and other data sources about the prevalence of a disease in a particular region or time.

[1139] "Health risk prediction" refers to predicting the likelihood that a child will contract a disease in the future based on the child's health information, disease prevalence information, and the user's emotional information.

[1140] "Personally tailored advice" refers to specific behavioral instructions or advice that are optimized based on the user's individual emotional state and health information.

[1141] "Notification" refers to the act of informing the user of the generated health risk predictions and advice, and is done by means of email, push notification, in-app notification, etc.

[1142] To implement the present invention, a specific embodiment of a child health prediction AI system combined with an emotion engine will be described below.

[1143] User data entry

[1144] The user (parent or teacher) enters their child's health information through a smartphone app or PC interface. Specific data entered includes height, weight, body temperature, symptoms (cough, runny nose, fever, etc.), diet, and amount of exercise. The emotion engine simultaneously recognizes the user's emotional information and adds it to the data. The device temporarily stores this information and formats it appropriately.

[1145] Sending data

[1146] The device transmits the formatted health and emotion data to a server using a secure communication protocol (e.g., HTTPS).

[1147] Receiving and storing data

[1148] The server stores the received health information and emotion data in a database. The stored data is checked for errors and corrected for inconsistencies. If there are any missing or erroneous data, they are automatically supplemented and corrected.

[1149] Collecting trend information

[1150] The server collects disease prevalence information by region and time period from public health agencies and other data sources, which is used for further analysis.

[1151] Data Analysis and Prediction

[1152] The server uses stored child health, emotional, and trend information to run machine learning algorithms, including time series analysis and pattern recognition techniques. This analysis compares the child's health data with past emotional data to more accurately predict health risks.

[1153] Generating personalized advice

[1154] The server generates specific advice based on the analysis results. By taking into account the emotional data, the advice content can be adjusted to suit the user's psychological state. For example, if the user is feeling stressed, more detailed explanations and warnings will be provided.

[1155] Notification Delivery

[1156] The generated predictions and advice are sent from the server to home devices (parents' smartphones or PCs) and school devices via email, push notification, in-app notification, and other methods.

[1157] User Action

[1158] The user (parent or teacher) checks the notification received on their device and takes specific action based on the advice. For example, they may schedule a vaccination or provide a mask for their child. The user also enters feedback on the advice provided. The emotion engine also recognizes the user's emotions when giving feedback, and this information is used for future analysis.

[1159] Continuous data collection

[1160] The device periodically prompts the user to enter their latest health and emotional information and continuously transmits it to the server, allowing the server to provide predictions and advice based on the most up-to-date information.

[1161] Specific examples

[1162] For example, suppose a user (parent) enters into a smartphone app, "My 5-year-old child has had a slight fever of 37.5°C every morning for the past three days, and has symptoms of a cough and runny nose." At the same time, the emotion engine determines the parent's stress level as "high." This data is sent to a server, where it is analyzed based on local and epidemic information. The server then predicts, "Influenza is currently prevalent in the area, so there is a high risk of the child developing the disease within the next week." The server then generates specific advice, such as "Please wash your hands thoroughly, wear a mask, and consider getting vaccinated." Furthermore, because the parent is feeling stressed, it also adds additional advice on how to reduce stress. The parent receives a notification on their device and can immediately consult a medical institution to reduce their child's health risks.

[1163] In this way, the present invention not only effectively manages children's health and provides a reassuring environment for parents and teachers, but also provides comprehensive support that takes into account the user's own emotional state.

[1164] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1165] Step 1: User Data Entry

[1166] The user (parent or teacher) uses a smartphone or PC interface to enter their child's health information. For example, the user enters information such as "height: 110 cm, weight: 20 kg, body temperature: 37.5°C, symptoms: cough, runny nose, fever, diet: normal, amount of exercise: low" into a form on the screen.

[1167] The emotion engine analyzes the emotion (e.g., stress level) entered by the user and recognizes "stress level: high" as emotional information.

[1168] These health and emotion information are stored in the device's temporary memory. Health and emotion information is acquired as input data and output in a formatted data format.

[1169] Step 2: Sending data

[1170] The device sends the formatted health and emotion data to the server using the HTTPS protocol. During this process, the formatted JSON formatted health and emotion information is used as input data. Specifically, the following data is sent to the server: { "Height": 110, "Weight": 20, "Temperature": 37.5, "Symptoms": ["Cough", "Runny nose", "Fever"], "Diet": "Normal", "Exercise": "Low", "Emotion": "Stress level: High"}.

[1171] Step 3: Receiving and storing data

[1172] The server receives health information and emotion data sent from the device. It receives formatted JSON data as input data and obtains data in a format that can be stored in a database as output data.

[1173] The received data is stored in a database within the server (e.g., a PostgreSQL database).

[1174] The server checks for errors and corrects inconsistencies in the stored data, and if weight data is missing, it fills in the missing data with an estimated value.

[1175] Step 4: Gathering trend information

[1176] The server collects disease epidemic information by region and time period from public health organizations and other data sources. It sends API requests to health organizations as input data and obtains disease epidemic information for the region as output data. In this process, it obtains influenza epidemic data from the "WHO API" and "CDC API," etc. Example of collection: "Influenza is currently spreading in the region."

[1177] Step 5: Data analysis and prediction

[1178] The server runs a machine learning algorithm based on the stored child's health information, emotional data, and epidemic information. The child's health information, emotional data, and epidemic information are used as input data, and health risk predictions are obtained as output data. Specifically, machine learning models such as ARIMA and LSTM are used, and the analysis results predict that there is a 70% risk of catching influenza within the next week.

[1179] Step 6: Generate personalized advice

[1180] The server generates specific advice to address health risks based on the analysis results. Taking emotional data into account, it generates specific behavioral instructions, such as "wash your hands thoroughly, wear a mask, and consider getting vaccinated." It also adds advice on how to reduce stress based on emotional information. The analysis results and emotional information are used as input data, and individually tailored advice is obtained as output data.

[1181] Step 7: Delivering notifications

[1182] The server then sends the generated predictions and advice to the home device. Individual advice and health risk predictions are used as input data, and notification messages are obtained as output data. Notification methods can be selected from email, push notification, in-app notification, and more, and specific messages such as "Please wash your hands thoroughly, wear a mask, and consider getting vaccinated. In addition, try relaxation techniques to reduce stress" are delivered.

[1183] Step 8: User Action

[1184] The user (parent or teacher) checks the notification they receive and takes specific measures based on the advice. Specific actions include "washing their hands thoroughly," "making sure their child wears a mask," and "making an appointment for a vaccination." Feedback, such as "I washed my hands and wore a mask," is also entered through the app. The feedback information is sent as input data to the server and used as output data to improve future advice.

[1185] Step 9: Continuous data collection

[1186] The device periodically reminds the user to enter their latest health and emotional data. The data is based on previously entered health information, and the device prompts the user to enter new temperature and symptom data every morning at 9:00 AM. The new data is then sent back to the server to keep the database up to date.

[1187] (Application example 2)

[1188] 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."

[1189] Conventional child health management systems are limited to collecting and analyzing health information and do not take into account the emotional information of parents and teachers. This can result in inappropriate advice to users. Furthermore, feedback collection and subsequent responses are insufficient, often resulting in a lack of continuous improvement. Furthermore, even in physical stores, effective support is not provided, making it difficult for parents and teachers to respond in a timely manner.

[1190] 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 acquiring health information, means for adding parent or teacher emotional information to the health information, means for analyzing the health information and emotional information, means for generating predictions and advice based on the analysis results, means for notifying the generated predictions and advice, and means for collecting feedback from the parent or teacher based on the content of the notification. This enables the provision of detailed advice that takes into account the emotional state of the parent or teacher, and enables fast and effective support in physical stores.

[1191] "Child health information" is data about a child's physical and health status, such as height, weight, temperature, symptoms, diet, and activity level.

[1192] "Emotional information" is data that represents the parent's or teacher's current emotional state (e.g., stress, relief, etc.).

[1193] "Acquisition" is the process by which a system captures data through sensors and input interfaces.

[1194] "Analysis" is the process of analyzing acquired data using algorithms and machine learning models to find specific patterns and trends.

[1195] "Prediction" is the process of estimating future health risks and conditions based on analytical results.

[1196] "Advice" refers to recommended actions or precautions provided to the user (parent or teacher) based on analysis results and predictions.

[1197] "Notification" is the process of sending prediction and advice information to the user's terminal and displaying it.

[1198] "Feedback" is data that users input into the system regarding their responses to and thoughts on advice.

[1199] "Server" means a central processing unit for storing data, analyzing, predicting and generating advice.

[1200] The system for implementing the present invention consists of a smartphone application that allows parents and teachers to input and manage their children's health and emotional information, and a cloud server for data analysis and prediction.

[1201] First, parents or teachers use a smartphone app to input their child's health data (height, weight, temperature, symptoms, etc.) and emotional information. This information is temporarily stored on the smartphone and then sent to a cloud server using a secure protocol such as HTTPS.

[1202] The cloud server stores the health and emotion information in a database, automatically checking and correcting for errors and inconsistencies. It then collects local epidemic information and provides this data to a machine learning model that performs data analysis. The machine learning model uses time series analysis and pattern recognition to predict future health risks.

[1203] Based on the prediction results, the cloud server generates specific advice that takes into account the emotional information of the parent or teacher. This advice is sent to the parent or teacher's smartphone via push notification or in-app notification. The parent or teacher who receives the advice takes appropriate action at a physical store or other location, and then enters the results and emotional feedback back into the smartphone app. The cloud server uses this feedback for further analysis to improve the accuracy of the system.

[1204] For example, if a parent types into their smartphone, "My 5-year-old child has had a slight fever of 37.5°C every morning for the past three days, and has symptoms of a cough and runny nose," and the emotion engine determines the parent's stress level as "high," that data is sent to a cloud server. The server references local epidemic information and generates a prediction that "Influenza is currently spreading in the area, so there is a high risk of the child developing the disease within the next week." The server then generates specific advice, such as "Please wash your hands thoroughly, wear a mask, and consider getting vaccinated," along with additional advice on how to reduce stress. This advice is then sent to the parent's smartphone, allowing them to immediately consult a medical institution and reduce their child's health risks.

[1205] An example of a prompt sentence to be input into the generative AI model is, "Please predict the risk of influenza and generate advice taking into consideration the child's health data (low-grade fever of 37.5°C for three days, with symptoms of cough and runny nose) and the parent's emotional information (high stress)."

[1206] In this way, the system of the present invention combines a child's health information with the emotional information of parents and teachers to provide more accurate health risk predictions and specific advice.

[1207] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1208] Step 1:

[1209] A user uses a smartphone app to input their child's health data (height, weight, temperature, symptoms, diet, amount of exercise, etc.) and emotional information (stress, peace of mind, etc.). This data is entered, formatted, and prepared for transmission to a cloud server. Entry is done manually by a parent or teacher, and the application checks for input errors and converts the data format.

[1210] Step 2:

[1211] The device sends the formatted health and emotional data to a cloud server using the HTTPS protocol. The data sent includes the child's health information (e.g., a slight fever of 37.5°C has lasted for three days) and emotional information (stress level is "high"). After sending, the server receives the data and stores it in a database.

[1212] Step 3:

[1213] The server stores the received health and emotion information in a database, and also performs error checks and corrects inconsistencies. If missing or errors are detected, automatic completion and correction processes are performed. The input here is the transmitted data, and the output is the error-corrected data.

[1214] Step 4:

[1215] The server collects current disease epidemic information from local medical institutions and public health centers. This epidemic information is stored in a database and used in later analysis processes. The input of this step is epidemic information from public institutions, and the output is the stored epidemic information.

[1216] Step 5:

[1217] The server runs a machine learning algorithm using the stored child's health information, emotional data, and trend information. The algorithm uses time series analysis and pattern recognition techniques to predict future health risks. The inputs are health information, emotional information, and trend information, and the output is a health risk prediction result.

[1218] Step 6:

[1219] Based on the analysis results, the server generates specific advice that takes into account the emotional state of the user (parent or teacher). For example, if a high risk of influenza is predicted, the server generates advice such as "wash your hands thoroughly, wear a mask, and consider getting vaccinated." The input to this step is the health risk prediction result, and the output is a written advice statement.

[1220] Step 7:

[1221] The server sends the generated predictions and advice to the parent or teacher's smartphone via push notification or in-app notification. The notification includes specific advice and precautions. The input here is the generated advice text, and the output is the notification to the user.

[1222] Step 8:

[1223] The user checks the notification and takes specific action based on the advice, such as consulting a medical institution or having their child wear a mask. The user then again inputs the results of their actions and emotional feedback into the smartphone app. This feedback is used to improve the accuracy of the system. The input here is the user's behavioral results and feedback, and the output is further data collection.

[1224] 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.

[1225] 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.

[1226] 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.

[1227] [Fourth embodiment]

[1228] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1229] 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.

[1230] 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).

[1231] 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.

[1232] 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.

[1233] 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).

[1234] 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.

[1235] 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.

[1236] 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.

[1237] 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.

[1238] 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.

[1239] 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.

[1240] 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."

[1241] The processing flow of the program that constitutes the AI ​​system for predicting children's health in order to put the present invention into practice will be described below.

[1242] User data entry

[1243] Users (parents or teachers) input their child's latest health information through a smartphone app or PC interface. This includes various data such as height, weight, daily temperature, symptoms (cough, runny nose, fever, etc.), diet, and amount of exercise. The device temporarily stores this information and formats it appropriately.

[1244] Sending data

[1245] The device then transmits the formatted health data to a server using encrypted protocols such as HTTPS to ensure security.

[1246] Receiving and storing data

[1247] The server stores the received data in a database, where it is simultaneously checked for errors and corrected for inconsistencies. For example, any missing data or formatting errors are corrected appropriately.

[1248] Collecting trend information

[1249] The server collects disease epidemic information by region and time period from public health agencies and other data sources, and this epidemic information is combined with children's health data for analysis.

[1250] Data Analysis and Prediction

[1251] The server uses machine learning algorithms to analyze children's health data and epidemic information. For example, it predicts future health risks based on past body temperature data and current symptoms. This prediction is made using time series analysis and pattern recognition techniques.

[1252] Generating personalized advice

[1253] The server generates specific, individualized advice based on the analysis results, such as "You are at high risk of developing influenza, so please wash your hands thoroughly, wear a mask, and consider getting vaccinated."

[1254] Notification Delivery

[1255] The generated predictions and advice are sent from the server to home devices (parents' smartphones or PCs) and school devices via email, push notifications, in-app notifications, and other means.

[1256] User Action

[1257] Users receive notifications and can take specific actions based on the advice, such as scheduling a vaccination appointment or making sure their child has a mask. Users can also provide feedback on the advice provided, helping to improve the system's accuracy.

[1258] Continuous data collection

[1259] The device periodically prompts the user to enter their child's latest health information and continuously sends it to the server, allowing the server to provide predictions and advice based on the most up-to-date information.

[1260] Specific examples

[1261] For example, a user (parent) might enter into a smartphone app, "My 5-year-old child has had a slight fever of 37.5°C each morning for the past three days, and has symptoms of a cough and runny nose." This data is sent to a server, which analyzes it based on local and epidemic information, and predicts that "Influenza is currently prevalent in the area, so there is a high risk of the child developing the disease within the next week." The server then generates advice such as "wash your hands thoroughly, wear a mask, and get vaccinated," and sends it to the parent's smartphone. The parent can receive the notification and immediately consult a medical institution to reduce their child's health risks.

[1262] In this way, the present invention effectively manages children's health and provides a reassuring environment for parents and teachers.

[1263] The processing flow will be explained below.

[1264] Step 1:

[1265] Users (parents or teachers) enter their child's health information into a smartphone app or computer, including height, weight, body temperature, symptoms (e.g., cough, runny nose, fever), dietary habits, and exercise.

[1266] Step 2:

[1267] The terminal temporarily stores the entered data and formats it appropriately, and also performs error checking to ensure the data is of the correct type and range.

[1268] Step 3:

[1269] The terminal transmits the formatted data to the server using a secure communication protocol (e.g., HTTPS).

[1270] Step 4:

[1271] The server stores the received data in a database, checks for inconsistencies and missing values, and completes or corrects the data as necessary.

[1272] Step 5:

[1273] The server collects regional and seasonal epidemic information from public health agencies and other data sources. This information is used for analysis.

[1274] Step 6:

[1275] The server uses the child's health information stored in the database and the collected epidemic information to run machine learning algorithms, including time series analysis and pattern recognition techniques.

[1276] Step 7:

[1277] Based on the analysis results, the server predicts the child's future health risks, such as the probability of developing influenza and calculating the necessary preventive measures.

[1278] Step 8:

[1279] The server generates specific advice based on the prediction results, such as "You are at high risk of influenza, so we recommend washing your hands, wearing a mask, and getting vaccinated."

[1280] Step 9:

[1281] The server then sends the generated predictions and advice to the parent or teacher's device via email, push notification, in-app notification, or other methods.

[1282] Step 10:

[1283] The user (parent or teacher) checks the notification received on the device and takes necessary action based on the advice, such as making an appointment for a vaccination or making sure that the child has a mask.

[1284] Step 11:

[1285] Users can provide feedback on the advice provided via the app or their device, which allows the system to continually improve.

[1286] Step 12:

[1287] The device periodically prompts the user to enter their latest health information and continuously transmits the acquired data to the server, allowing the server to provide predictions and advice based on the most up-to-date information.

[1288] These are the specific processing steps of the child health prediction AI system.

[1289] Example 1

[1290] 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."

[1291] Conventional child health management systems have limited methods for collecting and analyzing health information, and lack the ability to accurately predict children's health conditions and provide appropriate advice. Furthermore, because they do not collect epidemic information, they are unable to make predictions that take into account health risks by region or time of year. Furthermore, there are insufficient measures to format the collected health information or ensure its security, resulting in problems with the accuracy and safety of the data.

[1292] 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.

[1293] In this invention, the server includes means for acquiring the child's health information, means for formatting the information into an appropriate format, means for transmitting the formatted health information to the server using a secure protocol, means for error checking and correcting inconsistencies in the received health information and storing it in a database, means for collecting disease epidemic information by region and time from external data sources, means for analyzing the child's health information and epidemic information using a machine learning algorithm and predicting health risks, means for generating individual advice using natural language generation technology based on the analysis results, and means for notifying the generated predictions and advice. This enables efficient collection and analysis of a variety of health information, and enables highly accurate predictions of health risks that take epidemic information into account and the provision of specific advice.

[1294] "Children's health information" refers to data such as height, weight, daily body temperature, symptoms (cough, runny nose, fever, etc.), diet, and amount of exercise.

[1295] "Means of acquisition" refers to the means by which users input and collect health information using a smartphone app or computer interface.

[1296] The "formatting means" is a means for converting the input health information into a unified format and for performing error checks and corrections as appropriate.

[1297] "Secure protocol" refers to encrypted communication methods such as HTTPS, and is a technology that ensures security when sending and receiving data.

[1298] "Error checking and inconsistency correction" is the process of verifying the accuracy of received data and correcting missing data or formatting errors.

[1299] A "database" is a system for storing and managing collected health and epidemic information. Specifically, it includes relational databases such as MySQL and PostgreSQL.

[1300] "External data sources" refers to public health agencies and other reliable data sources that provide disease outbreak information by region and time.

[1301] "Machine learning algorithms" are statistical methods or artificial intelligence techniques used to analyze collected data and predict health risks. Examples include random forests and long short-term memory (LSTM).

[1302] "Natural language generation technology" refers to technology that generates advice in natural language that is easy for humans to understand based on the analysis results.

[1303] "Means of notification" refers to the method for communicating the generated predictions and advice to the user, and includes email, push notification, in-app notification, etc.

[1304] To implement the present invention, the following system configuration and operating procedures are required. First, a system is constructed for the purpose of collecting and analyzing children's health information. This system consists of three main elements: a user, a terminal, and a server. The detailed operation of each element and the necessary hardware and software are explained below.

[1305] User data entry

[1306] Users (parents or educators) enter their child's latest health information through a smartphone app or PC interface, including their height, weight, daily temperature, symptoms (cough, runny nose, fever, etc.), diet, and exercise.

[1307] Hardware: Consumer devices such as smartphones, tablets, and PCs.

[1308] Software: React Native apps, HTML5-based web apps, etc.

[1309] Sending data

[1310] The entered health information is temporarily stored on the device and formatted appropriately. For example, the device standardizes date formats and checks the format of numeric data. The device then converts the data into JSON format and sends it to the server using the HTTPS protocol.

[1311] Hardware: Same as above.

[1312] Software: JSON encoding, HTTPS communication, REST API, etc.

[1313] Receiving and storing data

[1314] The server captures the received data and performs error checking and inconsistency correction, specifically checking for missing data or formatting errors, before storing the data in a database.

[1315] Hardware: Servers, storage systems.

[1316] Software: Relational databases such as MySQL and PostgreSQL, error checking algorithms.

[1317] Collecting trend information

[1318] The server collects disease outbreak information by region and time period from public health agencies and other data sources (e.g., WHO, CDC). This information is stored in a database for later analysis.

[1319] Hardware: Server

[1320] Software: External API integration, data collection module.

[1321] Data Analysis and Prediction

[1322] The server uses the user's health information and trend information to apply machine learning algorithms to predict health risks, using time series analysis and pattern recognition.

[1323] Hardware: High performance computing server.

[1324] Software: Machine learning algorithms such as Random Forest, LSTM, etc.

[1325] Generating personalized advice

[1326] Based on the analysis results, specific advice is generated using natural language generation (NLG) technology, such as "You are at high risk of developing influenza, so please be sure to wash your hands and wear a mask."

[1327] Hardware: Server

[1328] Software: NLG toolkit, custom scripts.

[1329] Notification Delivery

[1330] The generated predictions and advice are sent from the server to home and school devices via email, push notifications, in-app notifications, and other methods.

[1331] Hardware: Servers, devices (smartphones, PCs, etc.).

[1332] Software: Push notification server, mail server.

[1333] User Action

[1334] Users receive notifications and take specific actions based on the advice, such as consulting a medical institution or making an appointment for a vaccination. Users can also enter feedback into the system to help improve its accuracy.

[1335] Hardware: Smartphone, PC

[1336] Software: Feedback forms, data collection systems

[1337] Continuous data collection

[1338] The device periodically prompts the user to enter their child's latest health information and continuously sends that data to the server, allowing the server to provide up-to-date predictions and advice.

[1339] Hardware: Smartphone, PC

[1340] Software: notification systems, data collection applications

[1341] Examples of concrete examples and prompts

[1342] For example, a user (parent) might enter into a smartphone app, "My 5-year-old child has had a slight fever of 37.5°C each morning for the past three days, and has symptoms of a cough and runny nose." This data is sent to a server, which analyzes it based on local and epidemic information, and predicts that "Influenza is currently prevalent in the area, so there is a high risk of the child developing the disease within the next week." The server then generates advice such as "Please wash your hands thoroughly, wear a mask, and consider getting vaccinated," and sends it to the parent's smartphone. The parent can receive the notification and immediately consult a medical institution to reduce their child's health risks.

[1343] Example prompt sentence:

[1344] "My 5-year-old child has had a temperature of 37.5°C every morning for the past three days, and is experiencing symptoms of a cough and runny nose. Please predict the future health risks associated with this condition and advise me on appropriate measures to take."

[1345] This system will enable the efficient collection and analysis of a wide range of health information, making it possible to accurately predict health risks taking into account current trends and provide specific advice.

[1346] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1347] Step 1: User Data Entry

[1348] Input: Using a smartphone app or PC interface, users input their child's health information, including their height, weight, daily temperature, symptoms (e.g., cough, runny nose, fever), diet, and exercise.

[1349] Specific behavior: The user enters information into a form on the app, for example, entering specific symptoms such as "I have had a slight fever of 37.5°C for three days."

[1350] Output: The terminal temporarily stores the input health information in its memory.

[1351] Step 2: Shaping the data

[1352] Input: Health information entered by the user.

[1353] Specific operations: The device formats the temporarily stored data, for example, standardizing date formats, checking the format of numeric data, and correcting inaccurate data.

[1354] Output: Formatted health information data.

[1355] Step 3: Encode and encrypt the data

[1356] Input: Formatted health information data.

[1357] What it does: The device converts the formatted data into JSON format and encrypts it using the HTTPS protocol, which allows for secure data transmission.

[1358] Output: Health information data encoded in JSON format and encrypted.

[1359] Step 4: Sending data

[1360] Input: Encrypted health information data.

[1361] How it works: The device sends encrypted data to the server using a communication protocol implemented through a REST API.

[1362] Output: The data is sent to the server.

[1363] Step 5: Receiving data and checking for errors

[1364] Input: Encrypted health information data.

[1365] Specific behavior: The server captures incoming data via a REST API endpoint, performs error checking to ensure the data is in the correct format, and corrects any inconsistencies.

[1366] Output: Health information data that has been error checked and corrected.

[1367] Step 6: Save your data

[1368] Input: Health information data that has been error checked and corrected.

[1369] Specific operation: The server stores the data in a database (e.g., MySQL or PostgreSQL).

[1370] Output: Health information data stored in a database.

[1371] Step 7: Gathering trend information

[1372] Input: External data sources providing epidemic information (e.g., public health agency APIs).

[1373] Specific operation: The server accesses external data sources and collects disease epidemic information by region and time period.

[1374] Output: A dataset containing epidemic information.

[1375] Step 8: Save Trend Information

[1376] Input: A dataset containing epidemic information.

[1377] Specific operation: The server stores the collected trend information in a database.

[1378] Output: Trend information data stored in a database.

[1379] Step 9: Data analysis and prediction

[1380] Input: Health information data, epidemic information data.

[1381] Specific operation: The server uses machine learning algorithms (e.g., random forest and LSTM) to analyze children's health and epidemic information, thereby predicting future health risks.

[1382] Output: Health risk prediction results.

[1383] Step 10: Generate personalized advice

[1384] Input: Health risk prediction results.

[1385] Specific operation: Based on the analysis results, the server uses natural language generation technology (NLG) to generate personalized advice, such as "wash your hands thoroughly, wear a mask, and consider getting vaccinated."

[1386] Output: The generated personalized advice.

[1387] Step 11: Creating and Delivering Notifications

[1388] Input: Generated personalized advice.

[1389] Specific operation: The server incorporates the generated advice into a notification message and delivers it to the home device via a push notification server or mail server.

[1390] Output: Notification message delivered to the user's device.

[1391] Step 12: User Action

[1392] Input: Notification message delivered to the user's device.

[1393] Specific actions: The user checks the notification and takes specific actions based on the advice, such as consulting a medical institution or making an appointment for a vaccination, and also enters feedback on the advice.

[1394] Output: Feedback is sent to the system.

[1395] Step 13: Facilitate ongoing data collection

[1396] Enter: your system's notification settings.

[1397] Specific operation: The device periodically sends notifications to the user prompting them to enter their child's latest health information. By entering new data, the device keeps the information up to date.

[1398] Output: The latest health information data is added to the system.

[1399] (Application example 1)

[1400] 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."

[1401] In modern society, managing children's health is extremely important. However, it is not easy for parents and teachers to manually collect daily health information and contact medical institutions at the appropriate time. It is particularly difficult to check children's health status in real time and take necessary measures when shopping in a physical store or going out. For this reason, there is a need for a system that can efficiently collect and analyze children's health information and provide appropriate advice.

[1402] 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.

[1403] In this invention, the server includes a means for acquiring child health information, a means for analyzing the acquired health information, and a means for generating predictions and advice based on the analysis results. This enables a means for entering a customer's health information using a smart device and sending it to the server, a means for receiving analysis results from the server and displaying them on the smart device, a means for periodically collecting data on the child's health information and sending it to the server, and a means for collecting information on current disease trends and using it for analysis. This allows parents shopping in a physical store to easily check their child's health status and quickly take appropriate measures.

[1404] "Children's health information" refers to data related to a child's health condition, such as height, weight, body temperature, symptoms, diet, and amount of exercise.

[1405] "Means of acquisition" refers to devices or software that allow users to input and record health information.

[1406] The "means of analysis" refers to algorithms or software that predict health risks and generate appropriate advice based on the acquired health information.

[1407] The "means for generating predictions and advice" is software that automatically suggests future health risks and appropriate measures based on the analysis results.

[1408] "Means of notification" refers to technologies such as email, push notifications, and in-app notifications used to notify users of generated predictions and advice.

[1409] "Smart devices" are mobile terminals with advanced computing power and communication functions, such as smartphones, smart glasses, and head-mounted displays.

[1410] A "server" refers to a computer system that stores and analyzes data over a network.

[1411] The "means for inputting health information of customers" refers to a user interface that allows customers to input health information via a smart device.

[1412] "Means for transmitting to the server" refers to the communication protocol and functions for securely transmitting the acquired health information to the server.

[1413] "Means for receiving and displaying analysis results from a server" refers to technology for receiving analysis results from a server and displaying them on the display of a smart device.

[1414] "Means for periodically collecting data and transmitting it to a server" refers to a system or program that periodically collects children's health data and transmits it to a server.

[1415] "Means for collecting disease epidemic information and using it for analysis" refers to software and databases for collecting disease epidemic information by region and time period and using it for analysis.

[1416] To practice the invention, we describe an implementation of a child health prediction system that can be used in a brick-and-mortar environment.

[1417] Hardware and software used

[1418] Hardware: Smart glasses (e.g., regular smart glasses)

[1419] Software: Python 3.x, HTTP / HTTPS API

[1420] Server: Data analysis server on the cloud

[1421] Communication protocol: HTTPS

[1422] Program processing flow

[1423] 1. Data entry by the user (parent or store staff)

[1424] The user uses the smart glasses' interface to input their child's latest health information, including height, weight, daily temperature, symptoms (e.g., cough, runny nose, fever), diet, and exercise. This information is temporarily stored in the smart glasses' memory and formatted accordingly.

[1425] 2. Data transmission

[1426] The smart glasses device transmits the processed health data to a server, using encrypted protocols such as HTTPS to ensure security.

[1427] 3. Receipt and storage of data

[1428] The server stores the received data in a database, where it is simultaneously checked for errors and corrected for inconsistencies.

[1429] 4. Collecting trend information

[1430] The server collects disease epidemic information by region and time period from public health agencies and other data sources, and this epidemic information is combined with children's health data for analysis.

[1431] 5. Data analysis and prediction

[1432] The server uses machine learning algorithms to analyze children's health data and epidemic information. For example, it predicts future health risks based on past body temperature data and current symptoms. This prediction is made using time series analysis and pattern recognition techniques.

[1433] 6. Generating personalized advice

[1434] The server generates specific, individualized advice based on the analysis results, such as "You are at high risk of developing influenza, so please wash your hands thoroughly, wear a mask, and consider getting vaccinated."

[1435] 7. Delivery of Notices

[1436] The generated predictions and advice are communicated from the server to the smart glasses display using suitable push notification technology.

[1437] Specific examples

[1438] For example, if a parent inputs into their smart glasses, "My temperature has been 37.5°C for the past three days, and I have symptoms of a cough and runny nose," that data is sent from the smart glasses to a server. The server analyzes local epidemic information and past data and predicts that "Influenza is currently spreading in the area, so there is a high risk of developing the disease within the next week." The server then generates advice such as "wash your hands thoroughly, wear a mask, and get vaccinated," and sends it to the parent's smart glasses. The parent can receive this notification and take immediate action.

[1439] Prompt Sentence Examples

[1440] Your temperature has been 37.5°C for the past three days, and you have a cough and runny nose. There is currently an influenza outbreak in your area, so you are at high risk of developing influenza. We recommend that you wash your hands thoroughly, wear a mask, and get vaccinated.

[1441] As described above, the present invention effectively manages children's health and provides an environment where parents and store staff can respond quickly through use in physical stores.

[1442] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1443] Step 1:

[1444] The user uses the smart glasses interface to input their child's latest health information.

[1445] Input: Height, weight, temperature, symptoms (cough, runny nose, fever, etc.), diet, amount of exercise

[1446] Data processing: The input data is converted into an appropriate format and temporarily stored in the smart glasses' memory.

[1447] Output: Formatted health data

[1448] Step 2:

[1449] The device transmits the formatted health data to the server.

[1450] Input: Formatted health data

[1451] Data Transmission: Transmit data using encrypted protocols such as HTTPS.

[1452] Output: Data sent to the server

[1453] Step 3:

[1454] The server stores the received data in a database, checks for errors, and corrects inconsistencies.

[1455] Input: Submitted health data

[1456] Data storage and processing: storing data in a database and correcting any missing or formatting errors.

[1457] Output: Accurate health data stored

[1458] Step 4:

[1459] The server collects disease outbreak information by region and time period from public health agencies and other data sources.

[1460] Input: Trend information by region and time

[1461] Data collection: Obtaining epidemic information from public health agencies and other data sources.

[1462] Output: Disease epidemic information by region and time period

[1463] Step 5:

[1464] The server uses machine learning algorithms to analyze children's health data and trend information to predict health risks.

[1465] Input: Health data, epidemic information

[1466] Data analysis: Predicting health risks using time series analysis and pattern recognition techniques.

[1467] Output: Predicted health risk

[1468] Step 6:

[1469] The server generates specific individual advice based on the analysis results.

[1470] Input: Health risk prediction results

[1471] Data generation: Generate specific advice to address health risks.

[1472] Output: The generated advice

[1473] Step 7:

[1474] The generated predictions and advice are communicated from the server to the display of the smart glasses.

[1475] Input: Prediction results and advice

[1476] Data transmission: Push notification technology is used to transmit data to the smart glasses.

[1477] Output: Predictions and advice displayed on smart glasses

[1478] Step 8:

[1479] The user receives a notification and takes specific measures based on the advice displayed on the smart glasses.

[1480] Input: Predictions and advice displayed on smart glasses

[1481] Take action: Based on the advice provided, take specific measures such as washing your hands, wearing a mask, and booking a vaccination appointment.

[1482] Output: The situation in which the countermeasure was implemented

[1483] 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.

[1484] In order to implement the present invention, a specific embodiment in which an emotion engine is combined with the processing flow of a child health prediction AI system will be described below.

[1485] User data entry

[1486] Users (parents or teachers) enter their child's health information through a smartphone app or PC interface. The information includes various data such as height, weight, body temperature, symptoms (cough, runny nose, fever, etc.), dietary habits, and exercise. The emotion engine also recognizes the user's emotional information and adds it to the data. The device temporarily stores this information and formats it appropriately.

[1487] Sending data

[1488] The device transmits the formatted health and emotion data to a server using a secure communication protocol (e.g., HTTPS).

[1489] Receiving and storing data

[1490] The server stores the received health information and emotion data in a database. The stored data is checked for errors and corrected for inconsistencies. If there are any missing or erroneous data, they are automatically supplemented and corrected.

[1491] Collecting trend information

[1492] The server collects disease prevalence information by region and time period from public health agencies and other data sources, which is used for further analysis.

[1493] Data Analysis and Prediction

[1494] The server uses the child's stored health, emotional, and trend information to run machine learning algorithms, including time series analysis and pattern recognition techniques. The server compares and analyzes the child's health data with past emotional data to more accurately predict health risks.

[1495] Generating personalized advice

[1496] The server generates specific advice based on the analysis results. By taking into account the emotional data, the advice content can be tailored to suit the user's psychological state. For example, if the user is feeling stressed, more detailed explanations and warnings will be provided.

[1497] Notification Delivery

[1498] The generated predictions and advice are sent from the server to home devices (parents' smartphones or PCs) and school devices via email, push notification, in-app notification, and other methods.

[1499] User Action

[1500] The user (parent or teacher) checks the notification received on their device and takes specific action based on the advice. For example, they may schedule a vaccination or provide a mask for their child. The user also enters feedback on the advice provided. The emotion engine also recognizes the user's emotions when giving feedback, and this information is used for future analysis.

[1501] Continuous data collection

[1502] The device periodically prompts the user to enter their latest health and emotional information and continuously transmits it to the server, allowing the server to provide predictions and advice based on the most up-to-date information.

[1503] Specific examples

[1504] For example, a user (parent) might enter into a smartphone app, "My 5-year-old child has had a slight fever of 37.5°C every morning for the past three days, and has symptoms of a cough and runny nose." At the same time, the emotion engine determines the parent's stress level as "high." This data is sent to a server, where it is analyzed based on local and epidemic information. The server then predicts that "Influenza is currently prevalent in the area, so there is a high risk of the child developing the disease within the next week." The server then generates advice such as "Please wash your hands thoroughly, wear a mask, and consider getting vaccinated." Since the parent is feeling stressed, the server also adds additional advice on how to reduce stress. The parent receives a notification and can immediately consult a medical institution to reduce their child's health risks.

[1505] In this way, the present invention not only effectively manages children's health and provides a reassuring environment for parents and teachers, but also provides comprehensive support that takes into account the user's own emotional state.

[1506] The processing flow will be explained below.

[1507] Step 1:

[1508] The user (parent or teacher) enters their child's health information through a smartphone app or PC interface. The information entered includes height, weight, temperature, symptoms (e.g., cough, runny nose, fever), dietary habits, and exercise. The device temporarily stores this information and formats it in an appropriate format.

[1509] Step 2:

[1510] The device uses an emotion engine to recognize the user's emotional state, including facial expressions, tone of voice, and the speed and content of input. Emotional data is temporarily stored along with health information.

[1511] Step 3:

[1512] The device transmits the formatted health and emotion data to a server using a secure communication protocol (e.g., HTTPS).

[1513] Step 4:

[1514] The server stores the received health information and emotion data in a database. The stored data is checked for errors and corrected for inconsistencies, and data is automatically supplemented or corrected as necessary.

[1515] Step 5:

[1516] The server collects disease prevalence information by region and time period from public health agencies and other data sources, which is then used in the analysis process.

[1517] Step 6:

[1518] The server uses stored child health information, emotional data, and trend information to run machine learning algorithms, using time series analysis and pattern recognition techniques to predict children's health risks.

[1519] Step 7:

[1520] The server then uses the analysis results to predict health risks, such as the probability of developing influenza and necessary preventive measures, and generates advice at the optimal time and in the most appropriate way, taking into account emotional data.

[1521] Step 8:

[1522] The server generates specific advice based on the prediction results. For example, "Due to the high risk of influenza, we recommend washing your hands, wearing a mask, and getting vaccinated." The content and tone of the advice are also adjusted appropriately based on the emotional data.

[1523] Step 9:

[1524] The server then sends the generated predictions and advice to home devices (parents' smartphones or PCs) and school devices via email, push notification, in-app notification, and other methods.

[1525] Step 10:

[1526] The user (parent or teacher) checks the notification received on the device and takes specific action based on the advice, such as making an appointment for a vaccination or making sure their child has a mask. The user provides feedback to the system about their actions based on the advice.

[1527] Step 11:

[1528] User feedback is sent to the server and stored in a database, allowing the system to continually improve and make future predictions and recommendations more accurate.

[1529] Step 12:

[1530] The device periodically prompts the user to enter their latest health and emotional information and continuously transmits this data to the server, allowing the device to provide predictions and advice based on the most up-to-date information.

[1531] As a specific example, suppose a user (parent) inputs into a smartphone app, "My 5-year-old child has had a slight fever of 37.5°C every morning for the past three days, and has symptoms of a cough and runny nose," and at the same time, the emotion engine detects a high stress level in the parent. This data is sent to the server and analyzed, and a prediction is made that "Influenza is currently spreading in the area, so there is a high risk of the child developing the disease within the next week." The server then generates advice such as "Please wash your hands thoroughly, wear a mask, and consider getting vaccinated," and, since the parent is feeling stressed, provides additional advice on how to reduce stress. The parent receives the notification and can reduce their child's health risks by consulting a medical institution as soon as possible.

[1532] The above are the specific processing steps of the child health prediction AI system that combines an emotion engine.

[1533] Example 2

[1534] 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."

[1535] Conventional child health information management systems simply input and manage health data, making it difficult to provide predictions and advice that take into account the user's emotional information. Furthermore, it is difficult to accurately predict health risks that incorporate current disease epidemic information, and there are issues with not being able to provide specific actions that users should actually take. For these reasons, there is a need for an effective system that allows users to manage their children's health with peace of mind.

[1536] 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.

[1537] In this invention, the server includes means for acquiring the child's health information, means for analyzing the acquired health information and the user's emotional information, means for collecting current disease epidemic information, means for generating a health risk prediction and individually tailored advice based on the analysis results, and means for notifying the generated prediction and advice. This enables highly accurate health risk prediction based on current disease epidemic information and provision of specific advice while taking the user's emotional information into consideration.

[1538] "Children's health information" refers to specific data about a child's health condition, such as height, weight, body temperature, symptoms (cough, runny nose, fever, etc.), diet, and amount of exercise.

[1539] "User emotion information" is data that indicates the user's (parent's or teacher's) emotional state (stress level, anxiety, etc.) and is recognized by the emotion engine.

[1540] "Disease outbreak information" means information collected from public health agencies and other data sources about the prevalence of a disease in a particular region or time.

[1541] "Health risk prediction" refers to predicting the likelihood that a child will contract a disease in the future based on the child's health information, disease prevalence information, and the user's emotional information.

[1542] "Personally tailored advice" refers to specific behavioral instructions or advice that are optimized based on the user's individual emotional state and health information.

[1543] "Notification" refers to the act of informing the user of the generated health risk predictions and advice, and is done by means of email, push notification, in-app notification, etc.

[1544] To implement the present invention, a specific embodiment of a child health prediction AI system combined with an emotion engine will be described below.

[1545] User data entry

[1546] The user (parent or teacher) enters their child's health information through a smartphone app or PC interface. Specific data entered includes height, weight, body temperature, symptoms (cough, runny nose, fever, etc.), diet, and amount of exercise. The emotion engine simultaneously recognizes the user's emotional information and adds it to the data. The device temporarily stores this information and formats it appropriately.

[1547] Sending data

[1548] The device transmits the formatted health and emotion data to a server using a secure communication protocol (e.g., HTTPS).

[1549] Receiving and storing data

[1550] The server stores the received health information and emotion data in a database. The stored data is checked for errors and corrected for inconsistencies. If there are any missing or erroneous data, they are automatically supplemented and corrected.

[1551] Collecting trend information

[1552] The server collects disease prevalence information by region and time period from public health agencies and other data sources, which is used for further analysis.

[1553] Data Analysis and Prediction

[1554] The server uses stored child health, emotional, and trend information to run machine learning algorithms, including time series analysis and pattern recognition techniques. This analysis compares the child's health data with past emotional data to more accurately predict health risks.

[1555] Generating personalized advice

[1556] The server generates specific advice based on the analysis results. By taking into account the emotional data, the advice content can be adjusted to suit the user's psychological state. For example, if the user is feeling stressed, more detailed explanations and warnings will be provided.

[1557] Notification Delivery

[1558] The generated predictions and advice are sent from the server to home devices (parents' smartphones or PCs) and school devices via email, push notification, in-app notification, and other methods.

[1559] User Action

[1560] The user (parent or teacher) checks the notification received on their device and takes specific action based on the advice. For example, they may schedule a vaccination or provide a mask for their child. The user also enters feedback on the advice provided. The emotion engine also recognizes the user's emotions when giving feedback, and this information is used for future analysis.

[1561] Continuous data collection

[1562] The device periodically prompts the user to enter their latest health and emotional information and continuously transmits it to the server, allowing the server to provide predictions and advice based on the most up-to-date information.

[1563] Specific examples

[1564] For example, suppose a user (parent) enters into a smartphone app, "My 5-year-old child has had a slight fever of 37.5°C every morning for the past three days, and has symptoms of a cough and runny nose." At the same time, the emotion engine determines the parent's stress level as "high." This data is sent to a server, where it is analyzed based on local and epidemic information. The server then predicts, "Influenza is currently prevalent in the area, so there is a high risk of the child developing the disease within the next week." The server then generates specific advice, such as "Please wash your hands thoroughly, wear a mask, and consider getting vaccinated." Furthermore, because the parent is feeling stressed, it also adds additional advice on how to reduce stress. The parent receives a notification on their device and can immediately consult a medical institution to reduce their child's health risks.

[1565] In this way, the present invention not only effectively manages children's health and provides a reassuring environment for parents and teachers, but also provides comprehensive support that takes into account the user's own emotional state.

[1566] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1567] Step 1: User Data Entry

[1568] The user (parent or teacher) uses a smartphone or PC interface to enter their child's health information. For example, the user enters information such as "height: 110 cm, weight: 20 kg, body temperature: 37.5°C, symptoms: cough, runny nose, fever, diet: normal, amount of exercise: low" into a form on the screen.

[1569] The emotion engine analyzes the emotion (e.g., stress level) entered by the user and recognizes "stress level: high" as emotional information.

[1570] These health and emotion information are stored in the device's temporary memory. Health and emotion information is acquired as input data and output in a formatted data format.

[1571] Step 2: Sending data

[1572] The device sends the formatted health and emotion data to the server using the HTTPS protocol. During this process, the formatted JSON formatted health and emotion information is used as input data. Specifically, the following data is sent to the server: { "Height": 110, "Weight": 20, "Temperature": 37.5, "Symptoms": ["Cough", "Runny nose", "Fever"], "Diet": "Normal", "Exercise": "Low", "Emotion": "Stress level: High"}.

[1573] Step 3: Receiving and storing data

[1574] The server receives health information and emotion data sent from the device. It receives formatted JSON data as input data and obtains data in a format that can be stored in a database as output data.

[1575] The received data is stored in a database within the server (e.g., a PostgreSQL database).

[1576] The server checks for errors and corrects inconsistencies in the stored data, and if weight data is missing, it fills in the missing data with an estimated value.

[1577] Step 4: Gathering trend information

[1578] The server collects disease epidemic information by region and time period from public health organizations and other data sources. It sends API requests to health organizations as input data and obtains disease epidemic information for the region as output data. In this process, it obtains influenza epidemic data from the "WHO API" and "CDC API," etc. Example of collection: "Influenza is currently spreading in the region."

[1579] Step 5: Data analysis and prediction

[1580] The server runs a machine learning algorithm based on the stored child's health information, emotional data, and epidemic information. The child's health information, emotional data, and epidemic information are used as input data, and health risk predictions are obtained as output data. Specifically, machine learning models such as ARIMA and LSTM are used, and the analysis results predict that there is a 70% risk of catching influenza within the next week.

[1581] Step 6: Generate personalized advice

[1582] The server generates specific advice to address health risks based on the analysis results. Taking emotional data into account, it generates specific behavioral instructions, such as "wash your hands thoroughly, wear a mask, and consider getting vaccinated." It also adds advice on how to reduce stress based on emotional information. The analysis results and emotional information are used as input data, and individually tailored advice is obtained as output data.

[1583] Step 7: Delivering notifications

[1584] The server then sends the generated predictions and advice to the home device. Individual advice and health risk predictions are used as input data, and notification messages are obtained as output data. Notification methods can be selected from email, push notification, in-app notification, and more, and specific messages such as "Please wash your hands thoroughly, wear a mask, and consider getting vaccinated. In addition, try relaxation techniques to reduce stress" are delivered.

[1585] Step 8: User Action

[1586] The user (parent or teacher) checks the notification they receive and takes specific measures based on the advice. Specific actions include "washing their hands thoroughly," "making sure their child wears a mask," and "making an appointment for a vaccination." Feedback, such as "I washed my hands and wore a mask," is also entered through the app. The feedback information is sent as input data to the server and used as output data to improve future advice.

[1587] Step 9: Continuous data collection

[1588] The device periodically reminds the user to enter their latest health and emotional data. The data is based on previously entered health information, and the device prompts the user to enter new temperature and symptom data every morning at 9:00 AM. The new data is then sent back to the server to keep the database up to date.

[1589] (Application example 2)

[1590] 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."

[1591] Conventional child health management systems are limited to collecting and analyzing health information and do not take into account the emotional information of parents and teachers. This can result in inappropriate advice to users. Furthermore, feedback collection and subsequent responses are insufficient, often resulting in a lack of continuous improvement. Furthermore, even in physical stores, effective support is not provided, making it difficult for parents and teachers to respond in a timely manner.

[1592] 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 acquiring health information, means for adding parent or teacher emotional information to the health information, means for analyzing the health information and emotional information, means for generating predictions and advice based on the analysis results, means for notifying the generated predictions and advice, and means for collecting feedback from the parent or teacher based on the content of the notification. This enables the provision of detailed advice that takes into account the emotional state of the parent or teacher, and enables fast and effective support in physical stores.

[1593] "Child health information" is data about a child's physical and health status, such as height, weight, temperature, symptoms, diet, and activity level.

[1594] "Emotional information" is data that represents the parent's or teacher's current emotional state (e.g., stress, relief, etc.).

[1595] "Acquisition" is the process by which a system captures data through sensors and input interfaces.

[1596] "Analysis" is the process of analyzing acquired data using algorithms and machine learning models to find specific patterns and trends.

[1597] "Prediction" is the process of estimating future health risks and conditions based on analytical results.

[1598] "Advice" refers to recommended actions or precautions provided to the user (parent or teacher) based on analysis results and predictions.

[1599] "Notification" is the process of sending prediction and advice information to the user's terminal and displaying it.

[1600] "Feedback" is data that users input into the system regarding their responses to and thoughts on advice.

[1601] "Server" means a central processing unit for storing data, analyzing, predicting and generating advice.

[1602] The system for implementing the present invention consists of a smartphone application that allows parents and teachers to input and manage their children's health and emotional information, and a cloud server for data analysis and prediction.

[1603] First, parents or teachers use a smartphone app to input their child's health data (height, weight, temperature, symptoms, etc.) and emotional information. This information is temporarily stored on the smartphone and then sent to a cloud server using a secure protocol such as HTTPS.

[1604] The cloud server stores the health and emotion information in a database, automatically checking and correcting for errors and inconsistencies. It then collects local epidemic information and provides this data to a machine learning model that performs data analysis. The machine learning model uses time series analysis and pattern recognition to predict future health risks.

[1605] Based on the prediction results, the cloud server generates specific advice that takes into account the emotional information of the parent or teacher. This advice is sent to the parent or teacher's smartphone via push notification or in-app notification. The parent or teacher who receives the advice takes appropriate action at a physical store or other location, and then enters the results and emotional feedback back into the smartphone app. The cloud server uses this feedback for further analysis to improve the accuracy of the system.

[1606] For example, if a parent types into their smartphone, "My 5-year-old child has had a slight fever of 37.5°C every morning for the past three days, and has symptoms of a cough and runny nose," and the emotion engine determines the parent's stress level as "high," that data is sent to a cloud server. The server references local epidemic information and generates a prediction that "Influenza is currently spreading in the area, so there is a high risk of the child developing the disease within the next week." The server then generates specific advice, such as "Please wash your hands thoroughly, wear a mask, and consider getting vaccinated," along with additional advice on how to reduce stress. This advice is then sent to the parent's smartphone, allowing them to immediately consult a medical institution and reduce their child's health risks.

[1607] An example of a prompt sentence to be input into the generative AI model is, "Please predict the risk of influenza and generate advice taking into consideration the child's health data (low-grade fever of 37.5°C for three days, with symptoms of cough and runny nose) and the parent's emotional information (high stress)."

[1608] In this way, the system of the present invention combines a child's health information with the emotional information of parents and teachers to provide more accurate health risk predictions and specific advice.

[1609] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1610] Step 1:

[1611] A user uses a smartphone app to input their child's health data (height, weight, temperature, symptoms, diet, amount of exercise, etc.) and emotional information (stress, peace of mind, etc.). This data is entered, formatted, and prepared for transmission to a cloud server. Entry is done manually by a parent or teacher, and the application checks for input errors and converts the data format.

[1612] Step 2:

[1613] The device sends the formatted health and emotional data to a cloud server using the HTTPS protocol. The data sent includes the child's health information (e.g., a slight fever of 37.5°C has lasted for three days) and emotional information (stress level is "high"). After sending, the server receives the data and stores it in a database.

[1614] Step 3:

[1615] The server stores the received health and emotion information in a database, and also performs error checks and corrects inconsistencies. If missing or errors are detected, automatic completion and correction processes are performed. The input here is the transmitted data, and the output is the error-corrected data.

[1616] Step 4:

[1617] The server collects current disease epidemic information from local medical institutions and public health centers. This epidemic information is stored in a database and used in later analysis processes. The input of this step is epidemic information from public institutions, and the output is the stored epidemic information.

[1618] Step 5:

[1619] The server runs a machine learning algorithm using the stored child's health information, emotional data, and trend information. The algorithm uses time series analysis and pattern recognition techniques to predict future health risks. The inputs are health information, emotional information, and trend information, and the output is a health risk prediction result.

[1620] Step 6:

[1621] Based on the analysis results, the server generates specific advice that takes into account the emotional state of the user (parent or teacher). For example, if a high risk of influenza is predicted, the server generates advice such as "wash your hands thoroughly, wear a mask, and consider getting vaccinated." The input to this step is the health risk prediction result, and the output is a written advice statement.

[1622] Step 7:

[1623] The server sends the generated predictions and advice to the parent or teacher's smartphone via push notification or in-app notification. The notification includes specific advice and precautions. The input here is the generated advice text, and the output is the notification to the user.

[1624] Step 8:

[1625] The user checks the notification and takes specific action based on the advice, such as consulting a medical institution or having their child wear a mask. The user then again inputs the results of their actions and emotional feedback into the smartphone app. This feedback is used to improve the accuracy of the system. The input here is the user's behavioral results and feedback, and the output is further data collection.

[1626] 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.

[1627] 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.

[1628] 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.

[1629] 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.

[1630] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1631] 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.

[1632] 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).

[1633] 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.

[1634] 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."

[1635] 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.

[1636] 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).

[1637] 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.

[1638] 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.

[1639] 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.

[1640] 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.

[1641] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1642] 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.

[1643] 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.

[1644] 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.

[1645] 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.

[1646] 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.

[1647] The following is further disclosed regarding the above embodiment.

[1648] (Claim 1)

[1649] a means of obtaining health information about the child;

[1650] A means for analyzing the acquired health information;

[1651] means for generating predictions and advice based on the analysis results;

[1652] a means for communicating the generated predictions and advice;

[1653] A system including:

[1654] (Claim 2)

[1655] 10. The system of claim 1, further comprising means for periodically collecting and transmitting data regarding the child's health information to the server.

[1656] (Claim 3)

[1657] 10. The system of claim 1, further comprising means for collecting and utilizing current disease epidemic information for analysis.

[1658] "Example 1"

[1659] (Claim 1)

[1660] a means of obtaining health information about the child;

[1661] a means for temporarily storing and appropriately formatting the acquired health information;

[1662] means for transmitting the formatted health information to a server using a secure protocol;

[1663] means for checking the received health information for errors and correcting inconsistencies and storing the information in a database;

[1664] A means of collecting regional and seasonal disease prevalence information from external data sources; and

[1665] A method for analyzing children's health and trend information using machine learning algorithms to predict health risks;

[1666] A means for generating personalized advice based on the analysis results using natural language generation technology;

[1667] a means for communicating the generated predictions and advice;

[1668] A system including:

[1669] (Claim 2)

[1670] 10. The system of claim 1, further comprising means for periodically collecting and transmitting data regarding the child's health information to the server.

[1671] (Claim 3)

[1672] 10. The system of claim 1, further comprising means for collecting and utilizing current disease epidemic information for analysis.

[1673] "Application Example 1"

[1674] (Claim 1)

[1675] a means of obtaining health information about the child;

[1676] A means for analyzing the acquired health information;

[1677] means for generating predictions and advice based on the analysis results;

[1678] a means for communicating the generated predictions and advice;

[1679] A means for inputting health information of customers using a smart device and transmitting the information to a server;

[1680] A means for receiving the analysis results from the server and displaying them on the smart device;

[1681] A system including:

[1682] (Claim 2)

[1683] 10. The system of claim 1, further comprising means for periodically collecting and transmitting data regarding the child's health information to the server.

[1684] (Claim 3)

[1685] 10. The system of claim 1, further comprising means for collecting and utilizing current disease epidemic information for analysis.

[1686] "Example 2: Combining Emotion Engines"

[1687] (Claim 1)

[1688] a means of obtaining health information about the child;

[1689] means for analyzing the acquired health information and emotional information of the user;

[1690] A means of collecting current disease outbreak information;

[1691] a means for generating health risk predictions and individually tailored advice based on the analysis results;

[1692] a means for communicating the generated predictions and advice;

[1693] A system including:

[1694] (Claim 2)

[1695] and means for periodically collecting and transmitting data relating to the child's health information and the user's emotional information to the server.

[1696] 10. The system of claim 1.

[1697] (Claim 3)

[1698] The server implements a machine learning algorithm using the child's health information, the user's emotional information, and current disease epidemic information.

[1699] Also includes

[1700] 10. The system of claim 1.

[1701] "Application example 2 when combining emotion engines"

[1702] (Claim 1)

[1703] a means of obtaining health information about the child;

[1704] a means of adding parent or teacher emotional information to the acquired health information;

[1705] means for analyzing health information and emotional information;

[1706] means for generating predictions and advice based on the analysis results;

[1707] a means for communicating the generated predictions and advice;

[1708] a means of collecting parent or teacher feedback based on the content of the notification;

[1709] A system including:

[1710] (Claim 2)

[1711] 10. The system of claim 1, further comprising means for periodically collecting and transmitting data regarding the child's health information to the server.

[1712] (Claim 3)

[1713] 10. The system of claim 1, further comprising means for collecting and utilizing current disease epidemic information for analysis. [Explanation of symbols]

[1714] 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 of obtaining health information about the child; A means for analyzing the acquired health information; means for generating predictions and advice based on the analysis results; a means for communicating the generated predictions and advice; A system including:

2. 10. The system of claim 1, further comprising means for periodically collecting and transmitting data regarding the child's health information to the server.

3. 10. The system of claim 1, further comprising means for collecting and utilizing current disease epidemic information for analysis.

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